Combining Anatomical Images with Estimates of Brain ...people.duke.edu/~abadea/ABadea_PhD.pdfΑντρέας Α. Ιωαννίδης ... 4.1.4 Background on active contours segmentation

Combining Anatomical Images with Estimates of Brain Activity

Extracted from Electrographic Data:

Methodology and Applications

Doctorate Thesis

-Patras 2003-

University of Patras School of Medicine National Technical University of Athens Department of Mechanical Engineering National Technical University of Athens Department of Electrical and Computer Engineering

Interdepartmental Program of Postgraduate Studies in Biomedical Engineering

University of Patras School of Medicine Department of Physiology and Department of Medical Physics 26500 Patras Professor George K. Kostopoulos

Alexandra Badea MSc in Medical Physics

Examination Committee:

Professor George K. Kostopoulos

Professor Nicolas Pallikarakis

Professor Ilias Kouvelas

Professor Ioannis Varakis

Associate Professor Panagiotis Dimopoulos

Associate Professor Anastasios Bezerianos

Lecturer Constantinos Papatheodoropoulos

Advisory Committee:

Professor George K. Kostopoulos

Doctor Andreas A. Ioannides

Professor Nicolas Pallikarakis

Εξεταστική Επιτροπή Καθηγητής Γεώργιος Κ. Κωστόπουλος

Καθηγητής Νικόλαος Παλλικαράκης

Καθηγητής Ηλίας Κούβελας

Καθηγητής Ιωάννης Βαράκης

Αναπληρωτής Καθηγητής Παναγιώτης ∆ηµόπουλος

Αναπληρωτλης Καθηγητής Αναστάσιος Μπεζεριάνος

Λέκτορας Κωνσταντίνος Παπαθεωδορόπουλος

Συµβουλευτική Επιτροπή

Καθηγητής Γεώργιος Κ. Κωστόπουλος

∆ρ. Αντρέας Α. Ιωαννίδης

Καθηγητής Νικόλαος Παλλικαράκης

Acknowledgment I am very grateful for the support of the Greek State Scholarships foundation

(IKY). This made possible my graduate studies in Greece and from here all the

experiences described below and many others.

I would like to thank all those who have taught me lessons during these years I have

been a graduate student.

To Professor George Kostopoulos for being an excellent teacher, for teaching among

others what an elegant character and honesty in research are, for being a lot more

to me than a PhD student supervisor.

To Doctor Andreas A. Ioannides for his close supervision, for giving himself example

of what hard work means, for being a never-ending source of ideas.

I am thankful for the chance to work in their laboratories and for their attempt to

create a common workplace, uniting via internet, phone calls and mainly common goals

people on opposite faces of the Earth.

To the members of the Physiology Department for the wonderful atmosphere and

openness to collaborating with people coming with different backgrounds and from

different countries. It has been great to be part of this large family.

To Professor Charles F. Starmer for teaching me about perseverance and marathon

running, about passing through life in a highly interactive, involved way.

To Professors Nicholas Pallikarakis and Basil Proimos for their involvement in the

graduate course in Medical Physics and Biomedical Engineering, an international

experience they host and nurture in Greece for quite a few years.

To my colleague, collaborator and friend Ovidiu Zainea for many discussions, for his

part in our collaborative work, for his care in planning the EEG experiments, and for

his contribution in keeping the programs, the PCs and the local network working.

Thanks to all the people who have used the software described in this thesis and

contributed comments, ideas or criticism related to it (Andreas Ioannides, George

Kostopoulos, Vahe Poghosyan, Marc Schellens, Cristian Badea, Maria Stavrinou,

Milton Ioannides and others).

Thanks to the people who wrote code, made it publicly available, and/or simply

discussed it and taught many other people, including myself, how to write code in

IDL.

Thanks to all the patient subjects who provided data for the studies.

Thanks to Cristian and Andi for teaching me what happiness is!

This thesis is dedicated to my family, without which nothing would have been

possible.

i

Table of Contents

Chapter 1. General Introduction 1

1.1 Motivation and goals 1

1.2 Plan of the thesis 3

Chapter 2. Imaging brain structure and function 5

2.1 Structural brain imaging and the use of MRI 7

2.1.1 History of MRI 7

2.1.2 Physical bases and principles of MRI 7

2.1.3 Clinical and research applications 12

2.2 Functional imaging based on MEG 13

2.2.1 History of MEG 14

2.2.2 Biophysical bases of MEG and EEG generation 14

2.2.3 EEG versus MEG 17

2.2.4 The experimental system 19

2.2.5 Inverse Problem 22

2.2.6 MEG:Clinical and research application 25

2.2.7 Coregistration of MRI and MEG 26

2.3 Conclusion 27

Chapter 3. Brain Segmentation 28

3.1 Introduction 28

3.1.1 Definitions related to image segmentation 28

3.1.2 Applications of Segmentation 29

3.1.3 The problem of brain segmentation 32

3.1.4 Mathematical morphology for image analysis 35

3.1.5 White matter – gray matter separation 38

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3.2 A mathematical morphology based method for brain

segmentation 40

3.3 A modified fuzzy c means method for white matter-gray

matter separation 44

3.4 Geometrical, differential properties of the cortex 47

3.5 Results 51

3.1.6 Whole Brain Segmentation 51

3.6.1 Gray matter-white matter separation with bias field compensation 52

3.6 Discussion and Conclusion 53

Chapter 4. Brain Structure Segmentation 56

4.1 Introduction 56

4.1.1 Motivation for segmentation 56

4.1.2 Selected structures of interest 58

4.1.3 Methods for subcortical structure segmentation 63

4.1.4 Background on active contours segmentation 64

4.2 Methods 66

4.2.1 Manual and snake based segmentation 67

4.2.2 Hippocampus Segmentation 69

4.2.3 Amygdala Segmentation 71

4.2.4 Central sulcus segmentation 73

4.2.5 Thalamus segmentation 75

4.2.6 Brain stem segmentation 77

4.3 Results 78

4.4 Discussion and conclusion 80

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Chapter 5. Visualization of surface activation 83

5.1 Introduction 83

5.2 Methods 85

5.2.1 Extracting the structural information 86

5.2.2 Computing the activation maps 87

5.2.3 3 D Visualization 88

5.2.4 Slice views 90

5.2.5 The VISIO software features 90

5.3 Results 92

5.3.1 Qualitative Evaluation of Segmentation 93

5.3.2 Surface activation visualization 94

5.4 Discussion 99

Chapter 6. Applications in Neurophysiology 101

6.1. Introduction 101

6.1.1. The somatosensory system 102

6.1.2. Background on the early somatosensory evoked potentials/fields 105

6.2. Methods 107

6.3. Results 108

6.3.1. Electrical stimulation of nerves in the limbs of normal subjects 108

6.3.2. Electrical stimulation of the limbs for a paraplegic subject 115

6.3.3. The primary visual cortex - a combined fMRI and MEG analysis 117

6.3.4. Use of anatomical constraints for EEG dipole localization. Application to

central sulcus 118

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6.4. Discussion 121

Chapter 7. A Software Tool for Interactive Determination of

the Plane of Cut through the Rat Brain 123

7.1. Introduction 124

7.2. Methods and materials 124

7.2.1. Reconstructing the rat brain (structures) based on atlas images 124

7.2.2. Search protocol 126

7.3. Results 127

7.4. Practical solution 128

7.5. Discussion 129

7.6. Conclusions 130

Chapter 8. General Discussion 131

8.1 Programs design 132

8.2 Contributions of SAV to understanding brain function,

complementing and integrating the relevant techniques 135

8.2.1 Source localization and extent 135

8.2.2 Source separation 135

8.2.3 Spatial resolution of electrophysiological techniques 136

8.2.4 Comparison of multimodal data 136

8.3 Applications 136

8.4 Outlook 137

Chapter 9. Conclusion 140

Publications i

Abbreviation List iii

References vi

Abstract Advances in hardware and software have made possible the reconstruction of brain

activity from non-invasive MEG and EEG data over a large part of the human brain. The

appreciation of the information content in the data is enhanced when relevant anatomical

detail is available for visualization. Different neuroscientific questions give rise to

different requirements for optimal combination of the information from functional and

anatomical data. Much of the software available today deals with scalar measures of

activity, e.g. changes in hemodynamic demand. The brain activity reconstructed from

MEG and EEG incorporates scalar but also vectorial information, which can be presented

in juxtaposition with relevant anatomical detail from MRI. Furthermore the direction of

the current density vector is expected to be related to the local cortical surface.

To address these problems we introduce an object-oriented software tool dedicated to the

visualization of spatio-temporal brain activity which allows the interplay of geometry and

vector properties of the current density directly in the representations.

The software (SAV) provides modules dedicated to: a) segmentation of the cerebrum

and/or b) subcortical or extra cortical structures and ultimately c) visualization of scalar

and vector fields in the background of the anatomy of the segmented surfaces. The

software succeeds to: a) bring forth the timing of activations and their relationships to the

cortical surface topography; b) allow the user to study the functional data in easy-to-

control view settings and hence; c) navigate through large data sets by focusing on

predefined anatomical structures.

We examine the use of detailed anatomical knowledge in functional studies and derive

quantitative properties of the segmented structures.

Additionally we investigate the applicability of quantitative imaging techniques to

planning an electrophysiological experiment on rat brain slices. We developed software to

visualize selected structures within the rat brain and a procedure to derive an optimal

sectioning plane, which would preserve as much as possible the afferent connections to the

selected structure.

Chapter1. Introduction

1

Chapter1. General Introduction

Chapter1. General Introduction.................................................................................................... 1

1.1 Motivation and goals.................................................................................................... 1

1.2 Plan of the thesis .......................................................................................................... 3

1.1 Motivation and goals

One of the most interesting objects of study for medical researchers and biomedical engineers

alike is the living brain. It is being studied from the cellular level to the system level, from the

experimental to modeling aspects and till the possibility of interfacing with electronic devices.

The goals of these studies are three fold: understanding it, healing or replicating it. Yet much

remains to be done to understand its structure and function.

The general goal of this thesis has been to contribute to solving the problem of linking brain

structure and function. A major landmark was set by Brodmann [1909] who classified brain

regions based on their cytoarchitecture. He looked in fact at the appearance of the cortex under

the light microscope. Since then there has been increasing interest in linking the appearance of

a region and its function.

Sophisticated techniques and devices have been developed for imaging the brain and mapping

its function.

Techniques for imaging the anatomy may use X-rays to produce radiographs on films and also

CT volumetric reconstructions. A newer technique is MRI which adds new information

because of the increased soft tissue contrast.

A number of different techniques have been described for non-invasively measuring human

brain activity. These can be broadly classified into hemodynamic, metabolic or electromagnetic

measurements. Current hemodynamic measurements, particularly functional MRI (fMRI),

provide excellent spatial resolution (millimeters) but are temporally limited by the latency of

the hemodynamic response (seconds) as the physiological time limit and the signal to noise

ratio from the technical point of view. SPECT, PET, EEG, MEG and fMRI are techniques with

different physical bases dedicated to imaging function. The main challenge, besides perfecting


2

each of these techniques (increasing the image quality), is to combine in an intelligent manner

the complementary information extracted from each of them.

Currently the most widely used method of analyzing functional brain imaging data consist in

projecting the functional data on anatomical slices [Fischl et al., 1999]. For this purpose the

anatomical and functional data sets must be brought into the same coordinate system.

Comparative studies over a large number of subjects require these data sets to be into a

standardized 3D space. The most common procedure is to report to the Talairach atlas

[Talairach et al., 1967; Talairach and Tournoux, 1988]. While this approach has certain

advantages (ease of use, widespread acceptance, applicability to subcortical structures) it also

has significant drawbacks. The atlas is based on a single subject and contains one hemisphere.

Even recently developed statistical atlases, based on large number of brains only give

probabilistic locations since there is high variability between normal brains. These drawbacks

are partly due to the presence of the extensive convolutions of the cerebral cortex, almost as

particular to an individual as the fingerprints. A central problem is the analysis of regions

buried within the deep and irregular sulci of the cortex. Estimates of the amount of buried

cortex range from 60 to 70% [Van Essen and Drury, 1997; Zilles et al., 1988] implying that

distances measured in 3-D space between two points on the cortical surface will easily

underestimate the geodesic distance (measured on the cortical surface), particularly in cases

where the points lie on different banks of a sulcus.

The location and extend of particular functional entity may differ from one normal individual

to another and even more in pathological cases. The difficulty is not simply in visualization,

because buried regions can be exposed either by cutting slices through the cortex using

noninvasive imaging techniques (MRI, CT, etc) or by using computational techniques to flatten

the cortex with minimal distortions. Rather, the main challenge is to decipher the complex

spatial relationships between regions contained on different slices or lying in different sulci.

Our goal has been to develop methods and implement them into software dedicated to

combining anatomical images with estimates of the human brain activity from electrographic

data, in such ways as to reveal novel information.

The largest part of the thesis is dedicated to extracting detailed anatomical information on the

brain from high resolution MR scans of the head and using this information to enhance the

appreciation of information on magnetic sources extracted from MEG.

Similar techniques have been used for reconstructing the 3D anatomy of the rat brain, this time

based on atlas data [Paxinos, 1997] and studying the geometry of connections between separate


3

structures. Specifically we examined the possibility of inferring the position and orientation of

a cut plane which would preserve intact as many as possible connections of the fornix to the

mamillary bodies. This would benefit electrophysiological experiments in rat brain slices for

the study of mammilarry bodies and the same methodology could eventually be applied to

other structures.

1.2 Plan of the thesis

The thesis is organized in nine chapters, the first two being introductory. The next five are

intended to be self contained and in general include their own introductory sections, methods,

results and conclusion. The last two chapters are a general discussion and conclusion to the

thesis.

The first chapter is the present one and aims to give a quick introduction to the problem of

combining anatomical images with estimates of brain activity from electrographic data, in the

general context of brain imaging. The second chapter gives a broader introduction to the

physical bases and techniques used for anatomical imaging with MRI and functional imaging

with MEG and EEG, with an accent on MEG. Also some details on the instrumentation used

for recording the MEG data used in this thesis is given in Chapter 2.

Chapters 3 and 4 introduce the problem of segmenting the cerebrum and its main

compartments: the gray and white matter in chapter three, and selected gray matter structures

in chapter four. The methods used for the two chapters are different; however they can be used

in combination for best results. Some of the segmented brain structures may be relevant for

activation studies, and we present examples in the next chapters, and/or may have significance

since their changed morphology may be an indicator in certain disorders (like Alzeimer’s

disease, schizophrenia, etc). Additionally the computation of local, differential properties of

these structures is described.

Chapter 5 introduces the various visualization options we have developed and used for

representing the segmented anatomy and overlaying it with functional data in the form of

equivalent current dipoles and current density or statistical parametric maps. We introduce into

the representation of activation data not only the scalar but also the vector aspect where

applicable.

Chapter 6 describes applications of the above methods in neurophysiology, specifically the

study of somatosensory evoked fields (the cases of median and tibial nerve stimulation), and

visually evoked fields. Another application makes use of the information on the local geometry

of the anatomy. This information is incorporated into an exhaustive search for the EEG dipoles


4

which best models the activity evoked by median nerve stimulation at the wrist. The activity

sources are dipoles located in the contralateral hand area of the primary somatosensory cortex.

Chapter 7 describes one of the possible uses of image processing, visualization and

quantification for planning an electrophysiological experiment on rat brain slices, in particular

when one wants to preserve the maximum possible number of connections between two

structures.

Chapter 8 and 9 consist in the discussion and general conclusion of the thesis.

At the end are the references and the lists of publications and abbreviations.

Chapter2.Imaging brain structure and function

5

Chapter 2. Imaging brain structure and function

Chapter 2. Imaging brain structure and function.......................................................................... 5

2.1 Structural brain imaging and the use of MRI............................................................... 7

2.1.1 History of MRI......................................................................................................... 7

2.1.2 Physical bases and principles of MRI ...................................................................... 7

2.1.3 Clinical and research applications.......................................................................... 12

2.2 Functional imaging based on MEG............................................................................ 13

2.2.1 History of MEG...................................................................................................... 14

2.2.2 Biophysical bases of MEG and EEG generation ................................................... 14

2.2.3 EEG versus MEG................................................................................................... 17

2.2.4 The experimental system........................................................................................ 19

2.2.5 Inverse Problem...................................................................................................... 22

2.2.6 MEG: Clinical and research application ................................................................ 25

2.2.7 Coregistration of MRI and MEG ........................................................................... 25

2.3 Conclusion.................................................................................................................. 26

The work of Broca [1865], who demonstrated that speech deficits result from damage to the

left frontal lobe, can be considered as the start of the modern research on correlating brain

structure and function. Intraoperative surgical mapping (through electrical stimulation)

pioneered by Penfield and others [Penfield and Roberts, 1959] allows the correlation of

structure and function in more detailed manner compared to pathological observation. This

technique is however invasive and therefore limited to pathological cases, which can present

altered relationships between structure and function [Woods, 1996]. Only with noninvasive

brain imaging can the normal population be studied. The same applies to studying function in

patients before and after localizing the area of pathology [Damasio and Frank, 1992]. This

approach allows for prediction of structure based on function and prediction of function based

on structure, a situation suited for framing and testing new hypotheses [Woods, 1996].

Functional brain imaging is a multidisciplinary research field that encompasses techniques

devoted to a better understanding of the human brain through noninvasive imaging of the

electrophysiological, hemodynamic, metabolic, and neurochemical processes that underlie

normal and pathological brain function.


6

The brain is a complex dynamical system, with many degrees of freedom, and multichannel

measurements are necessary to gain a detailed understanding of its behavior [Mira and Pesaran,

1999]. Such measurements include multielectrode recordings, optical brain imaging, functional

magnetic resonance imaging (fMRI) [Ogawa et al., 1992; Kwong et al., 1992], and

magnetoencephalography (MEG) [Hämäläinen et al., 1993].

The anatomy can be visualized with techniques like MRI and CT, while the functional aspects

can be studied noninvasively using PET, SPECT, EEG, MEG or fMRI.

The coupling between neuronal firing and blood flow is exploited by imaging techniques like

PET, SPECT and fMRI. fMRI exploits the fact that an increase in neuronal activity results in a

local increase in blood flow, exceeding metabolic oxygen demands. There is more

oxyhemoglobin than deoxyhemoglobin locally, oxyhemoglobin is paramagnetic, hence the

greater activity on the image.

PET and SPECT involve detecting gamma ray photons and reconstructing 3D maps of

radioactive tracer concentration. More active brain regions have a higher rate of blood flow and

receive the tracer earlier than other areas or emit stronger radiation. In contrast with these

methods, which give indirect measure of neuronal activity, EEG and MEG give a direct

measure of the electrical activity of neural cells.

MEG and EEG are techniques that measure, respectively, the magnetic induction outside the

head and the scalp electric potentials produced by electrical activity in neural cell assemblies.

They are the only noninvasive techniques that can analyze the whole brain with a sub

millisecond time resolution. Their space resolution (about 5mm for cortical sources) is too

coarse to be sensitive to the activity of one or even few neurons, but it reflects a cooperative

effect of a large number of neurons spread over at least a few millimeters of cortex.

The spatial resolving power of MEG and EEG is limited by the number of spatial

measurements (a few hundreds recording places) and the inherent ambiguity of the underlying

quasistatic electromagnetic inverse problem. Introducing constraints in the models on the

source generation of MEG and EEG signals can increase resolution substantially, but it can

also bias the reconstructions wrongly if these constraints reflect wrong assumptions.

The experiments designed to study brain function usually involve repeated measures of the

method’s specific parameter, a paradigm possible for noninvasive methods only. The simplest

such functional experiment is the activation study, aiming to identify, locate and eventually

quantify the brain region “responsible” for a certain function. Usually multiple scans are

acquired for two conditions, typically one “at rest” and a second “at activation”. These studies

generate huge amounts of data which are usually studied after statistical processing to


7

emphasize significance but also as single trials, in an attempt to preserve much of the original

information. It is the analysis and mainly the visualization of these large multichannel,

multidimensional data which is of interest to the present study.

The next subsections provide an introduction to the imaging methods which provide the basis

for the activation studies. Section 2.1 gives a background on MRI, the technique providing

anatomical data. Section 2.2 gives a background on MEG (and EEG). These are the techniques

providing the functional data used in our study. At the end of this section is a discussion on

combining information from anatomical (MRI) and functional (in particular case MEG)

techniques.

2.1 Structural brain imaging and the use of MRI

2.1.1 History of MRI

Wolfgang Pauli suggested in 1924 that the interior of the atom's nucleus could be manipulated

and caused to move with angular momentum (spin) and become magnetic. Isidor Isaac Rabi

won the Nobel Prize in physics in 1944 for his resonance method for recording the magnetic

properties of atomic nuclei. Bloch and Purcell discovered independently the nuclear magnetic

resonance or NMR and shared the Nobel prize in 1952. In 1971 Raymond Damadian

demonstrated that NMR could be used for a medical purpose showing that the nuclear

magnetic relaxation times of tissues and tumors differ. Damadian drove the development of a

full body NMR machine, later called MRI, which would become available only in 1977. This

NMR device was designed to detect cancer but did not produce high resolution images. In

1973 Paul Lauterbur [Lauterbur, 1973] described rotating the magnetic gradient around an

object to create an image based on backprojection algorithms, similar to those developed for

CT.

In 1975 Richard Ernst proposed using phase and frequency encoding, and the Fourier

transform. This technique is the basis of current MRI techniques, approved for clinical use by

FDA in 1985 [Hornak, 2002].

2.1.2 Physical bases and principles of MRI

Magnetic resonance imaging gives information on the proton density and the media in which

they are found, based on the absorption and emission of energy in the radio frequency range of

the electromagnetic spectrum.

At the basis of nuclear magnetism lie the properties of nuclei that contain an odd number of

protons, neutrons or both in combination, such as 1H, 13C, and 23Na. These properties are spin

angular momentum and the associated magnetic moment along the spin axis.


8

The classical approach

The proton can be considered to posses electrical charge e and an angular momentum I. The

electrical charge moving around the spin axis gives rise to a magnetic moment (µp).

In the presence of an external static magnetic field, the magnetic dipole moment is subject to a

torque, µp x B0. This results in precession of the magnetic moment around the magnetic flux

density, B0.

The frequency with which the moment precesses is given by the Larmor equation:

ω0=-γ B0

Where ω0 corresponds to the frequency of precession called the Larmor frequency and γ

(MHz/T) is the gyromagnetic ratio

The net magnetic moment (M) of a sample made of many nuclei is the vector sum of all

nuclear magnetic moments and at equilibrium it is aligned to B0 (which defines the z axis), see

Figure 2.1-1.

A second, radiofrequency field (B1), perpendicular to B0, i.e. in the xy plane is used to tilt M

away from B0 to produce a measurable component in xy plane. In a rotating frame reference

system, moving together with B1 the effective field is:

Beff=(ω0- ω1)/γ=B0- ω1/γ

For a B1 characterized by the Larmor frequency the effective field is zero and the

magnetization will precess around B1. Once displaced from the z axis by B1, the motion of the

magnetization vector will induce a current in the radio frequency (RF) coils. A typical such

signal, called free induction decay (FID) is shown in Figure 2.1-1 c.

Figure 2.1-1 (a) A rotating magnetic flux density B1 is applied perpendicularly to the static field of flux

density B0, M will experience an additional torque which moves it at an angle α from the z axis (x’ and y’

are rotating with the Larmor frequency). (b)The motion of M in the laboratory frame following the

application of a 90o pulse. This motion can be considered as composed of the motion of the rotating frame

relative to the laboratory frame plus the motion within the rotating frame. (c) The resulting FID that can be

measured from the xy component of the magnetization decays because of spin-lattice and spin-spin

interactions (from [Webb, 1998]).


9

The quantum approach

A thermal equilibrium the distribution of spins in the two energy states (parallel or antiparallel

to the applied field B0) follows Boltzmann’s law.

∆=

−

+

skTE

downspinn

upspinnexp

)21,_(

)21,_(

k is Boltzmann constant, Ts is the absolute temperature of the spin system, and n is the number

of spins in a given state and E∆ is the energy difference between the two states.

The energy of these states is: E=-µB0=- γm1ћB0.

Where µ is the magnetic moment, ћ is the reduced Planck’s constant (1.0546x10-34 Js) and m1 is

the magnetic cuantum number. For the protons the magnetic quantum number is m1=+/-21 and

therefore: E∆ = γћB0

The lower energy state (spin up or parallel to the external field) has a larger population of spins

compared to the high energy state (spin down or antiparallel to the external field). As a result,

the tissue will exhibit a net magnetization, dependent on the strength of the magnetic field and

the thermal agitation.

Transitions can take place from the lower state to the higher state if energy at the Larmor

frequency is absorbed:

Ћω0= γћB0

Figure 2.1-2. The stationary states of a proton spin in a constant magnetic field H0. The application of a

radio frequency (RF) magnetic field at the Larmor frequency results in energy absorption. After cessation

of the RF field the excited protons go back to the lower energy state and a FID is emitted [from Cho at al.,


10

1993].

In the external static magnetic field, B0, nuclei can be shifted from the parallel to antiparallel

alignment by the application of radio frequency energy, E = ћγB. If we consider an RF

magnetic field, B1, applied perpendicular to B0, the system will absorb energy. Upon

termination of the RF pulse, the nuclei return to their original alignment parallel to the applied

static field and energy is emitted in the form of a weak RF signal of specific frequency. This

frequency of the emitted signal depends on the strength of the applied static magnetic field as

well as the type of nuclei producing the signal.

T1 and T2 parameters

The emitted RF signal is called the free induction decay (FID) signal and is picked up by a

receiver coil. It constitutes a central part of MR imaging. The waveform of this signal is an

exponentially damped sine wave. The decay is due to relaxation phenomena spin-lattice (or the

longitudinal magnetization realaxation, Mz) and spin–spin (for the transverse magnetization

realaxation, Mxy). These processes are characterized by the T1 and T2 constants. T1-weighted

images reflect the recovery of the original signal as the flipped atoms return to baseline from

the excited state. They reflect the signal intensity along the z-axis. In addition to the rotation,

the net magnetization starts to dephase because each of the spin packets making it up is

experiencing a slightly different magnetic field and rotates at its own Larmor frequency. The

time constant which describes the return to equilibrium of the transverse magnetization, MXY,

is called the spin-spin relaxation time, T2.

The decay of transverse magnetization is due to: 1) molecular interactions; 2) variations in the

main magnetic field (never completely uniform; 3) added gradients. The combination of these

factors is what actually results in the decay of transverse magnetization. The combined time

constant measures the net remaining signal intensity in the transverse plane.

The Bloch’s equations describe the behavior of the nuclear magnetic moment or a sample of

minimally interacting spins.

BMdt

dM×= 0γ

NMR experiments can be performed in “continuous wave” mode or in “pulsed” mode. In the

continuous mode the radiofrequency is slowly scanned and any induced current is detected. As

the frequency approaches values close to the Larmor frequency of the protons, the effective

field in the rotating reference frame becomes smaller till B1 is larger than Beff and the

magnetization will precess around B1. This will induce a current that is detected. In pulsed

mode a short RF pulse is applied, during this time the magnetization will precess around B1.


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When the pulse is turned off the magnetization will precess around B0 inducing a measurable

current, which decays in time. The pulse duration determines how far the magnetization

rotates, or the flip angle.

The equation of Bloch for the net magnetization M ( kMjMiMM zyx ++= ), taking into

account the simultaneously occurring relaxation mechanisms is given by:

1

0

2

)())()(()()()(

TMtMk

TjtMitM

tBxtMdt

tdM zyx −−

+−= γ

and the solutions follow the curves shown in Figure 2.1-3, namely:

2/0

1/0 )1(

Ttxy

Ttz

eMM

eMM−

−

=

−=

Figure 2.1-3: a) T1-weighted images reflect the recovery of the original signal as the flipped atoms return to

baseline from the excited state. B) Decreases in T2* signal are due to dephasing of the flipped atoms [from

Hornak, 2002].

In order to produce an image, each MR signal must be referenced to a specific region of tissue.

This is accomplished by applying a gradient magnetic field and a narrow band RF field in

which the field strength varies linearly with position. Besides this slice selection gradient, other

two gradients are used for phase and frequency encoding. Using a computer-aided

reconstruction program, similar to that used in computed tomography, the signals attributed to

individual volume elements of tissue can be resolved and reconstructed into an image.

Traditional magnetic resonance imaging (MRI) systems measure spatial distribution of several

tissue-related parameters, as longitudinal and transversal relaxation times and proton density.

From a single anatomical slice can be collected a multitude of numerical data. The data

consists of T1, T2 relaxation constants values and/or proton density pixel intensities at each

spatial location in the MRI. These parameters can be aggregated into multidimensional data

which can used to cluster the tissue in the feature space.

Various pulse sequences are available: spin echo of T1 and T2, inversion recovery, cardiac or

respiratory gated imaging, and from the high speed imaging techniques: single shot imaging


12

(echo planar or spiral planar), multiple shot imaging.

The choice of the sequence parameters will have an influence on the amount of signal coming

from the object and hence will affect the SNR. These parameters include the time between the

90 degrees pulse and the echo, called the echo time TE, the inversion time TI, the time between

two successive 90 degrees pulses, called repetition time TR, the flip angle , the shape of the

radio frequent pulses, etc.

An example of the different appearance of the same tissue type in T1, T2 and PD images is

shown in Figure 2.1-4.

Figure 2.1-4: From left to right: T1 weighted, T2 weighted, PD images of the brain [from “The Whole Brain

Atlas” available online at http://www.med.harvard.edu/AANLIB/, authors Johnson KA and Becker JA].

The strength of the MRI signal, i.e., the signal emitted when nuclei return to their equilibrium

state, depends primarily on three parameters: the proton density (the higher the density of

protons, the larger the signal), T1, and T2. The contrast between brain tissues depends upon

how these parameters differ in the tissues.

In T1 weighted images the signal from fat is bright, whereas image intensities from areas of

muscle and fluid are lower. Cartilage, ligaments and tendons appear very dark. Bone marrow is

also bright due to its fat content.

In T2 weighted image fluid and edema appear bright. The fat displays moderate intensity

whereas the muscles are dark. Tendons, ligaments and cartilage still appear dark.

For most "soft" tissues in the body, the proton density is very homogeneous and therefore does

not contribute much to signal differences in the image.

2.1.3 Clinical and research applications

Two methods of imaging the 3D anatomy are CT and MRI and their relative advantages can

make useful a complementary approach using both techniques. In comparison with CT,

dedicated more to examinations of the chest, abdomen, pelvis, and the skull base, MRI is the

procedure of choice for neurologic and orthopedic diagnoses, but can be used also for liver,


13

kidney, pancreas, etc. and eventually in association with Gadolinium for venography or

artherography. MRI has a higher capacity for displaying soft tissue contrast.

A notable example is the discrimination between the gray and white matter of the brain. In

addition, MRI is unobstructed by bone, and thus it is especially beneficial in high resolution

imaging of the brain and spinal cord. Image contrast can be tailored to the specific clinical

application so that specific types of pathology are emphasized. Three-dimensional

reconstructions of anatomic structure can be obtained. These characteristics render MRI a very

effective and important tool for soft tissue imaging.

Obtaining a structural brain scan in a psychiatric patient can be useful to exclude trauma, or

other brain disorders (stroke, multiple sclerosis) that might have the psychiatric symptom as

their presenting problem.

While CT is the imaging method of choice for examining skull fractures, the presence of lots of

bone at the base of the skull make CT scans difficult to read for the cerebellum and base of the

skull. MRI on the other hand present advantaged related to soft tissue contrast. MRI scans can

be used to identify an old infarct or tumor. Additionally, MRI can identify problems with white

matter. For example, multiple sclerosis (MS) can often present with psychiatric symptoms,

especially depression. On MRI, the MS plaques can be seen as very bright areas in white

matter where demyelination has occurred.

Areas of increased signal intensity often occur as a normal consequence of aging

(approximately one per decade of life). MRI scans can give exquisite information about brain

size and shape, and this is may be helpful clinically in conditions like Alzheimer’s to assess

atrophy. Suddath and coworkers [Suddath et al., 1990] searched for anatomical correlates of

schizophrenia by performing MRI scans in identical twins, one of whom had schizophrenia and

the other did not. Compared to the twin, the schizophrenia patients clearly had increased

ventricular size. However this finding of is not helpful clinically as it is neither specific nor

sensitive for schizophrenia. MRI scans sometimes find evidence of other brain abnormalities in

neuropsychiatric disorders. For example, midline developmental defects in schizophrenic

patients [George et al., 1989; Scott et al., 1993] or the spreading of brain tumors could be

documented.

2.2 Functional imaging based on MEG

A good description of the MEG principles, instrumentation and applications can be found

elsewhere [Hämäläinen et al., 1993]. Here will be given just a short introduction and the

description of the actual system (section 2.2.3) used for the recording of MEG signals used

throughout this study.


14

2.2.1 History of MEG

While the first human EEG was recorded in 1924 and reported by Hans Berger in 1929

[Berger, 1967], the first measurements of magnetic fields produced by the brain’s electrical

activity were recorded in by David Cohen in 1968. He detected the magnetic alpha rhythm

using an induction coil magnetometer in a shielded room [Cohen, 1972]. This was possible

because of the observation of Zimmermann and colleagues that quantum interference could be

observed in a superconducting loop with a single junction if it was excited by a radio-

frequency bias. The SQUID (superconducting quantum interference device) was developed in

between 1964-1969 [Zimmerman et al., 1970] and used for the first time in biomagnetism in a

joint experiment done at MIT together with David Cohen and Edgar Edelsak.

Electromagnetic measurements have progressed from acquisition of a single or a few channels

to hundreds of signals acquired simultaneously. The measurements of magnetic and electrical

activity result in the magnetoencephalogram and encephalogram and are able to follow changes

in neuronal activity on a millisecond time scale, comparable to the dynamics of the neuronal

assemblies. MEG and EEG are produced directly by the electrical activity through which the

brain communicates and therefore suitable for measuring some of the electrical brain activity

with high (ms) time resolution.

2.2.2 Biophysical bases of MEG and EEG generation

The principal building blocks of the brain are neurons and glial cells. The two principal groups

of cortical neurons are the pyramidal and the stellate cells. The former are large cells, their

apical dendrites from above reaching parallel to each other. These cells are mostly present in

the 3’rd layer of the visual cortex or the 5-th layer in the motor cortex, they rely mainly local

information within different cortical layers. They yield information about the “vertical” activity

below the cortical surface, corresponding to cortical columns. Since they tend to be

perpendicular to the cortex the resulting direction of the electric current flowing in the

dendrites is also perpendicular to the cortex. The corresponding electric field is associated with

a magnetic field, perpendicular to it.

Not all cortical cells generate fields measurable through EEG/MEG. For example the stellate

cells generate “closed fields”, because their dendrites which receive the input have

ramifications in all directions. They constitute the so called “silent sources”. The pyramidal

cells, perpendicular to the cortex and clearly asymmetric along a direction perpendicular to the

cortex, are the main generators of the measured electromagnetic field. The essential property of

these neurons responsible for the E/MEG signal generation is that they are regularly arranged

and activated in a (more or less) synchronous way.


15

The electric and magnetic fields generated in the brain are caused by two distinct processes: a)

postsynaptic and b) action potential currents.

Post synaptic potentials

At equilibrium the concentration of ions inside and outside the membrane is maintained at

specific levels, the Na concentration being higher outside than inside the membrane, whereas

the K concentration is smaller outside than inside. The Na/K pump is involved in maintaining

these levels or returning to equilibrium, it moves three Na ions out and two K ions into the cell

in one duty cycle [Hämäläinen, 1993].

Figure 2.2-1 Networks of cortical neural cell assemblies are the main generators of MEG/EEG signals. Left:

Excitatory postsynaptic potentials (EPSP) are generated at the apical dendritic tree of a pyramidal cell and

trigger the generation of a current that flows from the non-excited membrane of the soma and basal

dendrites to the apical dendritic tree sustaining the EPSPs. Some current takes the shortest route between

the source and the sink by traveling within the dendritic trunk (primary current in blue), while

conservation of electric charges imposes that the current loop be closed with extracellular currents flowing

even through distant parts of the volume conductor (secondary currents in red). Center: Large cortical

pyramidal nerve cells are organized in macro-assemblies, their dendrites normally oriented to the local

cortical surface. Right: Functional networks made of these cortical cell assemblies and distributed at

possibly mutliple brain locations are the putative main generators of MEG and EEG signals (from [Baillet

et al, 2001]).

When a neurotransmitter molecule binds to a postsynaptic receptor, the membrane permeability

is altered for specific ions, and there is a flux of ions through the membrane which gives rise to

a post-synaptic potential of about 10mV, lasting about 10ms. The moving ions give rise to a

current within the cell. This mechanism can be represented as a dipolar field, decreasing as the

inverse of the squared distance. Currents in the surrounding tissues, called volume currents,

close the circuit and so that there is no accumulation of charge in the medium [Plonsey, 1981;


16

Karp, 1981] (Figure 2.2-1).

Both primary and secondary currents contribute to magnetic fields outside the head and to

electric scalp potentials, but spatially structured arrangements of cells are important for the

superposition of neural currents such that they produce measurable fields.

Macrocolumns of tens of thousands of synchronously activated pyramidal cells are believed to

be the main MEG and EEG generators because of the coherent distribution of their large

dendritic trunks locally oriented in parallel, and pointing perpendicularly to the cortical surface

[Nunez et al, 2000]. The activity from 5x5mm patch of cortex, assumed to be 4mm thick, and

made of macrocolumns characterized by a current density 100 nA/mm2 is estimated to be

about 10 nA-m, consistent with empirical evidence [Baillet et al, 2001]. A single synapse

contributions is estimated to be of about 20 fA-m [Baillet et al, 2001], very small to be

detected.

The currents associated with the postsynaptic potentials synchronously generated among the

dendrites of the pyramidal cells are believed to be the main source of the signals detected in

MEG and EEG. One reason is that they typically last longer than the rapidly firing action

potentials [Nunez, 1981]. In addition to postsynaptic potentials, other slow variations of the

membrane potential, such as those associated with depolarizing or hyperpolarizing after

potentials and dendritic events as calcium action potentials may be sources of extracellularly

measured potentials.

Action potentials

The AP propagation along the cell’s axon is modeled by a quadripolar structure generating a

field which decreases with the inverse of the cubed distance [Figure 2.2-2 ].

Figure 2.2-2 Extra cellular sources and sinks in the axons [from Ayache et al., 1999]

Its effect is negligible compared to the post synaptic potential, when measured outside the

cortex, despite the fact that the amplitude of the signal is higher.

The time constant is 1ms compared to 10ms for the postsynaptic potentials, accounting thus for


17

the high frequency components which tend to cancel when averaged over time. Because of

their short duration they tend to overlap much less than the postsynaptic potentials.

This contribution is therefore difficult to measure by current technology in EEG/MEG.

It is believed that because the field generated by the dipoles decreases with the inverse of the

square distance, only the currents produced in the cortex generate signals strong enough to be

recorded. However some authors reported scalp recordings having as source the hippocampus

[Tesche and Karhu, 2000] cerebellum [Tesche and Karhu, 1997], and thalamus [Tenke et al,

1993], [Llinas et al, 1999].

2.2.3 EEG versus MEG

Although EEG and MEG measure the same electrical phenomena they do differ in a number of

aspects which will be discussed below.

The sources of interest are classified as primary and secondary sources [Figure 2.2-3]. The

primary sources reflect the impressed current density, due to transformation of energy from

chemical to electrical and microscopic passive cellular currents. They represent areas of

activity associated with a given sensory, motor, or cognitive task. The secondary sources are

the volume currents that result from the macroscopic electric field. Both types of sources

produce an electric and magnetic field.

Figure 2.2-3 MEG and EEG measurements differ in that MEG is more sensitive to the intracellular current

or the primary sources (left) and EEG to the extracellular current or secondary sources (right)

EEG measures a sum of potentials caused by primary and secondary sources. It is more

sensitive to the effects of (secondary) currents, since only these can volume conduct to the

scalp to produce the EEG. Cohen and Cuffin [Cohen and Cuffin, 1983] stated that MEG

measures mainly the intercellular current while not being affected by these volume currents,

since their effects cancel due to symmetry. This may explain a more focal pattern in MEG

relative to the EEG potential distribution.

The average behavior of a population of pyramidal cells can be modeled by a dipole with a

magnetic field normal to the cortex and an electric field tangential to it.

MEG's primary response is to tangential generators, whereas EEG is sensitive to both

tangential and radial generators. Given the arrangement of pyramidal cells as dipole layers,


18

perpendicular to the cortical surface, in general the EEG measurement will give information on

the activity of cells located in gyri (parallel to the skull) but also in sulci, while MEG will give

information on the activity of cells located in sulci (normal to the skull) [Figure 2.2-1b]. MEG

gives clear information about tangential dipoles, which might be obscured by radial sources in

EEG.

The map representing the distribution of the magnetic field at the surface of the head caused by

a tangential current dipole is rotated at 90o relative to the corresponding EEG map. Differences

in the localization of dipoles with the two methods may be due to the directions along which

measurements are made.

The magnetic field given by a distribution of impressed current source elements ji located

within a finite, inhomogeneneous conductor (divided into homogeneous regions vj with

conductivity σj,) with spherical symmetry is given by:

Equation 2.2-1

Where r is the distance from an external field point at which is evaluated to an element of

volume dv inside the body, dv is a source element, and is an operator with respect to the

source coordinates.

The first term on the right-hand side of Eq. 2.2.1 represents the contribution of the volume

source, and the second term the contribution of the boundaries between regions of different

conductivity.

The influence of inhomogeneities is larger on EEG than on MEG. The field potentials are

influenced not only by the geometry of the neuronal populations and the electrical properties of

individual neurons but also by the presence of inhomogeneities, regions with different

conductivities. The layers or compartments lying around the brain (i.e. CSF, skull and scalp)

attenuate the original signal. The scalp EEG is most influenced by the conductivities of the

skull and scalp while MEG by the conductivities of the brain and CSF [Haueissen, 1996]. The

effect of anisotropic conductivity of the skull is the smearing out of the distribution of EEG of

the scalp, whereas the normal component of MEG is not affected [da Silva and van Rotterdam,

1999].

To explain the EEG the conductor model assumes the existence of layer with different

conductivities. These cause and attenuation of the signal and introduce equivalent dipole layers

at the boundaries of the compartments. However the brain and surrounding tissue behave like a

medium with constant magnetic permeability. Therefore the magnetic field is not influenced by

these layers, nevertheless it is affected by the induced dipole layers existing at the boundaries


19

of the compartments with different conductivity.

While EEG is a relative measurement, no electrodes or reference point is needed for MEG.

Also the transducers need not touch the scalp, because the magnetic field does not disappear

where the conductivity is zero.

There are differences in the MEG and EEG measurements regarding the representation of some

non dipolar sources (sources arranged in the form of lines, longer than 2cm give different

results for example) and these differences may be helpful in identifying the type of such

sources in the brain [da Silva and Van Rotterdam, 1999].

In the presence of noise it appears that MEG and EEG have different capabilities of retrieving

the position and orientation parameters: EEG gives better estimates of the orientation, while

MEG on the position. The inverse procedure fails more often in cases of bad SNR for EEG.

The use of MEG and EEG may complement each other and lead to a better model and a more

precise localization of the source.

2.2.4 The experimental system

The MEG system measures the very weak magnetic fields produced by the cooperative activity

of neuronal assemblies. Neuromagnetic fields are typically 50-500fT in strength, while the

earth's magnetic field is about 5x1-5T and the electromagnets used to pick up cars generate a

field comparable to that of MRI clinical machines (1.5-2.0T). The tiny fields generated by

neuronal assemblies require a very sensitive detector and eventually shielding from the

surrounding magnetic fields.

The only device with sufficient sensitivity for high quality biomagnetic measurements of

magnetic flux generated by the brain’s activity is the SQUID magnetometer, which operates at

low temperatures produced with liquid helium.

The SQUID is based on the Josephson effect. It consists of superconducting ring interrupted by

one or two Josephson junctions. The recording dewar contains magnetic detection coils,

continuously bathed in liquid helium to superconducting temperatures of 4K (-269oC). The flux

sensitivity of a SQUID is about 10-15 Tm2. To improve the sensitivity a flux transformer is

used. A flux transformer is a simple superconducting coil which collects ambient flux. This

initial 'pickup' (input) coil is connected in series to a second input coil which is tightly

inductively coupled to the SQUID itself. This simple circuit has the effect of funneling the flux

trapped in the pickup coil into the SQUID loop, thus providing enhanced flux sensitivity

(Figure 2.2-4 a).


20

The gradiometer consists of an inductively coupled flux transformer with two coils connected

in series and oppositely wound. Because of the opposite winding a uniform magnetic field will

not induce flux into the SQUID. If a magnetic field with a spatial gradient is applied in the z

direction there is a difference in fluxes in each coil and therefore a net current in the

gradiometer - a flux is then generated in the SQUID loop (Figure 2.2-4 b).

Figure 2.2-4. Flux transformers used to transport flux to the SQUID loop. (a) shows a simple one coil flux

transformer, (b) a first order gradiometer [from Singh, 1995].

The detection of such small magnetic fields in the presence of high background magnetic noise

requires shielding and compensation of the existing fields and therefore the recordings are

usually done within magnetically shielded rooms.

The main problems with shielded rooms are that they are very expensive and often do not

provide adequate signal to noise ratios. They fail to provide defense against noise sources from

within the patient such as the heart. This problem was overcome by considering distant noise to

be spatially uniform at the detector and the use of gradiometers which makes the system

sensitive only to spatial gradients of the field, rejecting sources of noise. The magnetic fields

generated by the brain decay rapidly with the distance.

Because the lower coil is much closer to the brain's activity than the upper coil, the output is

roughly equal to magnetic field. The gradiometer is less sensitive to distant sources of

interference and slight vibrations of the instrument than a magnetometer. Forming a virtual

second or third order gradiometer by using a reference system further extends this principle.

The formation of third order gradiometer technique actually allows the system to be operated

without any magnetic shielding, or in cooperation with a shield to achieve even better

performance.

a b


21

Magnetometer based systems optimize signal strength but gradiometer based systems optimize

signal to noise ratio, which is the more important value when examining MEG data.

Whole head MEG systems are available with a different number of channels, including may

have between 64, 149, 151 and 275 MEG channels to simultaneously measure activity across

the entire cortex.

The MEG of the BSI in RIKEN is a whole-head Omega biomagnetometer (CTF Systems, Inc.,

Vancouver, BC, Canada). The biomagnetometer consistes of Niobium detection coils emerged

in liquid helium contained within a Dewar. The helmet-shaped, liquid helium-filled dewar is

placed inside a shielded room (NKK, Japan). The helmet houses 151 primary channels

uniformly distributed in the inner lower surface of the dewar and 28 reference sensors. Each

primary channel is a first-order axial gradiometer with the two 1-cm-radius coils separated by 5

cm. The proximal sensor coils are on average a little over 3 cm from the (outside) wall of the

helmet. The distance between a proximal sensor coil and the brain surface varies from 5 to 8

cm depending on the shape and actual position of the subject’s head inside the dewar. The

reference channels are a collection of magnetometers and gradiometers placed about 30cm

above the uppermost gradiometer inside the dewar away from the subject’s head. The signal

from a subset of the reference channels is used to construct in the software a synthetic higher

order signal which behaves like a gradiometer with a long baseline and is therefore effective in

eliminating noise from distant sources. In addition to the MEG channels the EOG and ECG can

be acquired at the same time for offline noise elimination

Figure 2.2-5. The MEG system at RIKEN BSI is an OMEGA 151 (151 channels) (from

www.vsmmetdtech.com).

The equipment used to measures the very weak magnetic fields produced by electric activity in

the brain is a Whole-Cortex MEG System (Omega151) produced by CTF System Co. The


22

recording of the magnetic field around the head is characterized by good spatial sampling

(average distance between coil sensors: 3.1cm). The signal from all 151 sensors is sampled

simultaneously with (routine) sampling rate 1250 Hz.

The sensors include: axial gradiometer with 5cm baseline and 2cm coil diameter. Additionally

29 reference sensors are placed on the far side of the MEG array to allow software third

gradient mode for effective elimination of environmental noise. It is possible to record

simultaneously with the MEG electrical channels to monitor heart and eye blinks and 64

channels of EEG (electroencephalogram) Measurements are possible with the subject in sitting

or supine positions Figure 2.2-5.

The EEG system consists in two Synapms Amplifiers (Neuroscan, LTD) each having 32

channels; 28 monopolar and 4 bipolar channels, connected to the cap electrode array. The

Neuroscan Scan 4.1 (EEG analysis software) is used for recordings. The Polhemus Stylus

Fastrack digitizer is used to localize the electrode positions and to acquire the head shape.

2.2.5 Inverse Problem

The localization of neuronal activity based on measurements of electric potentials (EEG)

and/or magnetic field (MEG) involves solving two distinct but related problems. The “forward

problem” consists in calculating the electric potential and magnetic field outside the head,

given current sources and conductive properties of the tissues. The “inverse problem” consists

in determining the properties of the current sources within the brain based on the external

electric and/or magnetic measurements.

For a problem to be well-posed in the Hadamard sense, it must meet the following criteria: 1)

for each set of data, there exists a solution; 2) the solution is unique; 3) the solution depends

continuously on the data. While the bioelectric forward problem is well defined and is

governed by the quasi-static limit of Maxwell equations the inverse problem based solely on

EEG and/or MEG measurements lacks two of the three criteria for being well-posed: there is

not a unique solution and the solution doesn't depend continuously on the data. The lack of

uniqueness means that for any distribution of EEG and MEG signals outside the head there are

infinitely many possible configurations sources within the brain that are consistent with these

recordings, a principle originally pointed out by Helmoltz [1853]. The second property means

that small errors in measurements may cause large variations in the solution. This indicates that

there is a serious mathematical problem but in practice and especially for superficial sources

this does not lead to serious practical limitations.

Additional information or assumptions are added in order to solve the inverse problem and

these include [Malmivuo and Plonsey, 1995]: using simplified models for the source and the


23

volume conductor; imposing physiological constraints; recognizing the typical signal pattern

associated with certain source configurations (empirical approach); examining the lead field

pattern from which the sensitivity distribution of the lead and therefore the statistically most

probable source configuration can be estimated.

The model for the volume conductor can be a simple, spherical model or realistic, based on the

MRI of the head [Wieringa and Peters, 1993] which allows the extraction of the 3D geometry

of the brain and surrounding tissues. Numerical methods like the boundary element or volume

element methods are used in the later case. The necessity of using realistic models, particularly

those accommodating inhomogeneous volumes, is stronger for EEG than for MEG [da Silva

and Van Rotterdam, 1999]

The simplified signal source models traditionally used are equivalent current dipoles ((ECDs)

which should have good correspondence with the physiology and anatomy associated with the

actual source distribution. In the case of simplified source models at least as many independent

measurements are made as the model has independent variables in order to have as many

equations as unknowns and to evaluate the variables of the model [Malmivuo and Plonsey,

1995]. To reduce the sensitivity to noise the number of measurements greatly exceeds the

number of variables in the source model. The over specified system of equations is solved

using least squares approximation and possibly other constraints to improve the stability.

For the single moving dipole the position and moment are determined for each time moment by

a separate fit of the field. The position is allowed to move as time progresses. For the fixed or

rotating dipole the time course of the dipole’s strength is also specified, in addition to the

position, and in the case of the rotating dipole also the orientation. Time-varying multidipole

models were used with complex global optimization and fitting methods.

In the time-varying dipole model introduced by Scherg and von Cramon [Scherg and Cramon,

1985] an epoch of data is modeled with a set of spatially fixed dipoles whose amplitudes vary

with time. De Munck [deMunck, 1990] proposed the following approach based on the

stationary dipole model, proposed by Scherg and Von Cramon, and separating the parameters

into linear and nonlinear. First the functions that describes the change of the source as a

function of time must be estimated for a given position and orientation of the ECD; second the

orientation must be found given the time functions and position. These steps are performed

iteratively till the best time functions and orientation are found for a given position. Then the

nonlinear position parameters are updated and the process repeated until a best fit is obtained.

The localization accuracy reported for dipolar inverse solutions is smaller than 13 millimeters

[Leahy et al., 1999]. One drawback of this approach is that ECDs do not indicate the activated


24

volume but only its gravity center. Also the use of dipole models is meaningful if the activity

has a focal character and the number of possible sources can be anticipated with reasonable

accuracy [da Silva and Van Rotterdam, 1999].

The continuous current source mode is more biologically plausible than the discrete current

dipole model and readily allows the incorporation of external information, e.g fMRI [Liu et al.,

1998]. The number of solutions in this case is in general much larger than the number of

sensors and the problem is underdetermined. The minimum norm algorithm introduced by

Hämäläinen and Ilmoniemi in 1984 was the first effort in this direction. An algorithm using

direct Fourier based inversion was proposed by Dallas and colleagues [Dallas 1985; Kullmann

and Dallas, 1987] and reconstructions confined to an anatomically defined surface [Wang et

al., 1992]. More general linear frameworks for 3D reconstructions were introduced by Sarvas

[1987] and a probabilistic approach by Clarke and Janday [1989]. Magnetic Field Tomography

(MFT) introduced a two step procedure for the estimation of the general parameters, a

probability weight which could be used for the incorporation of additional information and an

iterative procedure to solve a non-linear algorithm for the estimation of the current density

(Ioannides et al. 1990). The MFT algorithm adopted the theoretical framework of Clarke and

Janday but used problem-defined expansion functions (the lead fields used by the original

minimum norm method) which together with the non-linear estimation algorithm were capable

of tomographic reconstructions of focal or distributed generators, without prior assumptions

about their number or nature [Ioannides et al 1989; 1990]. MFT allows the 3D reconstruction

of the dynamic brain activity with a spatial resolution of 2-5 mm at the cortical level [Ribary et

al., 1991; Moradi et. al., 2003], which deteriorates with depth. This procedure produces more

focal images than the traditional minimum-norm solutions. Another possibility is to use a

MUSIC-type probability weighting [Mosher, 1992] combined with cortical constraints to focus

the image [Dale and Sereno, 1993].

The techniques for estimating the distributed sources may include Bayesian approaches [Baillet

and Garnero, 1997; Phillips et al., 1997; Schmidt et al., 1999] or constrain the current location

and orientation perpendicular to the cortical surface [Wang et al., 1992; Dale and Sereno, 1993;

George et al. 1995; Baillet and Garnero, 1997]. The use of constraints has been introduced as a

restriction of the activated regions to foci identified by similar fMRI experiments [Di Russo et

al., 2002]. Adaptive beamformer techniques were used for MEG [Van Veen et al., 1997; Gross

and Ioannides, 1999; Sekihara et al., 2001] but these work best if the sources are uncorrelated.

Methods for tomographic reconstructions are also available today for EEG [Michel et al., 2001;

Stern and Silbersweig, 2001] but these tend to have less resolution than the corresponding


25

reconstruction for MEG data, at least when simple models for the conductivity profile are used

for the forward problem.

2.2.6 MEG: Clinical and research application

Due to EEG's long tradition and its role as a gold-standard in certain clinical applications it is

anticipated that simultaneous MEG and EEG will be used as complementary tools.

MEG has been used in normal subjects to give information on brain functions including touch,

speech, vision, hearing and motor skills but also in the assessment of pathological functional

deficits, neuropharmacological investigations, trauma assessment, epileptic assessment and a

growing list of research investigations in neuroscience and psychiatry.

MEG can contribute to clinical care in: (1) the pre-surgical MEG mapping of neurosurgical

candidates who have brain tumors or vascular malformations in the region of the sensori-motor

strip; (2) the assessment of pathological functional deficits or evaluation of residual

neurological function after severe head injury; (3) the assessment of pathological functional

deficits in epilepsy, the most common clinical application of MEG being in the planning of

epilepsy surgery; (4) assessment of traumatic brain injury. In the case of mild head trauma

patients may have normal MRI and CT and normal clinical EEG, but abnormal

neuropsychological function. MEG provides evidence of traumatic brain injury (for ex.

anomalous delta and theta bursting) in this population with a higher sensitivity compared to

MRI and EEG.

Also the role of MEG has been studied for in: (1) fetal brain activity monitoring; (2)

schizophrenia [Rojas et al., 2002] or; (3) migraine [Bowyer et al. 2001], (4) neurological

evaluation of neuropharmaceuticals.

The comparison of pathological (in the presence of tumor, infarct, injury, etc.) versus normal

subject responses following auditory, visual or somatosesnsory stimulation may reveal

abnormal sources location and response patterns. Somatosensory evoked magnetic fields have

been employed for example for assessment of traumatic brain injury.

MEG shows potential as a diagnostic method which could replace invasive EEG monitoring in

some patients, guide intracranial electrode placement, and reduce the length of EEG evaluation

prior to surgery.

2.2.7 Coregistration of MRI and MEG

The location of the activated area is best examined in the background of the anatomy, using the

MRI. In our case MRI images were acquired for each subject with either of a: 1. 1.5-T Siemens


26

Magneton Symphony or 2. Varian Unity Inova 4T system. The co-registration of MEG and

MRI data is performed using 3 or more landmarks on the head of the subject. These points are

digitized in the MEG coordinate system using a position sensing device, a Polhemus Isotrack

device in our case. This locates the dewar and gradiometers with respect to a head-centred

coordinate system.

Before MRI scanning, capsules containing vitamin E oil or another contrast media can be

attached to the fixed marks on the head. These show up as bright white marks on the MRI

scans and allow a coordinate transform to be calculated which places the MRI slices in the

head-centered coordinate system. Alternatively more complex registration procedures can rely

on the head anatomy.

Figure 2.2-6 Landmark sites can be used for corregistration. Capsules of vitamin E are placed at the nasion

and preauricular points, and in the same places are placed the MEG localization coils. Images from

http://www.ctf.com/Pages/page27.html.

Before the MEG procedure (three) probe coils are attached to the scalp, one close to the nasion

and two close to the preauricular points (these are the same landmark sites as the Vitamin E

capsules used during the MR scan). The coil positions and subject’s head shape (a few

thousand points) are digitized with a three-dimensional digitizer (Polhemus, 3DSpace/Fastrak,

U.S.A.). The coils can be detected by the MEG system itself, form a coordinate system that can

be used to register the MEG and MRI together. The digitized points are then matched to the

surface of the scalp using dedicated software developed at RIKEN for this purpose. Before and

after each run the probe coils are activated and their positions identified by the CTF software.

This defines the exact position of the sensors relative to the coils and hence with respect to the

head of the subject and its MRI representation. The procedure yields an accurate

superimposition of the estimated sources on the corresponding MRI slice. This allows solutions

to be compared on the same anatomical background from runs of the same experiment or even

across different experiments on the same subject.

2.3 Conclusion

A problem in functional brain mapping is how to best combine the advantages of


27

complementary techniques as those dedicated for anatomical imaging (MRI) and those for

functional imaging, like MEG and EEG or PET, SPECT. Unique among the functional

techniques because of a high temporal resolution, complemented with noninvasive nature,

MEG and EEG can provide new insights for understanding brain function. The measurements

are non-invasive, and make possible the repetition of measurements with the same human

subject.

The information EEG and MEG provide is considered to be complementary, as MEG is best in

detecting sources within the sulci of the cortex while EEG detects both tangential and radial

sources but has a lower spatial resolving power and is hindered by the high resitivity of the

skull. For MEG this layer appears as transparent and one more advantage is that it does not

require that EEG electrodes be pasted onto the scalp. However MEG is an expensive technique

yet because of the high costs involved in creating the conditions for a high sensitivity magnet

and the suppression of magnetic background.

Chapter3. Brain segmentation

28

Chapter 3. Brain Segmentation

Chapter 3. Brain Segmentation ........................................................................................................28

3.1 Introduction ...................................................................................................................... 28

3.1.1 Definitions related to image segmentation...............................................................28

3.1.2 Applications of segmentation...................................................................................29

3.1.3 The problem of brain segmentation .........................................................................32

3.1.4 Mathematical morphology for image analysis .........................................................35

3.1.5 White matter – gray matter separation .....................................................................38

3.2 A mathematical morphology based method for brain segmentation................................ 40

3.3 A modified fuzzy c means method for white matter-gray matter separation................... 44

3.4 Geometrical, differential properties of the cortex ............................................................ 47

3.5 Results .............................................................................................................................. 51

3.1.6 Whole brain segmentation........................................................................................51

3.6.1 Gray matter- white matter separation with bias field compensation........................52

3.6 Discussion and Conclusion .............................................................................................. 53

3.1 Introduction

3.1.1 Definitions related to image segmentation

Image segmentation is defined as partitioning of an image into non-overlapping, constituent

regions, homogeneous with respect to image characteristics like intensity or texture [Pham et al.,

2000].

The segmentation problem is to determine the sets Sk included in the image domain I, whose union

is the entire image I. The sets that make up the segmentation result must satisfy:

UK

kkSI

1=

= , where =jk SS I Ø ; for k≠j and each Sk is connected.

When the connectedness constraint is removed the problem is called pixel classification and the

sets are called classes.


29

Labeling is the process of assigning a meaningful designation to each region or class and can be

performed separately from segmentation. Essentially the numerical index k of a set Sk is mapped

to an anatomical designation. Often in this case the value of K is assumed to be known based on a

priori knowledge of the anatomy. An example of labeling is the assignment of labels to healthy

versus tumoral tissue type.

To differentiate between classification and segmentation one shall think of segmentation to be a

top-down parceling of an image into anatomically meaningful continuous groups of voxels; while

classification shall be viewed as the bottom-up (data driven) labeling of voxels with a tissue class

label without demanding spatial contiguity for a class. The image data represent only one measure

(or a few measures in the case of multi-spectral data) concerning the underlying anatomy, and by

itself is sufficient only for classification. Anatomically distinct regions of the brain are

differentiated on the basis of histology, cytoarchitecture, connectivity, cytochemistry or function.

As such, data from external sources are required to constrain and guide the segmentation process.

3.1.2 Applications of Segmentation

Segmentation has application in robot vision, object recognition and medical imaging.

Segmentation is an important issue in biomedical engineering with applications at different levels:

from large anatomical structures like: brain, heart, knee, jaw, spine, pelvis, liver, prostate, and

blood vessels till the microscopic, cellular or nuclear level.

Frequently used to improve visualization of medical imagery and allow quantitative measurements

of image structures, segmentations are also valuable in building anatomical atlases, researching

shapes of anatomical structures, tracking anatomical changes over time, and cell counting.

The traditional method of medical image analysis, used to be the inspection of two-dimensional

grayscale images produced on films placed on a light box. New imaging techniques produce

digital images, carrying a lot more information stored in a format (DICOM for example) suitable

for sharing and communicating data. One main advantage of these techniques relates to their

suitability for detailed quantitative analysis of the location, appearance, size, or shape of patient

anatomy and the segmentation is one important step in this analysis. Starting from the

segmentation the analysis may include three-dimensional visualization, volumetric measurement,

shape analysis, image-guided surgery, and detection of anatomical changes over time.


30

3.1.1.1 Visualization

Segmentation of medical imagery allows the creation of 3D surface models which can be then

inspected from all angles and also sliced to offer the traditional representation of 2D grayscale

images, this time at any orientation and position by means of interpolation algorithms.

3.1.1.2 Volumetric Measurement

Measurement of the volumes of anatomical structures is necessary in medical studies, both of

normal anatomy and of various pathological conditions or disorders.

One example comes from the study of schizophrenia, where volume measurements of the lateral

ventricles, structures in the temporal lobe such as the hippocampus, amygdala, and

parahippocampal gyrus, the planum temporale, and the corpus callosum are used to study the

variation in neural anatomy between schizophrenic and control patients.

3.1.1.3 Image registration

Improvements in segmentation aid the techniques for image registration, such as surface matching

methods, landmark matching or atlas-based registration.

3.1.1.4 Shape Representation and Analysis

Various quantitative representations of shape are studied in order to mathematically describe

salient anatomical characteristics.

One example of a shape representation is a skeleton, a construct which is similar to the centerline

(or medial axis) of a segmented structure. One way to imagine a skeleton is the “brush fire”

approach: one thinks of simultaneously lighting fires at all points on the boundary of the structure.

The fires burn inward, traveling perpendicular to the boundary where they started, and then

extinguish when they hit another fire. The connected “ash” lines left where the fires extinguish is

the skeleton of the structure.

A richer shape representation is the distance transform, a function that measures the distance from

each point in a structure to the nearest point on that structure’s boundary. The distance transform

can also be imagined with the pyrotechnic approach: it is the time that the fire first reaches each

point in the structure. Consequently it is considered richer than the skeleton, since it contains more

information.


31

Shape representations are important for quantitative anatomical comparisons. Distance transforms

shape representations have been applied to the classification of anatomical structures in a study

that aims to differentiate between the hippocampus-amygdala complexes of schizophrenics and

normals [Golland et al., 2000]. An example of grayscale MR image data and the shape

representation derived from it for this study can be seen in Figure 3.1-1.

Figure 3.1-1. Shape representation example. A segmentation of the hippocampus-amygdala complex (left), a 3D

surface model of the hippocampus-amygdala complex (center), and a distance map used to represent the shape

of the hippocampus-amygdala complex (right) [from Golland et al., 2000]

Shape representations can also be used to aid the segmentation process itself by providing

anatomical knowledge. A generative shape model, trained from a population of shape

representations, can be used to visualize new shapes according to the learned modes of variance in

the shape population (allowing visualization of “average” anatomy and of the main anatomical

variations that may occur). Then, at each step of the segmentation of new data, fitting the model to

the current most likely segmentation can provide anatomical information to the algorithm.

3.1.1.5 Image-Guided Surgery

In order to remove brain tumors or to perform difficult biopsies, surgeons must follow complex

trajectories to avoid anatomical hazards such as blood vessels or brain areas which are of

functional importance to the patient (speech, use of right hand, etc). Before surgery, path planning

and visualization is done using preoperative MR and/or CT scans along with three-dimensional

surface models of the patient’s anatomy

During the procedure, the results of the preoperative segmentation may still be used: the surgeon

has access to the pre-operative planning information, as three-dimensional models and grayscale

data are displayed in the operating room. In addition, “on-the-fly” segmentation of real time

imagery generated during surgery has been used for quantitative monitoring of the progression of

surgery in tumor resection and cryotherapy. The tissue may be deformed during the process.


32

3.1.1.6 Change Detection

For longitudinal studies, spanning over a lengthy time interval, segmenting regions of interest is

crucial for quantitative comparisons. Multiple sclerosis (MS) is a disorder that progresses over

time, accurate temporal measurements of neural changes may lead to a better understanding of the

disease. The goals of this kind of projects are analysis of lesion morphology and distribution,

quantitative evaluation of clinical drug trials, and monitoring of disease progression in individuals.

To this end, automatic segmentation is used to identify MS lesions. The volume of such lesions, as

measured from segmented data, has been shown to correlate with clinical changes in ability and

cognition.

3.1.3 The problem of brain segmentation

Cortical segmentation using MRI has been one of the most needed items on the list of engineering

in medical research. It is one of the most pressing needs for neuro-anatomical analysis because it

helps in: (1) the quantification of cortical thickness and cortical folding curvatures [Zeng et al.,

1999] and (2) in determining the spatial inter-relationships of the neuro-anatomical structures. The

quantification of specific regions is required for long-term monitoring of a disease progression or

remission. Since the manual cortical segmentation methods are subject to errors both in accuracy

and reproducibility and are time-consuming, fast, accurate and robust semi-automatic or

completely automatic techniques are needed.

The problem of brain segmentation is challenging due to the complexity of the images and of the

structure of interest. Manual segmentation is extremely tedious for large structures, especially for

the gray or white mater boundaries. The complexity of the cortical sheet folding makes slow and

impractical even the segmentation for a single subject. Moreover, the manual segmentation may

result in large inter and intra-segmenter variability. Automatic methods are desirable since studies

which compare different aspects of brain morphology across subjects typically involve vast

amounts of data and therefore manual segmentation is very time consuming. There are still

challenges to be met for the “automatic” methods. These mainly relate to the complex shape and

appearance of structures, i.e. anatomically distinct tissues can have the same gray-level appearance

while the same tissue can have different gray-level appearance in different regions of the image

due to distortions caused by the imaging equipment.

Traditional image segmentation methods rely on intensity values and thresholding. The

thresholding process is complicated by the overlapping intensity distributions of brain and non-


33

brain structures (skin- gray matter, fat, bone, and optic nerves –white matter), image acquisition

limitations (intensity inhomogeneity, partial volume effects, etc), and motion artifacts. One effect

of the intensity inhomogeneity may be that the same tissues do not have a unique range of values

and the histogram analysis for different image regions shows that the grey value ranges may vary

from slice to slice for the same type of tissue, and even within the slice.

One solution to this problem is to identify the largest connected component with gray values

corresponding to the brain. The method proposed by Höhne and Hanson relies on the use of

mathematical morphology [Höhne and Hanson, 1992]. The next step would be to ensure that the

topology is equivalent to a sphere. If this is not the case the topological defects are removed using

editing tools [Teo et al., 1997] or automatically [Kriegeskorte and Goebel, 2001]. Later

developments use this surface to initialize a deformable surface which aims to refine the boundary

[Kapur et al., 1996]. The model be placed outside the brain but these methods have difficulty

progressing within the narrow sulci the sulci or from within, from the WM/GM interface which

will contain larger sulci. An approach relying on the use of double deformable surfaces has been

proposed by MacDonald [MacDonald et al., 2000] for identifying both the internal and external

surfaces of the gray matter.

In general these methods need an initial model, based on previous segmentations, or a statistical

atlas. Spherical models circumvent this need and have been applied for structure’s segmentations

[Tek and Kimia, 1997]. But these models still need to be placed in the proximity of the structures

boundaries for good results and sometimes need to be manually initialized. These methods may

require also the transformation of the current brain and may therefore introduce uncertainties.

Modern techniques for segmentation relying on deformable surfaces require

Some of the main difficulties in achieving a robust and fast cortical segmentation are:

(1) Partial volume averaging (PVA): This is caused by the finite extent of an imaging system's

point spread function (PSF). If there is more than one tissue within the extent of a partial density

function or one voxel, then PVA is prominent;

(2) Tissue inhomogeneity and non-uniformity are intrinsic properties that add to the boundary

fuzziness regardless of the imaging system's quality. Due to the spatial inhomogeneities in the

radio-frequency (RF) gain in the RF coil, the intensities associated with these two tissues overlap;

(3) Shading artifacts: There are three kinds of artifacts: (i) hardware, (ii) those due to MR physics

and (iii) patient-related. The hardware related artifacts include: zipper artifact, corduroy artifact,

dot artifact, data clipping artifact, spurious echo artifact, coherent ghosting artifact, and calibration


34

artifact. The MR physics related artifacts are: magnetic susceptibility artifact, chemical shift

artifact, truncation artifact and criss-cross artifact. The patient-related artifacts are a result of the

motion artifacts due to voluntary or involuntary patient movements.

(4) Random noise associated with the MR imaging system;

(5) Convolutedness and variability of the brain structure. This is due to the complex topology

causing bends and twists. Besides that, this morphological shape differs from subject to subject.

(6) Variability in tissue types: This is due to the number of tissue types and connectivity (classes)

present in the tissue volume such as optic nerve and blood vessels.

(7) Size and types of brain tumor. Current techniques are suitable mainly for medium to large-

sized tumors.

(8) Operator variability: This is due to the variability in tracing (intra-and inter-observer

variability) of the cortical boundary/regions for image segmentation algorithms;

(9) Error susceptibility. This refers to failures for fully supervised methods;

(10) Imaging variability: Variability in imaging parameters such as inter-scan interval, voxel

dimension, signal-to-noise ratio, position and orientation of the subject in the scanner cause

complications in the segmentation process

(11) Availability of shape models: This accounts due to the absence of explicit shape models that

capture the deformations in human brain anatomy and topology. Work has been done in this

direction, especially for gray matter structures and the parietal and occipital lobes 20 normal MR

brain data sets and their manual segmentations are provided by the Center for Morphometric

Analysis at Massachusetts General Hospital and are available at http://neuro-

www.mgh.harvard.edu/cma/ibsr.

When choosing among the many segmentation approaches the following factors shall be taken into

consideration: (1) the application used, (2) the kind of supervision needed/not needed, (3) the

accuracy, (4) the robustness desired, (5) the speed issues.

The segmentation techniques may be classified into three core classes. Techniques which use: (i)

the regional-based approach, (ii) the boundary/surface-based approach and (iii) the fusion of

boundary/surface with region-based approach.

Region-based techniques: (1) atlas-based and threshold; (2) mathematical morphology; (3)

probability; (4) clustering; (5) texture-based; (6) knowledge-based; (7) neural-network; (8) region-

linking with hyperstack and (9) fusion of the above. Probability-based techniques are further

classified into: (i) Bayesian-based, (ii) expectation-minimization-based and (iii) Markov Random


35

Field-based. Clustering-based techniques are classified into: (i) FCM and (ii) AFCM types. A class

of techniques exists which consists of the fusion of: (i) texture with pixel-classification-based, (ii)

knowledge-base with pixel-classification, (iii) neural-network based with pixel-classification and

(iv) pixel-classification with edge-based.

Boundary/surface-based techniques are classified into four major types: (i) edge; (ii)

reconstruction; (iii) parametric and (iv) geometric-based. Parametric-based techniques are further

classified into two types: (1) 2-D-based and (2) 3-D-based. The 3-D based parametric-based

techniques are further classified into: (1) ribbon; (2) topological-surface; (3) constrained

parametric; and (4) parametric-based with threshold/connected components.

Boundary/Surface fused with Region techniques are classified into two types: (i) parametric and

(ii) geometric-based. Parametric-based is classified into two types: (1) parametric fused with EM-

based and (2) parametric fused with clustering. Geometric-based are classified into four types: (1)

level sets with Bayesian-classification; (2) coupled level sets with classification; (3) level sets with

clustering, and (4) level sets with shape modeling.

One categorization can be made in model based methods or methods which do not rely on a model

to initialize the segmentation. Our method belongs to the first class.

Within the methods relying on intrinsic parameters found in the image there are multispectral or

unispectral methods, ours uses T1 weighted images only. At the chore of the method is the

application of mathematical morphology. A short introduction in mathematical morphology is

given in the next section 3.1.4.

3.1.4 Mathematical morphology for image analysis

Mathematical morphology (MM) was introduced in 1964 by Matheron and Serra, [Serra, 1982]

from a need to analyze texture in petrography. Later on MM methods came to be used not only for

studying the arrangement of spectrographic phases, milling of rocks but also for brain histology,

dynamics of cloud movements, automatic reconstruction of relief maps, character recognition,

segmentation of cancer cells, etc. These methods use the notion of geometrical structure, or

texture, quantified by introducing the concept of “structuring element”. The structuring elements

interact with the object of study, modifying the shape and reducing it to a form more expressive

than the original. The main objective of morphology is to reveal the structure of the objects by

transforming the sets which model them.

Four principles must be satisfied by every morphological transformation and its measure, namely:


36

(i) translation invariance; (ii) scale invariance; (iii) locality (local knowledge); and (iv) semi-

continuity.

Mathematic ally these properties can be expressed:

(i) hh xX )]([)( Ψ=Ψ ;

(ii) )()( XX Ψ=Ψ λλ

(iii) :ZboundedZbounded ∃′∀ ZXZZX ′∩Ψ=′∩∩Ψ )()]([

The transformation Ψ satisfies the local knowledge principle if, for any bounded set Z’ in which

we want to know Ψ x , we can find a bounded set Z in which the knowledge of X is sufficient to

locally perform (within Z’) the transformation.

(iv) For every decreasing sequence of closed sets tending towards a limit∆ , and every increasing

transformation1 Ψ , there must correspond a sequence of transformed sets tending towards the

transform of ∆ . In other words, the boundary of the transform is equal to the transform of the

boundaries.

A central place in mathematical morphology is occupied by the hit or miss transform, a point by

point transform of a set.

If X is a set and B a structuring element and suppose B is centered at the point x. with components

B1 and B2. Suppose B is ceneterd in x and we denote this by Bx1, Bx

2. A point x belongs to the hit

or miss transform BX ⊗ of X, if and only if Bx1 in X and Bx

2 is included in the complement Xc of

X.

{ }cxx XBXBxBX ⊂⊂=⊗ 21 ;:

It was thought that characterizing quantitatively a structure mean assigning it a few representative

parameters (for the plane: area, number of intercepts, number of particle). Behind each parameter

lies one elementary transformation. The area can be interpreted as the number of times a test point

(belonging to the structuring element) hits the image under study when translated on the grid,

vertex to vertex. The number of intercepts has behind a transformation involving a pair of

consecutive points. The connectivity number can be calculated in a similar manner. The smallest

structuring element of 0 dimension (the point), of 1 dimension (the doublet) and two dimensions

(the triplet) open the way to the area, length, and number measurement on the set X. If the 1 An increasing transformation satisfies the following: if A included in B then the transform of A

is included in the transform of B.


37

dimension of the structuring element is added to the dimension of the parameter measured we

obtain a constant, which represents the number of dimensions of the object space.

Two basic operators are dilation and erosion.

Erosion is a particular case of the hit and miss transform, where B2 is the empty set.

The eroded set X B of X is defined as the locus of centers x of Bx included in the set X:

X B }|{ BBx x ⊆= =IBb

bX∈

The dilate BX ˆ⊕ is the locus of the centers of the Bx which hit X. The definition of dilation is

given relative to the transposed (reflected) set of B, so as to make an analogy with erosion.

UXx

xBBX∈

=⊕ ˆˆ ≠∩= ]|{ BBx x Ø}

The algebraic properties of the dilation and erosion dual transforms include: a) translation

invariance; distibutivity; iterativity; increasing and inclusion.

After having eroded X by B it is not possible in general to recover the initial set by dilating the

eroded set by the same structuring element B. This dilate reconstitutes only a part of X, simpler

and with less details , but may be considered as that part which is the most essential.

The successive application of erosion and dilation results in opening of X with respect to B:

XX B (= BB ⊕)ˆ

The effect of opening is that it smoothes the contours of X, cuts the narrow isthmuses, suppresses

small islands and the sharp capes of X.

The properties of opening include: antiextensive XX B ⊂ , increasing BB XXXX '' ⊂⇒⊂ ,

idempotent BBB XX =)(

The closing of X with respect to B is the result of applying successively dilation followed by

erosion:

XX B (= )B̂⊕ B

By dual interpretation with opening, the effect of closing is that it blocks up narrow channels, the

small lakes and the thin gulfs of X.

The properties of closing include - extensive: XX B ⊃ , increasing BB XXXX ⊂⇒⊂ '' ,

idempotent BBB XX =)(

Analyzing the same object X by dissimilar structuring elements (like disk or a set of points) may

result in very different pieces of information on its geometric structure. The structuring elements


38

can be chosen from a wide number of possible shapes but they must be geometrically simple,

bounded. From multiple shapes with similar properties the most extreme being: isotropic (disk)

versus anisotropic (segments) for studies of orientation, convex (disk or segment like) for size

distribution versus nonconvex (circle, boundary of the segment or clusters of points) for set

covariance, infinitesimal iterations.

3.1.5 White matter – gray matter separation

The segmentation in different classes has applications in image-guided surgery and neuroscience.

In image-guided surgery, 3D visualization are created by rendering 3D models of the brain with

overlays of tumors for visualization in pre- and intraoperative surgical planning.

The analysis of MRI of the human brain often involves white matter (WM) segmentation.

In the functional brain mapping the surface of interest may be the WM/CSF interface, the

WM/GM interface or run midway through the white matter (WM/GM). One of the reasons for

preferring the GM/WM surface display is that the folding of the cortical sheet obstructs the view

of activations buried within sulci if the GM-CSF interface is used for visualization. The

visualization of the WM-GM interface allows a better view of the activations in the sulci. A

second reason relates to the higher accuracy of the white matter-gray matter boundary surface

segmentation, as compared to the gray matter- CSF boundary [Dale et al., 1999].

The quantification of cerebral volume is important for assessing brain development and/or changes

in the normal brain or in pathological states [Iwasaki, 1997]. The segmented volumes of white and

gray matter (GM) in different parts of the brain are measured and compared in order to develop

hypotheses about various disorders.

For example in studies of ageing was observed a decline of global gray matter with age, involving

cortical and deep gray matter structures and cerebellum diffusively, but not significantly for white

matter [Good et al., 2001]. Separating gray matter from white matter is particularly important if

one considers that gray matter reduction is relevant in schizophrenia [Lawrie and Abukmeil, 1998;

McCarley et al. 1999] and Alzheimer’s disease [Jack et al., 1997], while myelination is an

indicator of brain maturation [Giedd et al 1999; Pauss et al., 1999]. Myelination can also serve as a

marker showing if brain abnormalities are related to the neuronal body (nonmyleinated: gray

matter) or to the axons (often myelinated: white matter).


39

Significant correlations between brain volume and other putative MR neuronal markers indicate

that atrophy reflects axonal loss in multiple sclerosis. There has been considerable interest in

measuring tissue loss (atrophy) as a global marker of multiple sclerosis outcome.

Brain white matter bulk consists predominantly of axons (46%) followed by myelin (24%), and

progressive atrophy implies loss of these structures, especially axons, although variable effects on

tissue volumes may also arise from glial cell proliferation or loss, gliosis, inflammation and edema

[Miller et al., 2002].

Intensity-based segmentation of the GM, WM compartments is challenged by the complex

geometry of the gray matter-white matter interface and by the fact that a substantial part of the

voxels at the interface sample both white and gray matter (the partial volume effect).

The direct use of global thresholding is hardly suitable because of the presence of intensity

inhomogeneities. These may be caused by non uniformities in the RF field during acquisition as

well as other factors. For a detailed explanation see [Webb, 1988; Bushberg et al., 1994]. The

result is a shading effect or a bias field where the pixel or voxel intensities of the same tissue class

vary slowly over the image domain.

Several methods have been described to separate and estimate the white matter and gray matter

volumes. Some of these methods employ histogram analysis and assume that the histogram is

composed of a gray matter and white matter distribution. One method to separate the two

distributions is to find the best separation threshold in terms of variances within and between the

two distributions [Momenan et al., 1997; Otsu, 1979]. Other methods [Kennedy et al., 1979;

Wieringa, 1993] use as threshold the position of the local minima between the two distributions as

threshold. Similarly the Utrecht method [Schnack et al., 2001] uses shape information for the two

histogram peaks to find one single threshold for separating the white matter from gray matter. The

intensity threshold is found from the crossing of the tangential lines to the steepest slope points of

the polynomials fitted to the wm and the gm peaks, multiplied by a scaling factor obtained from

calibration. The calibration factor is used to compensate for noise and artifact influences. A

prerequisite of their technique is the intensity nonuniformity correction. Schnack’s method as well

as the nadir algorithm [Filipek et al., 1994, Kennedy et al. 1989] and Otsu’s algortihm [Otsu,

1979] start from an initial total brain segmentation. Otsu’s algorithm is a variance optimization

method, comparable to c-means which allows the separation into more than two classes.

Although most brain segmentation methods do not take into account the scanner nonuniformity

various methods have been developed to correct for the introduced bias [Wells et al., 1996;


40

VanLeemput et al., 1999] and some combine the segmentation with the bias field correction

[Kapur et al., 1996; Zhang et al., 2001].

It has been shown that intensity inhomogeneities are well modeled by the product of the original

image and a smooth slowly varying multiplier field. Pham and Prince [Pham and Prince, 1999]

have analyzed the performance of a classification algorithm based on fuzzy c means (FCM) in the

case where the bias field is scalar and vector field and concluded that the assumption of a scalar

field gives results similar to the vector field assumption while involving simpler and faster

computation. Another method for labeling the white matter and gray matter relying on fuzzy c

means and compensating meanwhile for the bias field is the one reported by Ahmed and others

[Ahmed et al., 2002]. They modify the objective function to be minimized in the FCM method in

such a way as to include the effect of the neighbors’s labeling. This approach should thus be

suitable for cases where the images are corrupted by noise.

3.2 A mathematical morphology based method for brain segmentation

We use a semi-interactive, region based approach, based on mathematical morphology for

extracting the entire brain. Our approach is similar to the ones described in [Höhne and Hanson,

1992; Kapur at al., 1996], and is shown schematically in Figure 3.2-1.

The method relies on the fact that the intensities at each spatial location of the MRI data can serve

for identifying tissue clusters and selected anatomical structures. The intensity information

however does not suffice in many cases because different tissues may have similar MR

characteristics and because of image acquisition artifacts like noise, RF inhomogeneity, partial

volume effects, etc.

The procedure starts by pre-processing the MRI i.e. by applying a filter for a noise reduction

purpose. We use median filtering or anisotropic diffusion filtering. For cases where the

inhomogeneity field is introducing major problems to the segmentation process the anisotropic

diffusion filter can be applied but this leads to a significant increase in the total time required for

segmentation. Practically the segmentation starts with interactive thresholding, using values

suggested from the smoothed histogram. All these factors introduce errors in the initial

binarization into brain and non-brain voxels. To overcome this problem, we use mathematical

morphology to isolate the largest connected component classified initially as brain, by cutting its

connection to non-brain structures.


41

Figure 3.2-1.Flowchart of the brain segmentation process done by the CORTSEG module

The core of the method consists in a succession of morphological operation starting from the

thresholded image, as shown in Figure 3.2-2.

Thin connections between regions with similar intensity values (bridging veins from the cerebral

cortex to dura, from dura to the skull, the optic nerves, etc.) are cut using erosion with a ball like

structuring element of radius 1. Interactive selection of the number of erosions is allowed. The

next step of the segmentation process is a region growing operation. The region growing starts

from a seed selected by the user, by mouse clicking on a voxel within the eroded mask. The

algorithm adds to the initial region all voxels connected to it and whose values are between the

selected thresholds. Dilation with a ball like structuring element is applied to restore the

boundaries of the brain, distorted in the erosion step. The dilation is conditioned on the initially

assigned brain labels, through thresholding, aiming that no boundaries are actually expanded. The

number of dilations is equal to the number of erosions. Closing is applied in the end to fill in small

holes, i.e. voxels that are not part of the brain but are surrounded by voxels who are.

Preprocessing

Erosion

Seed planting & region growing

Thresholding

Closing

Conditional Dilation

Surface rendering

MRI

Y

N OK?


42

a) original b) thresholded c) eroded

d) region grown e) dilated f) closed

Figure 3.2-2. The stages of the morphology based segmentation: a) original MR slice, b) the voxels in between

the selected thresholds are shown in pink, overlaid on the original MR slice (in gray) , c) in the eroded image

(shown in pink) the brain (B) and non brain compartments (nB) are disconnected through severing thin

connections (C), d) the region grown from a seed is in pink retrieves the brain compartment, e) the dilated

image restores the original size, f) the closed image fills in little holes (h) (voxels which are classified as brain

surrounding voxels classified as background)

The results at any stage of the segmentation process can be visualized and evaluated qualitatively

by varying the degree of transparency of the segmented slice overlaid on the original MRI slice

[Figure 3.2-3]. The segmented volume can be shown in varying color scales while the original

MRI is always shown in gray scale.

The output is either a binary volume with pixels classified as brain or non-brain or a volume

containing the gray value information for the brain volume.

B

nB

C

h


43

The methodology is implemented into the Cortical Segmentation (CORTSEG) software module,

whose interface is shown in Figure 3.2-3.

Figure 3.2-3. Cortseg interface

The segmentation result is visualized using the VISIO module, presented in Chapter 5.

Figure 3.2-4.Rendering of segmented brain

If unsatisfactory, the segmented brain can be edited and refined with STRUCTSEG, presented in

Chapter 4.


44

3.3 A modified fuzzy c means method for white matter-gray matter

separation

A new step in the segmentation procedure is necessary for separating the segmented brain into

classes: white matter, gray matter and CSF starting from an eventually noisy, inhomogenously

“illuminated” MRI scan.

Inhomogeneity artifacts in the MRI, often disregarded by segmentation protocols, can spoil the

quality of the segmentation. To avoid this problem, we use a modified fuzzy c-means method

incorporating neighborhood information for labeling a voxel and simultaneously correcting for the

bias field. The first order neighbors are considered only.

The fuzzy c means clusters data by interactively computing a mean intensity for each class and

segmenting the image by classifying each voxel in the class with the closest mean. The fuzzy

partition obtained is in the end defuzzified using the maximum membership conversion.

The MRI data are first segmented into brain and background interactively, based on thresholding

and applying a succession of mathematical morphology operators: erosion, region growing,

dilation and closing.

We assume that the gray matter is a sheet of approximately constant thickness and we proceed to

iteratively erode the initially segmented brain. From the statistical properties of the gray values of

the eroded and remaining voxels, we infer the optimal number of erosions and the initial mean

value for the WM and the GM clusters [Figure 3.3-1].

Figure 3.3-1: The intensity values of the eroded voxels (considered to be gray matter GM) increases with the

number of erosions, reaching a constant value, meanswhen no more gray matter has been left to be eroded.:.

This values is assigned to the white matter (WM) mean.


45

After a number of erosions the mean value of the voxels does not change too much, indicating that

only WM is left.

Assuming that the MRI recorded signal is the product of the true signal, the one generated by the

tissue, and the spatially varying gain field:

kkk GXY = , k=1, …N

Yk is the observed and Xk the true signal intensity and Gk the gain field, measured at voxel k. N is

the total number of voxels.

Applying the log transform allows the artifact to be modeled as an additive field [Wells et al.,

1996.]

kkk xy β+= Equation 1

where yk and xk are the true and observed log transformed intensities at the k-th vozel. βk is the

bias field at voxel k.

The standard FCM objective function for partitioning a set a set { }Nkkx 1= into c clusters is:

2

1 1ik

c

iik

pN

kvxuJ −=∑∑

= =

Where: c is the number of classes; N is the number of voxels; { }ciiv 1= are the prototypes of the

clusters and the partition matrix U=[uik] ∈ U must satisfy:

∀<<∀=∈ ∑∑==

N

kik

c

iikik iNuandkuu

11,0:,,1]1,0[ and p is a weighting factor describing the

amount of fuzziness of the resulting classification.

The modified objective function accounts for the neighborhood effect (the labeling of a voxel is

influenced by the labels in its immediate neighborhood) as well as for the bias field [Ahmed et al.,

2002].

−+−= ∑∑∑∑∑

Ν⊂= == = krxir

c

iik

pN

kRik

c

iik

pN

km vxu

NvxuJ 2

1 1

2

1 1

α

Nk is the set of neighbors in a window around xk and NR is the number of neighbors.

α is a parameter which controls the effect of the neighbors, a higher value being used for low SNR

images.

Substituting the kkk xy β+= Equation 1


46

−−+−= ∑∑∑∑∑

Ν⊂= == = kryirr

c

iik

pN

kRik

c

iik

pN

km vyu

NvxuJ 2

1 1

2

1 1βα

The optimization problem can be formulated as:

{ } { } mviU

JNkk

ci 11 ,,

min== β

, subject to U∈U

The zero gradient condition involved derivation of Jm with respect to uik, vi, and βk and yields three

necessary conditions for Jm to be a local minimum.

A Lagrange multiplier is used for solving the constrained optimization in equation. 1

−+

+= ∑∑∑

== =

c

iik

c

i

N

kiik

p

Rikik

pm uu

NDuF

11 11λγα

Where 2ikkik vyD −−= β and ∑

Ν⊂

−−=kry

irri vy 2βγ

We put the conditions that the derivatives of Fm with respect to uik, vi, and βk are null:

;0=∂∂

ik

m

uF ;0=

∂∂

i

m

vF ;0=

∂∂

k

mFβ

and knowing that the sum of all partitions at a point is 1: kuc

jjk∑

=

∀=1

,1 we obtain the values for

the partition matrix, the cluster centers and the bias field:

11

12

2

* 1

−

=

⊂

⊂∑∑

∑

−−+

−−+

=p

c

j

Nxjrr

Rjk

Nxirr

Rik

ik

kr

i

kr

vxN

D

vxN

D

u

βα

βα Equation 2.

( ) ( )

∑

∑∑

=

⊂=

+

−+−

= N

kik

p

Nxrr

Rkkik

pN

ki

u

yN

yuv kr

1

1*

)1( α

βαβEquation 3.

∑

∑

=

=−= c

iik

p

c

iiik

p

kk

u

vuy

1

1*β Equation 4.


47

The algorithm for segmenting the images into classes while correcting for the bias field can be

summarized as follows:

1. Select the initial class prototypes { }ciiv 1= and initialize the bias field { }N

kk 1=β with small and equal

values, for example 0.01.

2. Update the partition matrix using 1

1

12

2

* 1

−

=

⊂

⊂∑∑

∑

−−+

−−+

=p

c

j

Nxjrr

Rjk

Nxirr

Rik

ik

kr

i

kr

vxN

D

vxN

D

u

βα

βα Equation 2

3. Calculate the new clusters centers using ( ) ( )

∑

∑∑

=

⊂=

+

−+−

= N

kik

p

Nxrr

Rkkik

pN

ki

u

yN

yuv kr

1

1*

)1( α

βαβ

Equation

3.

4. Estimate the bias field using∑

∑

=

=−= c

iik

p

c

iiik

p

kk

u

vuy

1

1*β

Equation

4.

Steps 2) to 4) are repeated till ε<− oldnew VV , where V is the vector of cluster centers and ε is a

small number, for example 0.01.

We use α between 0.7 and 0.85, p =2 and Nr =9 for 2D and 27 for the 3D case.

The segmentation accuracy can be measured as: a) 100* Number of correctly classified

voxels/number of voxels in the class or b) using the Dice coefficient, defined as (2*Volume of

voxels assigned to class k by both the ground truth and the algorithm)/(volume of voxels assigned

o k by algorithm + volume of voxels assigned to k by ground truth) - this coefficient approaches 1

if both segmentations are in agreement and 0 if there is no overlap.

3.4 Geometrical, differential properties of the cortex

The cortical surface is an important feature of the brain, but its precise folding, geometry and

variability are little understood. Once the cortex has been extracted it can be represented as a set of

surface patches. We aim to find a parametric representation and infer the local geometrical

properties and differential properties. A precise mathematical representation of a typical cortical

surface can be done by means of a family of local quadratic [Joshi et al., 1995]. The local


48

structural information relevant to shape is embodied in the principal curvature, normal and

principal direction fields over the surface.

Differential geometry provides a natural mathematical framework to study the cortical surface

geometry. Among many geometric quantities defined for the surface, curvature information is

valuable since it quantifies the structure of the sulci and gyri, and provides the basis for

comparative studies.

This representation allows to make statements about the intrinsic geometry of the surface as well

as to map one surface geometry onto another or onto an atlas based surface and thus to study the

variability of the cortical surface in an ensemble of brain data. These features are thus relevant for

spatial transformation and registration of brain images using for example elastically deformable

models [Davatzikos, 1997].

The digital and mathematical representation allows the precise tracing of sulci or/and gyri onto the

cortical surface, based on the sign of the mean curvature. This is important for parcellating the

cortex in distinct entities and helps understanding the structure and functionality of these areas.

The segmented 3D object can be represented as a set of voxels, or points, and the isosurface

delimiting the boundary with the background as a set of surface patches. We are interested in

finding surfaces defined in the neighborhood of one of their points (surfaces in the small).

We define surfaces to be manifolds supported by local coordinate systems with at least two

derivatives allowing for the generation of curvature maps. We fit quadratic functions to the

elementary patches obtained from triangulation. Practically we want to estimate the parameters

that minimize the mean square distance from the data points to the surface defined by those

parameters.

Consider a surface M. This can be defined as the set of zeros of a smooth function of 3 variables x,

y, and z : f(x, y, z)=0 . It can also be defined parametrically as:

S={(x, y, z): x=d(u,v), y=e(u, v), z=f(u,v), u, v belong to D in R2 }

Or in a less general form, equivalent to the graph surface (or Monge patch):

S={(x, y, z): x=u, y=v, z=f(u,v), u, v belong to D in R2 }

The points on the graph that are directly connected to a vertex i. form the neighborhood N(i). For

each point i the surface(M) is expressed as the graph of the function z(x, y) of f (x, y).

We determine using the triangulated 3D volume, the mesh vertices and polygons connectivity, and

compute the mesh normals for the set of polygons.


49

We determine the neighborhood of each vertex xi and the tangent plane at the vertex point i. and

the tangent plane. Each point in the tangent plane, x satisfies: ( ) 0=−• ii xxn rrr

We can define an orthonormal coordinate system with xi as origin and choose the basis vectors in

the tangent plane:

Where: ( )zyx nnnn ,,=r

We have to express coordinates of the vertex i and of its neighbors in the new system:

coordinates transformed= (coordinates –fiducial)*(transforormation matrix)T

We store the data relative to the patches: fiducial, neighbors, vertices (including fiducial and

neighbors), normal, transformation matrix (the new vector basis).

In a neighborhood of the fiducial point, the surface can be given in the form:

S=(u, v, f(u, v))

The coordinate vectors: ),1,0(),0,1(vfXv

ufXu

∂∂

=∂∂

= are linearly independent, thus the

representation z=f(u, v) yields a regular surface as long as f(u, v) has a few continuous derivatives.

We fit the surface with a quadric: 2)2()1(

2)0(),( vguvgugz vu ++=

The tangent plane is determined by Xu and Xv .

The normal vector X3: 2)(

3XvXuXvXuX

×

×=

In the fiducial point (0, 0, 0): )0,0()1()2()0,0()1()0( )2,1,0(;)2,0,1( ugvgXvvgugXu +=+=

Let s be the length of the regular curve X(t)=X(u(t), v(t))

The first fundamental form (given by the inner product of the tangent vectors) determines the arc

length of a curve on a surface and satisfies: 22

2

++

=

dtdvG

dtdv

dtduF

dtduE

dtds

121 bnbandaab i ×=⟩⟨

=

≠+−

≠+−

≠+−

=

0,)),(,,(1

0,)),(,(1

0,),),((1

zT

zxzzz

yT

yzxyy

xT

xxzyx

nifnnnnn

nifnnnnn

nifnnnnn

a


50

Where: vvvvvuuvuuuu XXXGXXXFXXXE ======

The second fundamental form is given by: 22 2 NvMuvLuII ++=

Where: 333 XXNXXMXXL vvuvuu ===

The normal curvature in a direction vp=axu+bxv is given by 22

22

22)(

GbFabEaNbMabLavpk

++++

= .

The maximum and minimum values of the normal curvature at a point on a regular surface are

called the principal curvatures (k1, and k2). The principal curvatures and principal directions are

obtained from the solutions of:

The Gaussian (K) and Mean (H) curvatures are obtained after expanding the determinant above:

It results that the principal curvatures are given by KHHk −±= 22,1

Figure 3.4-1Brain rendered based on the mean curvature: note that the sulci have low values (black in the

figure) of the mean (Subject A.I.)

0det =−−−−

kGNkFMkFMkEL

21

)21(212

21

2

2

2

kkFEGMLNK

kkFEG

GLFMENH

=−−

=

+=−

+−=

a) b)


51

3.5 Results

3.1.6 Whole Brain Segmentation

A software module, CORTESG, has been created and has been used to segment the cerebrum out

of MRI scans from various machines and, for verification purposes, a computational phantom,

with various degrees of noise and inhomogeneity. We use the computational phantom from the

McConnell Brain Imaging Center at the Montreal Neurological Institute, McGill University,

Canada [Collins et al., 1998; www.bic.mni.mcgill.ca/brainweb. The phantom is available with

varying pixel size, levels of noise and bias field for T1, T2 or PD images.

We measured the segmentation accuracy for the T1 phantom with isotropic voxels of 1 mm size.

The noise levels were of 3, 5, and 9% and the inhomogeneity field of 20% or 40% and we

obtained values above 97% in all cases.

Our actual MRI-s are used for visualization purposes in the context of studying the spatio-temporal

pattern of activations for evoked fields (MEG) or potentials (EEG). In these cases the evaluation of

the segmentation result is done qualitatively, by visual inspection of the segmented surface.

The segmentation can be evaluated qualitatively by varying the transparency of the segmented

slice, seen in juxtaposition with the original MR slice. For a quantitative check we have used the

simulated brain phantom from the McConnell Brain Imaging Center with T1 data. The

segmentation accuracy, measured as 100 by the ratio between the correctly identified voxels and

the total number of brain voxels, is 98.6% for 3% noise and 20% inhomogeneity, and 98.0% for

9% noise and 40% inhomogeneity.

The speed performance of the software modules was evaluated mainly on a PC, 1700 MHz AMD

Athlon, 512 MB Ram, under Windows 2000 operating system. The morphology based brain

segmentation from T1 weighted MRIs of 256x256x256 pixels takes approximately 20 minutes for

a trained user. An exception is when anisotropic diffusion filtering is used, since this adds

considerable more time, increasing the time to about 1 hour.

An example of whole brain segmentation is given in Figure 3.5-1A, which shows a slice of the

segmented volume superimposed on the MR scan. The 3D rendering of the segmented brain is

shown in Figure 3.5-1B and C. The transparent rendering of the brain surface allows to asses the

location and shape of selected brain structures or surface parts of the cortex and an example is

shown in Figure 3.5-1B for the hippocampus-amygdala complex. It is sometimes useful to

visualize in the background of the cortex the location of landmark features as the central sulcus


52

outline in Figure 3.5-1C. The SAV program allows the delineation of important landmarks on the

3D brain representation, by drawing in 3D (in VISIO as was the case for the central sulcus outline)

or by fast marking the landmarks in subsequent slices (in combination with STRUCTSEG).

Figure 3.5-1. Segmentation examples: a) whole cortex segmentation, visual evaluation. b) a combination of

brain segmentation and subcortical structures reveals information abut the shape and location of the structure,

here the hippocampus; d) the central sulcus as an outline over the cortex. Subject J.R.

SAV has been designed to allow the removal of segmented areas so that parts of the brain which

are normally covered by other structures can be visualized. For example, the cerebellum was

removed for activation studies which required the ventral part of the brain, or the adjacent part of

the brain stem to be exposed.

SAV allows volumetric calculations to be performed. Evaluation of the brain volume gave results

ranging from 815 to 1200, 1500 cm3 (the ventricles are not taken into account).

3.6.1 Gray matter- white matter separation with bias field compensation

We apply the procedure based on FCM for separating the white and gray matter compartments of

the brain to real and phantom MRI data. Figure 3.5-2 shows examples of white-gray matter

segmentation derived from T1 weighted anatomical MRI scans for one of our MRI data sets and

for the Montreal computational phantom.

d c

b a

C


53

Figure 3.5-2. Examples of segmented slices for a phantom (a, c) and a real MRI data set (b, d) (subject J.R.)

Examples of 3D segmentations for a real MRI and a set of functional data corresponding to left

arm stimulation of the median nerve at the wrist are shown if Figure 3.5-3. The hand area of SI is

activated. The latency is 31 ms and the threshold used is 25%. For ease of orientation an outline of

the central sulcus is overlaid on the segmented MRI.

Figure 3.5-3. 3D rendering of the gray matter surface and (a) the white matter surface (b). Activation maps

elicited by left arm stimulation of the median nerve at the wrist, 31 ms after stimulation. In pink the outline of

the central sulcus (subject J.R.).

The WM and GM volumes are calculated counting the number of voxels belonging to each class

and multiplying these values by the voxel size, since no gap exists between the slices. The

segmentation is evaluated by comparing the results of the present method on the Montreal brain

phantom. The segmentation accuracy has been evaluated against the computational phantom data

as the ratio between the number of correctly classified voxels and the number of voxels in the

class, multiplied by 100, and we found the accuracy to be in the range of 70%.

3.6 Discussion and Conclusion

Surface based analysis has demonstrated potential for enhancing progress in understanding cortical

structure, function and development in normal and pathological conditions. Accurate


54

representations and mapping algorithms are therefore needed for the analysis of high resolution

data.

Depending on the application one or more parameters are prevailing in the evaluation (or choice)

of the segmentation method. Often the evaluation is based on accuracy, efficiency and

repeatability. Accuracy is most important for quantitative measurements of brain structures while

efficiency is most important for visualization purposes. While the efficiency can be easily and

objectively measured the evaluation of accuracy and repeatability are more a subjective matter

since there is a lack of “golden truth”/ or ground truth. Alternatively the manual segmentation can

be used as ground truth or a simulation by means of brain phantoms. The manual segmentation

approach is extremely time consuming due to the large size of the data set and the complexity of

the white matter -gray matter surface, requires a long training period and may have limited

reproducibility. However it is the manual contouring which is often used as the ground truth for

such segmentation. Alternatively one can employ computational phantoms but these phantoms

offer a limited number of conditions and although very important for validation purposes may not

span the whole range of problems one can meet in reality with a particular MR scan.

We have adopted a relatively fast, interactive segmentation approach, which enables to

successfully segment the brain surface from MRI, using mathematical morphology.

The first attempts at segmentation were done in 2D and sometimes required the use of multiple

seeds for region growing. The 3D approach is superior in that it requires only one seed for the

whole volume and therefore less interaction from the user and the connectivity analysis is done in

3D.

Figure 3.6-1. Effect of using multiple seeds for the region growing in the 2D segmentation approaches. The left

figure shows the eroded image in yellow and the thresholded image in blue. The center image is the result of

using one seed for starting the region growing. The use of a second seed was necessary, as shown at the right.


55

The same method worked reasonably well for the segmentation of the lateral ventricle since it

constitutes a region with distinct gray values, CSF being much darker than the surrounded brain

tissue. For other structures however a special procedure needed to be developed, and will be

detailed in Chapter 4.

While this method appears suitable for the whole brain segmentation it may not suffice in cases

where the separation of white matter and gray matter is needed.

The GM sheet is a thin (~2mm) and convoluted sheet and therefore its segmentation is likely to be

affected by partial volume effects. Noise and inhomogeneity artifacts add to the complexity of the

MR segmentation problem. This may not be so much import when segmenting the whole

cerebrum and the results from a morphology based segmentation techniques proved to be

satisfactory in general. Further refinements could be done in difficult cases using the editing

possibilities of STRUCTSEG. However for separating the gray matter and white matter

compartments the partial volume effects and the inhomogeneities of the field become important.

We attempt to overcome these using a modified FCM algorithm which incorporates neighborhood

analysis and estimates the bias field. The neighborhood effect achieves regularization, useful for

segmenting noise corrupted images.

The initial choice of the parameters for FCM affects the convergence and the accuracy of the

algorithm. We propose to select these parameters from the analysis of voxels left after eroding the

brain segmented with mathematical morphology. These features add to the functionality of our

surface activation visualization software.

The integration of segmentation and visualization modules within the same software environment,

offers new opportunities, in addition to the direct estimation of the cortical thickness. For example,

the results of tomographic analysis can be either constrained to the identified gray matter domain,

or the bands of maxima in such tomographic solutions can be contrasted to the gray matter

topology.

Chapter 4. Brain Structure Segmentation

56

Chapter 4. Segmentation of Individual Brain Structures

Chapter 4. Segmentation of Individual Brain Structures ........................................................... 56

4.1 Introduction ................................................................................................................ 56

4.1.1 Motivation for segmentation .................................................................................. 56

4.1.2 Selected structures of interest................................................................................. 58

4.1.3 Methods for subcortical structure segmentation .................................................... 63

4.1.4 Background on active contours segmentation........................................................ 64

4.2 Methods...................................................................................................................... 66

4.2.1 Manual and snake based segmentation .................................................................. 67

4.2.2 Hippocampus Segmentation................................................................................... 69

4.2.3 Amygdala Segmentation ........................................................................................ 71

4.2.4 Central sulcus segmentation................................................................................... 73

4.2.5 Thalamus segmentation.......................................................................................... 75

4.2.6 Brain stem segmentation ........................................................................................ 77

4.3 Results ........................................................................................................................ 78

4.4 Discussion and conclusion ......................................................................................... 80

4.1 Introduction

4.1.1 Motivation for segmentation

Brain structure segmentation is essential for quantitative MRI analysis, i.e. studies on the

anatomy of specific brain areas or nuclei and provides information on their normal appearance

as well as on the onset and progress of certain neurological diseases (dementia, schizophrenia,

etc.). Traditional measures refer to cross section, area, length, or volume of a structure. For

bilateral structures an asymmetry index can be computed and this proved useful for the study

of hippocampus in epilepsy [Wang at al., 2001]. Flattening is a computational technique [Engel

et al., 1997 and http://white.stanford.edu/~brian/mri/segmentUnfold.htm, Fischl et al., 1999;

Hurdal et al., 1999; Van Essen and Drury, 1997] which can be used for comparing structures.

Small volumetric changes or regionally specific changes in a tissue compartment can be

assessed by a voxel by voxel comparison of gray matter density [Wright et al., 1995], image

intensity [Andreasen et al., 1994], or probability of a segmented structure [Paus et al., 1996;

Penhune et al., 1996]. Modern approaches address the shape information and statistical


57

measures. These can be obtained through high dimensional transformations from one brain to

another or to a brain atlas, and may incorporate information on the normal brain variability.

Deformation based morphometry aims to characterize spatial modes of anatomical variability

globally or to identify regionally specific differences in a deformation field which warps one

brain onto another.

The quantitative analysis of MRI data is important in: a) functional brain mapping [Dhawan et

al., 1992], b) assessing structural brain abnormalities [Shenton et al., 1992], c) computer-

assisted neurosurgery [Kikins, 1996] but also d) investigating the relationship between brain

structures and memory processes, emotion and personality [Sullivan et al., 1995; Mori et al.,

1999; Mori et al., 1997] and their evolution with aging.

In functional neuroimaging, exact localization is crucial for the correct interpretation of focal

activations with respect to specific functions [Pruesner et al., 2002]. High precision in

measurement is required due to subtle volumetric differences between brain regions in patients

and in normal control subjects. MRI volumetry is an established research tool in the

investigation of the relations among brain structures to the onset and time course of pathologies

like carcinoma, multiple sclerosis, schizophrenia, dementia, epilepsy, depression or

Alzheimer’s disease. Some of these brain disorders involve volume changes in several brain

regions simultaneously and an ideal study should be able to address all those changes in the

same patient population. Besides volume or area measurements shape analysis is emerging as a

powerful morphometric tool, as exemplified by the work of Shenton and colleagues [Shenton

et al., 2002].

A few applications of quantitative MRI analyses are in:

a) schizophrenia: Some of the robust MR findings regarding abnormal structures include:

enlarged lateral ventricles, reduction of the medial temporal lobe volume (amygdala–

hippocampal complex and/or parahippocampal gyrus), and reduction of gray matter volume of

the superior temporal gyrus [Shenton et al., 2002]. The findings reported in a recent paper

[Lawrie et al., 2002] suggest that even persons with high risk for schizophrenia may exhibit

reductions in temporal lobe volumes.

Shape analysis studies indicate abnormalities of the amygdalo–hippocampal complex in

schizophrenia and an increased left/right asymmetry (R>L) in volume and shape [Wang et al.,

2001]. Shenton and colleagues [Shenton et al., 2002] suggest that some of the structural

abnormalities are neurodevelopmental in origin. They localize brain regions responsible for the

left/right asymmetry differences in patients in the tail of the hippocampus and in portions of the

amygdala.


58

b) temporal lobe epilepsy (TLE): The temporal lobe structures have been the main subject of

morphological studies in TLE. The mophometric measurements address the hippocampus

[Webb et al., 1999], amygdala or the amygdala–hippocampal complex [Watson et al., 1997]

but also the thalamus [Kim et al., 2002]. Intensity abnormalities, correlated with gliosis, have

been noticed in the MR scans in these areas. The lesions are noticeable because of a subtle

decrease in T1 intensity and an increase in T2 intensities (correlated with increased water

content). The glucose metabolism appears decreased in these areas in TLE [Kim et al., 2002].

c) Alzheimer’s disease Amygdala atrophy appears more prominent in advanced stages of

Alzheimer’s disease [Tsuchiya and Kosaka, 1990]. More intense neuronal loss occurs in the

corticomedial nuclear group than in the basolateral group. Volumetric measurements of the

amygdala and the amygdalohippocampal complex appear more accurate than those of the

hippocampal formation alone in distinguishing patients with Alzheimer disease in the early-

stages [Lehericy et al., 1994; Watson et al., 1997; Lawrie and Abukmeil, 1998].

4.1.2 Selected structures of interest

Of importance in the morphometric studies are the structures of the temporal lobe. In particular

the hippocampus plays an important role in the pathogenesis of temporal lobe seizures [see

Webb et al., 2000 and references therein]. We have concentrated on a number of brain

structures including the hippocampus, amygdala and the hippocampal-amygdalar complex, the

thalamus and the ventricles (the lateral ventricles). We have been also interested in studying

functional aspects where the central sulcus, the postcentral gyrus, the primary visual cortex, the

brain stem, or cerebellum play significant roles.

4.1.1.1 The hippocampus

The hippocampus (HH) is a horseshoe shaped region of the subcortical brain, located in the

medial temporal lobe, inferior to the choroidal fissure and the temporal horn of the lateral

ventricle. It is part of the limbic system, which includes: the cyngulate gyrus, hippocampal

formation, septal nuclei, and amygdala; and also the mammilary nuclei and the anterior

thalamic nucleus, according to Brodal [Brodal, 1992].

Deviations from normal morphology of the hippocampus may be related to temporal lobe

epilepsy, medial temporal sclerosis, schizophrenia, alcoholism or psychosis. For example MR

findings in studies of medial temporal sclerosis (MTS) indicate hippocampal atrophy and

increased signal intensity in T2 weighted images (increased tissue free water, gliosis).

Comparative hippocampal volumetry provides means to quantify these changes. Zeineh and

colleagues [Zeineh et al., 2000], segment and unfold the hippocampus, allowing the


59

demarcation of the fusiform, parahippocampal, perirhinal, entorhinal, subicular, and CA fields

to be viewed and compared across subjects.

The visualization of electrophysiological data localizing deep bioelectromagnetic sources can

give insight into the role of the hippocampal structure, believed to be involved in the formation

of spatial memory and other kinds of memory [Squire, 1992].

In the sagittal plane the hippocampus is divided into three parts: head, body, and tail. The more

anterior head, called the pes hippocampus, is marked by digitations [Figure 4.1-1a]. The body

is more cylindrical in shape, and the tail tapers posteriorly.

The gray matter of the hippocampus is an extension of the subiculum of the parahippocampal

gyrus. In coronal plane the hippocampus and parahippocampal gyrus form an S-shaped

configuration. The hippocampus itself consists of two interlocking C-shaped structures: the

cornu ammonis and the dentate gyrus. Histologically, the cornu ammonis is further divided into

four sections: CA1 to CA4 [Figure 4.1-1].

The alveus and fimbria, white matter tracts along the superior surface of the hippocampus,

continue posteriorly as the fornix and serve as major efferent pathways to the rest of the brain.

Figure 4.1-1 A diagram of the hippocampus and a coronal cross section (adapted from [Hui et al., 1997]). A.

Medial view of the hippocampus showing the head, body, and tail sitting on the parahippocampal gyrus. 1:

intralimbic gyrus; 2: band of Giacomini; 3: uncus; 4: fimbria (a band of white matter sweeping posteriorly

to form the fornix); 5: margo denticulatus (a row of small bumps in the hippocampal body which

represents the visible portion of the dentate gyrus. Anteriorly, the Margo denticulatus becomes the band of

Giacomini, which then merges with the uncus. Posteriorly the Margo denticulatis becomes the fasciola

cinerea, and the visible continuation of CA3 is known as the fasciolar gyrus); 6: fasciola cinerea; 7:

fasciolar gyrus; 8: gyri of Andreas Retzius (represent the bulge of CA1 at the tail); 9: parahippocampal

gyrus; 10: isthmus; 11: fornix. B. Coronal section of hippocampus. 1: CA1; 2: CA2; 3: CA3; 4: CA4; 5:

dentate gyrus; 6: alveus; 7: choroid plexus; 8: subiculum; 9: fimbria; 10: hippocampal sulcus. Coronal

sections of the hippocampal body show the interlocking relationship between the cornu ammonis and the

dentate gyrus. The cornu ammonis is divided into four segments, CA1 through CA4. The dentate gyrus

A B

Anterior Posterior Medial Lateral


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forms the medial U in this configuration. CA4 is located at the hilus of the dentate, whereas CA1 is

continuous with the subiculum. The hippocampal sulcus separates the dentate gyrus from the subiculum.

The alveus envelops the cornu Ammonis and forms the fimbria at the superomedial surface.

4.1.1.2 The amygdala

The amygdala (AG) is an olive shaped mass of gray matter, made of several distinct nuclei,

located in the supero-medial part of the temporal lobe, immediately anterior to the inferior horn

of the lateral ventricle. It is partly anterior and partly superior to the hippocampus and to other

gray matter structures like claustrum and the tail of the caudate.

The amygdala is composed of several distinct nuclei, including three main groups: a) the

basolateral amygdala nuclei (the lateral nucleus, the basal nucleus and the accessory basal

nucleus), b) the centromedial nuclei (the central nucleus and the medial nucleus), and c) the

central amygdaloid nucleus. The last two groups are sometimes referred to as the cortiomedial

nucleus. The amygdala receives input from the neocortex, cingulate cortex and hippocampus.

Major efferent pathways include the ventral amygdalofugal pathway and the stria terminalis,

both innervating the hypothalamus.

Figure 4.1-2 The amygdala is located in the temporal lobe. A: shows the locally zoomed area of interest, the

right temporal lobe. From B to D: the coronal slices advance from posterior-to the most anterior. B: a slice

through the posterior amygdala and its relation to hippocampuss (below) and tail of the caudate (laterally).

C: a slice through the middle part of amygdala. D: shows the anterior-most aspect of the amygdala, at the

level where the optic tract emerges from the cerebri to form the optic chiasm. Numbers in the figure refer

to: 1: Amygdala; 2: Hippocampus; 3: Temporal horn of the lateral ventricle; 4: Posterior aspect of optic

chiasm; 5: Mammillary bodies; 6: Claustrum; 7: Tail of the caudate; 8: Optic tract; 9: Entorhinal cortex;

10: Sulcus semiannularis.(from Convit et al., 1999).


61

Figure 4.1-2 shows the amygdala positioned directly anterior to the pes hippocampus and

above the tip of the temporal horn.

4.1.1.3 The central sulcus and its neighboring gyri

A major landmark among cortical feature, the central sulcus has been studied from the

anatomical and functional point of view, perhaps as much as the visual cortex.

The central sulcus, or the Rolandic sulcus (abbreviated CS or rol), delimits the boundary

between the sensory and motor cortices, as well as the boundary between the frontal and

parietal lobes. One way to locate it from a surface view is to notice that it is placed between

two parallel sulci, the precentral and the postcentral sulci, it descends from the top medial

surface of the brain laterally to the towards the sylvian sulcus. However the lower part of the

central sulcus never actually intersects the sylvian fissure, but terminates just above it [Figure

4.1-3a].

Figure 4.1-3The major sulci have been used for parcellating the brain. The central sulcus (Rolandic Sulcus)

in red. f1=the superior frontal sulcus, f2=the inferior frontal sulcus, ips= intraparietal sulcus, t1= superior

temporal, cm=callosomarginal sulcus (from [N. Tzourio et al., 1997]).

The precentral gyrus is limited posteriorly by the central sulcus and anteriorly by the precentral

sulcus. It is occupied by the motor cortex of the pyramidal tract. The precentral sulcus is

frequently composed of two parts, having an inferior segment lying more anteriorly, often

intersecting the superior frontal sulcus.

The postcentral gyrus is limited in front by the central sulcus and posteriorly by the

interparietal sulcus. It often intersects the intraparietal sulcus [Figure 4.1-3 a]. It is occupied by

the end station of the sensory tract, the somatosensory cortex.

The identification of the central sulcus often necessitates a combination of all possible cardinal

views and a side view of the brain.


62

4.1.1.4 Thalamus

The thalamus is considered a central relay station for information throughout the brain. It

processes information from all sensory modalities except olfaction and receives major afferent

connections from the cerebellum [Jones, 1985; Steriade et al., 1997). In addition, the thalamus

has major reciprocal connections with the frontal lobe [Jones, 1985; Fuster, 1997; Steriade et

al., 1997). The thalamus also relays cortical information back to the body to allow for complex

processes such as movement and speech [Jones, 1985; Crosson and Hughes, 1987; O’Leary et

al., 1994; Fuster, 1997; Steriade et al., 1997]. This connectivity has led to the suggestion that

the thalamus may be the site of “gating” or filtering sensory stimuli [Jones, 1985]. More

recently this role has been extended to include global brain integration and pacemaker roles of

the thalamus [Llinas, 2001]

Anatomically the thalamus is a paired gray matter structure, located paramedially and

consisting of a cluster of nuclei, making up about 80% of the diencephalon. Its two large lateral

portions are in general (in about 70% of cases) connected across the midline by the

intermediate mass.

It is placed medial to the internal capsule. Its medial aspect forms part of the lateral wall of the

third ventricle. It is composed of several anatomical and functional groups of nerve cells: some

groups are essential part of central sensory pathways. Some others are part of the

extrapyramidal motor system. Some groups are part of the limbic system or relay for the

association cortex.

Figure 4.1-4 Thalamus outlines in a coronal slice at the middle of the thalamus (from [Portas et al., 1998a])

4.1.1.5 Brain Stem

Located at the junction between the cerebrum and the spinal cord, the brainstem relays

information between the peripheral nerves and spinal cord to the brain and it contains most of

the cranial nerves (all but two of the twelve pairs). It consists of the midbrain, medulla

oblongata, and the pons. The brain stem has a role in: alertness, arousal, breathing, blood

pressure control, digestion, heart rate and other autonomic functions.


63

The brain stem is made up of three distinct parts, all of them containing ascending and

descending nerve tracts: a) medulla oblongata, a center for several important reflexes (heart

rate, breathing, swallowing, vomiting); b) the pons, a relay between cerebrum and cerebellum;

and host to reflex centers c) the midbrain, a visual reflex center; and part of the auditory

pathway.

The medulla oblongata is approximately 3 cm long, located at the most inferior portion of the

brain stem and is continuous inferiorly with the spinal cord. On its anterior side are two

prominent enlargements, called pyramids because they are broader near the pons and taper

towards the spinal cord, extending the length of the medulla. The pyramids consist of

descending nerve tracts involved in the conscious control of skeletal muscles. An important

feature is that near their inferior ends the descending nerve tracts cross to the opposite side, or

decussate.

Two rounded, oval structures called olives, protrude from the anterior surface of the medulla

oblongata just lateral to the superior margins of the pyramids. The olives consist of nuclei

involved in functions such as balance, coordination and modulation of sound impulses from the

inner ear. The following nuclei of cranial nerves: ix (glossopharyngeal), x (vagus), xi

(accessory), and xii (hypoglossal) are also located in the medulla.

Functionally the medulla oblongata acts as a conduction pathway for both ascending and

descending nerve tracts. Various medullary nuclei also function as centers for several reflexes

(regulation of heart rate, blood vessel diameter, breathing, swallowing, vomiting, coughing,

and sneezing).

4.1.3 Methods for subcortical structure segmentation

Three classes of approaches have been proposed to segment brain structures: manual, fully

automatic or semiautomatic.

While most clinical applications use manual methods, semiautomatic as well as automatic

segmentation methods have been reported in the literature [Duchesne et al., 2002; Joshi et al.,

2002; Dawant et al., 1999; Kelemen et al., 1999; Wang et al., 2002]. In the manual method the

rater identifies and labels the structure of interest in subsequent slices in order to reconstruct it

in 3D. The 3D reconstruction is achieved by stacking contours traced in subsequent slices

[Filippi et al., 1998].

The T1 images are generally used as basis for brain structure segmentation [Pucci et al., 1998],

but also the potential of T2 and PD images is investigated. A recent study [Spinks et al., 2002]

makes use of multispectral images and report that the performance of (thalamus) segmentation

is enhanced through the combined analysis of the three types of images.


64

The model based methods may use surface tessellation and map the surface model to the given

brain structure surface. The models or templates can be constructed from atlases [Haller et al.,

1997] or from a single subject manual segmentation [Shen et al., 2002] or the mean in a

population. The models are positioned close to the structure’s position in the actual MRI and

allowed to deform, to find the configuration which will minimize an energy functional.

The models incorporate information on the brain structure’s geometry and eventually statistical

shape variation. A particular approach has been introduced by Barra and Boire, [Barra and

Boire, 2001] which uses information fusion and fuzzy maps for segmenting selected structures.

Methods for volumetric measurements include: a) the use manual tracing of boundaries in

subsequent slices and calculating the volume within the structure by pixel counting; b) the use

of tessellation [Arndt et al., 1994]; c) high dimensional brain mapping [Cserrnanasky et al.,

1994; Wang et al., 2001].

To enable the comparison across subjects Laaks and colleagues [Laaks et al., 2000] report the

ratio of the between the volume and the intracranial area, multiplied by 1000; where the

intracranial area is measured in a coronal section, at the level of the anterior commissure.

The results can also be normalized to the intracranial area or volume: for example the mean

hippocampal values can be expressed as a proportion of cerebral hemisphere volume

4.1.4 Background on active contours segmentation

Kass, Witkin and Terzopoulos [Kass et al., 1987] developed the active contour models, also

called snakes. The snake is defined by an energy functional for which the global minima

should be found. The snake is a contour of controlled continuity and can be acted upon by:

internal contour forces, image forces, and external forces which are either supplied by an

interactive user or another, higher process. The applications of active contour models include

contour extraction, motion tracking, image interpretation and shape analysis.

The initial contour is drawn manually and placed near the edges under consideration, and then

image forces draw the contours to the edge in the image. As the algorithm iterates, the energy

terms can be adjusted to obtain a local minimum.

The contour is represented by a vector v(s) = (x(s), y(s)) having the arc length as parameter.

The total energy of the active contour is the integration of local energies along its normalized

contour, which is written as:

dssvEsvEsvEdxsvEE conimagesnakesnake ))(())(())(())((1

0int

1

0

* ++== ∫∫ (Equation 4.1-1)


65

The energy term being minimized has: an internal energy (due to bending or discontinuities),

an image energy part (given by lines, edges and terminations), and a third energy term, which

incorporates external constraints.

Eint, the internal energy of the contour is given by:

2/))()()()(( 22int svssvsE sss βα += (Equation 4.1-2)

where vs and vss are the first and second derivative of v by the arc length s.

The first order term has large values where there is a gap in the curve (vs) and it makes the

contour behave like a membrane. If α is 0 a discontinuity may occur. The second order term

has large values where the curve bends rapidly (vss) and makes the contour act like a thin plate.

If β is 0 a corner may develop. The values of α and β at a point determine how much the

contour is allowed to stretch or bend at that point. For example a large β determines the

minimum energy contour to occur when the curve is smoother.

Several modifications/enhancements of this original model have been published, including the

incorporation of global shape constraints and its variability, through the use of point

distribution models in their active shapes models [Cootes et al., 1994].

Amini and coworkers [Amini et al., 1988] proposed a dynamic programming approach using

hard constraints, as well as first order and second order continuity constraints (soft constraints).

Their method is however computationally expensive. Williams and Shah [Williams and Shah,

1992] introduce a fast, greedy algorithm which retains the use of hard constraints but it is one

order of magnitude faster than the original one.

The minimum energy contour may be found using techniques like: a) variational calculus

[Kass et al. 1987]; b) dynamic programming: using hard constraints, as well as first order and

second order continuity constraints (soft constraints) [Amini et al., 1988]; c) a greedy

algorithm [Williams and Shah, 1992], allowing a contour with controlled first and second order

continuity to converge on an area of high image energy, the edges.

The performance of the snake algorithm depends on the initial states and given weighting

parameters. If the initial contour is not placed around an object, it tends to settle into a local

minimum near its initial location in state space. Even if placed near the true object boundary,

the snake can be trapped by weak ridges or isolated edge points. The original internal energy

measure of the contour makes the snake to shrink to a point or a line if no external force is

around to attract it. Due to this problem, blurred edge images are used to attract snake points

from distance. However, the true object boundary cannot be found if too much blurred edge

image is used. Hence, Cohen [Cohen and Cohen, 1993] introduced the balloon models to drive

the snake automatically to a good position. An external force is added, that makes the snake to


66

behave like an expanding balloon. The snake can pass over weak local minima due to its

inflating force. If the weighting parameter for the inflating force is chosen appropriately, the

snake can evolve until it settles on the object boundary. Gunn and Nixon [Gunn and Nixon,

1994] introduced a dual active contour. They used two inter-linked snakes to solve problems in

original snake method. One snake expands from inside the object, the other contracts from the

outside. The two snakes evolve until they meet at the same equilibrium state. If a snake stops at

local minima, the algorithm adds a driving force to the snake to move toward the other snake.

It is possible to segment each slice that contains the object of interest individually and

reconstruct the 3D object from consecutive contours [Cohen and Cohen, 1990; Chung and Ho,

2000]. However, these methods do not guarantee results of the same quality of results as the

3D based approaches.

Deformable surfaces are 3D models directly applicable to volumetric data [Staib and Duncan,

1996; Ghanei at al., 1998]. The initial model for the deformable templates may be generated

from a stack of initial contours, drawn by the user on cross sections of the volumetric data

[Ghanei at al., 1998] or can be derived from statistical atlases [Webb et al., 1999].

The hippocampus can be segmented via a high-dimensional transformation of a segmented

atlas based brain, to the individual brain [Haller et al., 1995], comparing the values of the

voxels that correspond to the hippocampus in the labeled image (initially hand segmented).

Linear [Webb et al., 1999] or nonlinear registration and normalization [Haller et al., 1995;

Collins and Evans, 1997] methods between a particular MRI and a labeled volume derived

from a large number of subjects have been used, as well as registration of two brains, one of

which was previously labeled.

Deformable models seem more appropriate than other segmentation and edge detection

methods due to their ability to treat a structure as a unit object, producing a closed contour.

However the result of using models is dependent on the initial placement of the model and the

parameters of the model, which may need tuning from a trained user.

4.2 Methods

We introduce a software tool, STRUCTSEG for segmenting deep cortical structures or cortical

patches based on stacking contours traced within subsequent slices. Theses contours are either

manually traced or refined from those coarsely traced, by means of an active contours method.

The algorithm we use is based on the fast algorithm proposed by Williams and Shah, [1992].

This method was chosen: a) for its simplicity; b) for its speed relative to dynamic programming

approach; c) for its stability and flexibility regarding the inclusion of hard constraints relative

to the variational calculus approach.


67

The data consist of 3D digital MRI scans of the head from normal subjects, T1 weighted, 256

x256x256 voxels, of about 1mm size. The software is implemented in IDL language.

The image can be presented in any of the three standard orientations. In general is presented a

zoomed image, three times greater than the original. The zoom factor can be interactively

changed.

The image brightness and contrast can be adjusted according to the parameters of the original

scan images.

The current point in the window where the segmentation is done is also visible in the two

complementary orientations, in separate windows.

4.2.1 Manual and snake based segmentation

In the manual mode the outline of the selected structure is drawn onto an MRI slice (either

coronal, sagittal or horizontal).

The contour from one slice is exported to the next slice on the anterior-posterior direction,

where it serves as initial contour. The procedure described for the previous slice is repeated.

The 2D contours obtained in this way are stacked and used for reconstructing the 3D structure

of the hippocampus. The structure’s volume is calculated after filling in the polygons, counting

the voxels and multiplying by the voxel dimensions.

The brightness and contrast can be adjusted so that the user can better understand the

information in the image. The user can see how the current slice fits in the 3D context of the

rest of the data by examining the "cardinal" views: coronal, sagittal, and axial.

The snake based segmentation is done after applying an edge operator (Sobel for example) and

starts from a manually traced contour. This contour undergoes deformation in order to find the

object/structure of interest. The energy functional to be minimized is:

Equation 4.2-1

The first and the second term are the first and second order continuity constraints and

correspond to the internal energy in the original model.

The last term measures image forces, such as edge strength or intensity.

The algorithm is iterative. During each iteration, a neighborhood of each point is examined and

the point in the neighborhood that has the smallest energy value is chosen as the new location.

Only closed contours are considered.

The continuity term is calculated as the difference between the average distance between points

and the distance between the two points under consideration:

Equation 4.2-2


68

The points having distances near the average will have the minimum value; the value of this

term is normalized to the largest value in the neighborhood to which the point may move,

giving a value between [0, 1].

The curvature term can be estimated in the case of evenly spaced points using:

Equation 4.2-3

The value is normalized by dividing it by the largest value in the neighborhood, thus ranges

from [0,1].

The image energy is given by the image gradient magnitude and normalized to the maximum

range of the gradient value in the neighborhood: (min-magnitude)/(max-min). The values are

negative, so that points with large gradient will have small values. In order to prevent the

occurrence of large values of this term in areas where the gradient magnitude is nearly uniform,

if (max-min) <5 then min is given the value (max-5).

At the end of each iteration the curvature at each point along the new contour is calculated. If

the value is a curvature maximum, then the β parameter at this location is set to 0. This step

functions as primitive high level process giving feedback to the energy minimization step. The

curvature at these points is computed as:

Equation 4.2-4

Curvature maxima points having values of the curvature above a threshold are considered as

corner points for the next iteration. A further condition for designating a point as corner is that

the gradient magnitude must be above some minimum value, preventing thus the formation of

contours unless the contour is near an edge.

β is set to 0 at the points satisfying the conditions: a) curvature maxima above a curvature

threshold; b) gradient above a threshold.

In summary, the active contours algorithm is applied on MRI slices where an edge detector was

applied. The energy functional is computed for the current location Vi and its neighboring

points. The new position of the point is the position having the smallest energy value. It is

assumed that checking local minima will lead to a global minimum.

Vi-1 has been moved already to its new position, and its location is used, together with that of

Vi to calculate the first order continuity term. Vi+1 is used, together with Vi-1, to compute the

second order constraints. V0 is processed twice, as the first and as the last point in the list

(close contour and obtain a behavior close to that of other points).

The parameters (the size of the neighborhood, point spacing, edge strength, curve strength,

maximum number of iterations, the contour change limit etc.) can be changed for every one


69

slice. If the result is not satisfactory, for example the contour got trapped by other edges, the

manual segmentation can replace the automatic one.

Finally the contours are stacked one on top of the other and the structure is reconstructed in 3D.

The results of segmentations done on different slice orientations can be combined. If further

editing is necessary, the segmented structure can be examined in the background of the original

scan and the original segmentation can be corrected.

A first examination of the segmentation result can be done using the visualization module,

VISIO.

4.2.2 Hippocampus Segmentation

Most methods for segmenting the hippocampus use coronal images, because both hippocampi

can be assessed simultaneously and the errors in estimating the volume of an anisotropic

structure are minimal if the slices that build up the volume are oriented perpendicularly to the

long axis of the object.

The “coronal slices” in which the hippocampus is studied are defined by various authors as: a)

perpendicular to the intercomissural line (the most usual definition); b) perpendicular to the

axis connecting the genu of corpus callosum to the clava (gracile tuberculus); c) perpendicular

to an axis tilted at 20o negative to the canthomeatal line (unites the corner of the eye to the

external auditory meatus); this direction corresponding to the longitudinal axis of the

hippocampus.

In our volumetric study of the hippocampal formation we include: Cornu Ammonis, dentate

gyrus, and subiculum.

Figure 4.2-1 Hippocampus tracing at the level of the hippocampal: a) head; b) the body and c) the tail.

A:alveus, th: temporal horn of the lateral ventricle, lv: lateral ventricle, the body; cc: the crus cerebri; ac:

ambient cistern; s: splenium of corpus callosum (subject J.R.).

th

a th

lv

cc

ac

s


70

We use coronal slices for segmenting the hippocampus and proceed along the antero-posterior

direction. We also examine axial and sagittal slices to obtain additional information on the

hippocampal anatomy and its relations to neighboring structures.

The following landmarks are used for segmenting the various parts of the hippocampus,

illustrated in Figure 4.2-1:

1. The hippocampus head: At the level of the hippocampal head, coronal scans show the

amygdala as a close superomedial relation. The two structures are separated by the anterior tip

of the temporal horn, although they are often not easily distinguished. The tip of the lateral

ventricle horn lies just lateral to the hippocampal head. Slightly posterior to this, the temporal

horn may extend medially above the hippocampal head separating it from the amygdala lying

above and medially to it.

The first slice where we segment the hippocampus is where the hippocampus first appears

below amygdala, where is a clear separation between the two structures [Abrahams et al.,

1999]. The hippocampus can be differentiated from the amygdala by visualization of the alveus

and typically, by a region of CSF superior to the alveus [Webb et al., 1999]. The alveus is

visible in images of good quality as a narrow band of white matter separating the rest of the

hippocampal head from the amygdala.

2. The hippocampus body: More posterior coronal sections reveal the transition between the

head and body of the hippocampus, at about the level of the red nucleus. The intralimbic gyrus

serves as a landmark demarcating the hippocampal body from the head. The body is more oval-

shaped and sits squarely on the parahippocampal gyrus. The temporal stem and inferior horn of

the lateral ventricle are used as lateral borders. The alveus and fimbria are used as dorsal

border, but should be excluded. The CSF of the ambient cistern and the crus cerebri are used

for medial border. The white matter of the temporal lobe helps define the inferior border.

3. The hippocampus tail: More posterior, differentiation between cornu Ammonis and dentate

gyrus becomes more difficult as the body flattens further to become the tail. The fimbria loops

upward and anteriorly to form the fornix, which is seen as a band of white matter adjacent to

the inferomedial border of the lateral ventricle and inferolateral to the corpus callosum. The

hippocampal tail loops similarly to form the indusium griseum, a thin strand of gray matter that

lies at the upper surface of the corpus callosum at the midline. Therefore if visible in the MRI

the lower border of the splenium of the corpus callosum can serve as the dorsal border. In more

anterior slices the pulvinar of the thalamus serves as dorsal border.


71

The CSF of the quadrigeminal cistern helps define the medial border. The lateral border is

defined by the ascending crura of the fornices and the CSF of the atrium of the latereal

ventricle.

The white matter of the temporal lobe helps define the ventral border.

The most posterior position where the hippocampus is to be segmented can be identified as the

slice where the crura of the fornices depart from the lateral wall of the lateral ventricle [Laaks

et al. 2000], or where the lateral ventricles split into the frontal and temporal horns [Webb et

al., 1999].

An example of segmented slice is shown in Figure 4.2-2, comparing the manual and snake

based segmentation.

Figure 4.2-2. Example of hippocampal segmentation: a) manual, b) snake based (subject J.R.).

A combination of coronal with sagittal images for identifying the most anterior slice, where it

connects to the amygdala and coronal slices proved to be the most helpful method.

For the hippocampus, the volumetric studies include: the dentate gyrus, hippocampus proper,

the subicular complex [Laaks et al., 2000] and eventually fimbria.

4.2.3 Amygdala Segmentation

We used coronal images for segmenting the amygdala in combination with sagittal images for

identifying the boundary between the amygdala and hippocampus or with the hippocampal-

amygdala transitional area. This method is particularly helpful for defining the amygdalar

boundaries, especially its posterior end [Figure 4.2-3].

A major problem is the separation of the hippocampal-amygdala transitional area, a thin strip

of gray matter which forms a distinct anatomical structure with distinct connections and

cellular composition. It is located at the posterior end of the amygdala, where the rounded

cortical nucleus of the amygdala transitions to strip of gray matter which connects to the

A B


72

subiculum of the hippocampus. In the coronal plane it appears at the level of the mammillary

bodies. To isolate it horizontal sections are most suitable.

Figure 4.2-3. The sagittal images are used to identify the anterior end of the hippocampus and the posterior

end of amygdala. The points in saggital slices are visible as stars in the corresponding coronal slices. They

indicate the coronal slice position and help identify the boundary amygdala–hippocampus (subject J.R.).

Several landmarks can be used for defining the boundaries of the amygdala:

a) the most anterior slice can be defined using as landmarks: i) the level of closure of the lateral

sulcus, identified in horizontal sections [Puessner, 2000], ii) the level the temporal stem (a

white matter tract linking the temporal lobe with the rest of the brain) [Shenton 1992], iii) the

section posterior to the one where the optic chiasm appears as a continuous structure.

b) the most posterior slice is defined in the coronal orientation using as landmarks: i) the level

where gray matter first starts to appear superior to alveus and lateral to the hippocampal head

[Puessner, 2000] (the alveus may not be visible because of partial volume effects (PVA) and in

this case the inferior horn of the lateral ventricle is used as border); ii) the mammillary bodies

for delimiting the amygdala from the anterior hippocampus region [Shenton, 1992].

c) the superior border can be defined using the thin layer of white matter dividing the amygdala

from adjacent structure, sometimes though this layer is not visible and the amygdalar gray

matter mixes with that of the putamen. In this case can be used an imaginary horizontal line

between the superior-lateral part of the optic tract and the fundus of the inferior part of the

circular sulcus of the insula.

d) the inferior boundary : the tendorial indentation can serve for separating the amygdala from

the enthorinal cortex, excluding gray matter inferior to the indentation. Sometimes in coronal

slices the hippocampal–amygdalar boundary is subjectively traced as a horizontal line, the

boundary being affected by PVA. In other slices the alveus can serve as a demarcation

landmark.


73

Horizontal sections can be used for increasing precision in the definition of the medial and

lateral borders, helping to exclude the enthorinal cortex which may have been considered as

part of the amygdala in the coronal sections.

e) the alveus , the ambient cistern or the uncal recess of the inferior horn of the lateral ventricle

can be used as the medial landmark

f) the inferior horn of the lateral ventricle is used as a lateral landmark

Figure 4.2-4 Segmentation of the amygdala, contoured in green. The temporal horn of the lateral ventricle

and the alveus serve as landmarks (subject A.I.).

4.2.4 Central sulcus segmentation

We followed the approach described in [Tzourio et al., 1997] to identify the central sulcus. The

approach is based on combining information from slices of different orientations.

a) Starting from (para) medial saggital slices the central sulcus is identified as the first sulcus

anterior to the callosomarginal sulcus (CM) [Figure 4.1-3 d, Figure 4.2-5b]. On paramedial

saggital slices, at the top of the hemisphere , the CS forms a typical notch , just in front of the

end of the ascending part of CM [Figure 4.2-5 c]. This is the rolandic genus and corresponds

to the hand area.

b) Next the CS is identified in lower horizontal slices as lying between the precentral and

postcentral sulci, the group of these three sulci making a typical pattern at the level of the

vertex. On upper axial slices the CS is characterized by a specific curvature, the rolandic genu,

and never intersects any of the surrounding sulci that run in a different direction, such as the

(superior) frontal sulcus or the intraparietal sulcus. [Figure 4.2-5-c, bottom]

On lateral saggital slices the CS is the third sulcus encountered when starting from the

ascending branch of the sylvian sulcus and moving backward [Figure 4.2-5-c, up].

Once the CS is identified the precentral and postcentral sulci are identified as the sulci which

lie anteriorly ands posteriorly respectively, and run parallel to it.


74

Figure 4.2-5 Identification of central (rolandic) sulcus (CS or rol) on (a) upper axial slices. (b) a paramedial

sagittal slice; (C) on external parasaggital slices (up) for the inferior part of CS and a horizontal slice at the

level of the hand area (down), CS (Rol) in red, the precentral sulcus in yellow, the postcentral sulcus in

blue, intraparietal in light blue, the superior frontal in green, the calosomarginal sulcus (CM) in orange.

f1=the superior frontal sulcus, f2=the inferior frontal sulcus, ips= intraparietal sulcus, t1= superior

temporal, cm=callosomarginal sulcus. (from N. Tzourio et al., 1997).

Since we were interested mainly in the hand area, the first two methods of identifying the CS

were usually for outlining the CS in MRI sections, the vertices stored being used for creating a

3D volumetric and surface model. A few typical slices are shown in [Figure 4.2-6].

Figure 4.2-6. The CS segmentation starts from medial slices, proceeds laterally and uses horizontal slices

for the lower parts of CS. However sagittal slices may be very useful for the most lateral parts. Most of the

times the combination of the two is used (subject R.B.).


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4.2.5 Thalamus segmentation

To identify the thalamus on MRI we used the region of interest (ROI) approach, since it is

highly reliable and respects individual variability [Andreasen et al., 1990; Jernigan et al., 1991;

Gur et al., 1998; Portas al., 1998a,b; and Lawrie et al., 1999].

For segmenting the thalamus we use coronal and sagittal slices and take advantage of the fact

that the thalamus can be identified on the basis of its relationship to the ventricles.

Figure 4.2-7 The tracing of the thalamus in coronal sections is helped by the following landmarks: 3v- the

third ventricle, lv-the lateral ventricle, ic-internal capsule, PU: pulvinar, bs: brain stem, cf-crura of the

fornix. The images shall be counted from left to right and top to bottom. The number of the right most

image in a row is indicated next to it (image modified from Portas et al., 1998a]. For details see text.

The thalamus is first visualized on MRI slightly posterior to the anterior commissure [Spinks,

R. et al., 2002], extending between the foramen of Monro and the posterior commissure,


76

Visualization terminates just beyond the level of the corpora quadrigemina (the two superior

and two inferior colliculi).

The landmarks which we used are listed below, based on [Portas et al., 1998a]:

1. The most anterior slice is defined using as landmark the mammillary bodies of the

hypothalamus or after the slice containing the clearest view of the anterior commisure [Spinks

et al., 2002]. The internal capsule also will appear thicker than in the previous section.

2. The most posterior slice is defined as the slice where the thalamus merges under the crura of

the fornices.

3. The most inferior slices is where thalamus merges with the brain stem. The zona incerta and

its junction with the internal capsule serve as the inferior border of the thalamus, thus

excluding the subthalamic nucleus, the substantia nigra and the nucleus rubor.

4. The superior margins are defined using the lateral ventricle as a landmark. As one moves

from anterior to posterior, the thalamus loses its magnitude. The coronal height of the thalamus

decreases, thus allowing the fornix to serve as the superior boundary in more posterior slices.

Medially the boundary is defined by the third ventricle.

The two separated volumes, one obtained from coronal and the other from sagittal slices are

combined in the end.

An illustration of the shape variability form the most anterior to the most posterior coronal

slices in a normal subject us shown in Figure 4.2-7, [based on Portas et al., 1998a]. Several

features and landmarks useful for segmenting the thalamus are described below.

1. The thalamic reticular nucleus is noticeable first. For the most anterior slices the boundaries

are: the internal capsule (ic), (laterally), the main body of the lateral ventricle (lv) (dorsally),

the third ventricle (3v) (medially).

2. The VA (ventralis anterior) nucleus is just dorsal to the hypothalamus (h).

3. The mammillary bodies (mb) of the hypothalamus are visible.

4. The lateral edges of the thalamus are affected by partial volume effects

5. The mammilothalamic tracts can be viewed, and as marked in 6 the interpeduncular fossa.

7. Globus pallidus (gp) is marked for a reference

8. Note the extension of the interpedunculare fossa in slice 8 and next slices

9. The medial lemnisucs (ml), the putamen (p), zona incerta (zi) as the inferior border, the

subthalamic nucleus, and the crus cerebri are visible.

10. The brain stem (bs) is evident, substantia nigra (sn)


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11. The intherhalamic adhesion (the intermediary mass) of the thalamus is visible. The

dorsomedial nucleus appears. White matter can be seen separating the main body of the

thalamus from the lateral geniculate nucleus (lgn).

13. To delineate the brain stem from thalamus (when the habenula is not visible) it is necessary

to draw a line from the hypothalamis sulci of the third ventricle to the deepest indentation of

CSF, ventrally and medially to the medial geniculate nuclei (mgn).

14. Partial volume effects make the delineation of the lateral thalamic boundary difficult.

15. The lateral limit is marked by the internal capsule. To delineate the brain stem from

thalamus is necessary to draw a line dorsally from habenula to the deepest indentation of CSF,

ventrally and medially to mgn.

16. The cerebral aqueduct (ca) is evident and the lgn and mgn appear as ventral bumps on the

pulvinar nucleus (pu).

17. The brain stem is clearly delineated from the pu. CSF serves to identify the central border.

18. The pulvinar is ball shaped. The fourth ventricle is visible and perhaps the pineal gland.

20. The boundaries are: the lateral ventricle (dorsally), the cistern of the great cerebral vein

(laterally), temporal stem (medially) and CSF. Note also the superior colliculi (sc)

21. A small portion of the pulvinar appears between the crus fornices (cf) and the CSF of the

great cerebral vein (superior cistern).

4.2.6 Brain stem segmentation

The brain stem is relatively easy to identify in horizontal slices but some user interaction is

necessary to cut the connections to the cerebellum through the cerebellar peduncles and to

establish the superior limit with the diencephalon. For the definition of these boundaries see the

section on thalamus segmentation.

Figure 4.2-8. Tracing the brainstem at the level of: a) medulla; b) pons; c) middbrain. Cp: cerebellar

peduncles (subject J.R.).

iv ventricle cp

cp


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4.3 Results

Methods for segmentation, visualization and volumetric analysis of brain structures from MRI

scans are proposed in this chapter, and implemented into the software called STRUCTSEG.

We have applied it to various subcortical structures and also to the brain stem, cerebellum,

cortical areas like the pre and postcentral gyri, the central sulcus, and the primary visual cortex.

A number of structures were segmented and used primarily for activation visualization

purposes (brain stem, central sulcus, pre and post central gyri, amygdala and thalamus).

Preliminary volumetric studies were conduced for the hippocampus and amygdala and our

results are comparable with results reported in the literature.

The segmentation accuracy evaluation was done by comparing the volumes of the

corresponding structures with those reported in other studies and qualitatively by inspecting the

segmented brain structures in the background of the original MRI.

The volumetric measurements use voxel counting, and the number of voxels is multiplied by

the voxel size in case there is no gap between the slices. Examples of volumes of segmented

structures and the percentage they represent out of the brain volume, are shown in Table 4.3.1

for one subject (J.R.).

Table 4.3-1Segmented brain structures volumes and the percentage they represent out of the brain volume

(subject J.R.)

Examples of segmented structures are shown in Figure 4.3.1, the right most column shows the

individual structures in relation with the cerebrum.

Structure (both left and right

hemisphere)

Volume (cc) Area

(cm2)

Volume/Brain

Volume (%)

Brain 1423.00 1345.11 100.00

Cerebellum 157. 88 350.94 11.09

Brain Stem 36.25 86.50 2.55

Hippocampus 7.99 39.30 0.56

Amygdala 4.41 20.49 0.31

Hipopcampus-Amygdala Complex 13.71 51.87 0.96

Thalamus 15.78 56.59 1.11

Lateral Ventricle 28.73 92.34 2.02

Central Sulcus 10.08 79.96 0.71

Pre and Post central gyri (GM) 70.87 21.36 4.98


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Hippocampal-amygdala

Amygdala

Figure 4.3-1.Selected segmented structures presented independently and in relation to the cortex (right

most column). (Subjects J.R., T (brain stem), V.P. (calcarine sulcus walls), R.B. (lateral ventricle). The right

column shows the individual structures in the background of the whole cortex.

Cerebellum Brain, no cerebellum

brain stem (BS) BS, medial lemniscus

Hippocampus Thalamus

Pre and postcentral gyri Central sulcus

Lateral ventricle Calcarine walls


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We segmented the amygdalas for 3 subjects and the results are shown in Table 4.3-2.

Table 4.3-2. Amygdalar volumes

The hippocampus volumes for subject A.I. are 3.3cm3 (LH) and 3.5cm3 (RH). For subject JR

the hippocampus volumes are 4.3 cm3 (LH) and 3.6cm3 (RH).

The main use of our segmentation results, pertaining to the: central sulci, gyri, thalamus and

brain stem, is for mapping functional data from MEG and EEG. Another use of the

STRUCTSEG program is for editing preexisting segmentations of the brain where undesired

connections like those to the eyes were still present.

The speed performance of the STRUCTSEG software module was evaluated mainly on a PC,

1700 MHz AMD Athlon, 512 MB Ram, under Windows 2000 operating system. The duration

of the segmentation process for brain structures (i.e. hippocampus, amygdala etc.) depends on

the complexity of the structure and its extent. The larger the number of slices where the

structure is present, the longer the process. Typically, the segmentation process does not take

more than 1 hour. An exception is the central sulcus, which is thin and sometimes interrupted

and may require editing and the combination of segmentations done on different slice

orientations. The snake based segmentation adds a few seconds for each slice (approximately

2-5s).

4.4 Discussion and conclusion

The subcortical structures may have relatively low contrast and multiple as well as

discontinuous edges in MRI. This characteristics make challenging an automatic segmentation.

The application of active contours to the problem of segmenting selected brain structures and

nuclei is investigated. In some slices the snake “bleeds” and finds other edges. In these cases

the manual segmentation is preferred. Because the MRI scans have different image quality, the

interactive tools for changing the zoom factor, contrast and brightness have been found helpful.

In the case of hippocampus segmentation a problematic area is the hippocampal–amygdala

transitional area. The amygdala, located at the anterior border of the hippocampus, presents

challenges because of its small dimensions and the nondistinct edges (especially in the

hipocampal-amygdala transitional areas) but also because parts of the basal ganglia from

Subject Right Amygdala (cm3) Left Amygdala (cm3)

TR 2.08 2.71

TK 1.93 2.45

JR 2.81 2.92


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superior and enthorinal cortex from inferior blend in [Pruessnes, 2000]. Similar problems

appear for the thalamus, where is it difficult to find “hard” boundaries in axial or sagittal

sections, but the combination of the two orientations can be useful. The brain stem and

cerebellum appear easier to segment, however user intervention is required to separate these

structures at the cerebellar peduncles. The good definition of the lateral ventricle’s boundaries

makes it a good candidate for semiautomatic segmentation, using the same procedure as for the

whole brain.

The volumetric measurements are derived from segmentation of specific brain structures based

on one or at most three subjects for the same structure. Although insufficient for statistics our

measurements compare well in general with other data reported in the literature. We measured

the hippocampus volume and this ranges from 3.3-4.3 cm3 but we cannot yet make a statement

about differences between the right and left hippocampus due to the reduced number of

segmentations. The ratios between the hippocampal and the brain volume are close to 0.5, as

reported elsewhere [Webb et al., 1999].

Most authors report absolute measurements for the volume of the hippocampi, which range

from 2.5-3.5 cm3, with lower limits for the normal hippocampus 1.7cm3 (LH), 2.0cm3 (RH).

Pruessnerr and colleagues [Pruesnerr et al., 2000] manually segment the hippocampus and

amygdala and report larger volumes of the right hippocampus, 3.324 versus 3.208 cm3, but no

interhemispehric differences for the amygdala (1.154 versus 1.160 cm3). The measured

amygdala volumes usually overestimate the true size according to postmortem studies [Convit

et al., 1999], the reported values range from 1.3cm3 [Rossi et al., 1994] to 3.4 cm3 [Watson et

al., 1997]. Convit and colleagues report values of 1.05 cm3 for the right, 1.14cm3 for the left

amygdala. Our result are in the range 1.9-2.9cm3.

Spinks and colleagues [Spinks et al., 2002] used manual tracing in multispectral images and an

artificial neural network and estimate the volume for the left (7.05+/-1.12 cm3) and right

thalamus (6.55+/-0.94 cm3). The bilateral thalamic volume for one subject is 16.71 cm3,

unnormalized.

While the structure’s shape can be retrieved from 2D contours, the use of a predefined model

may speed up the procedure and be beneficial for cases where a large number of subjects are to

be analyzed. The segmentations we provide can serve as initial models but may need to be

corrected for the individual brains.

In general a certain amount of knowledge is needed to separate a structure from its

environment. Our manual method is helped by the simultaneous visualization of the three

cardinal planes and the use of various image operators, like contrasting and zooming. While


82

tedious the manual segmentation is considered the gold standard against which automated

segmentation methods are to be evaluated.

Our semiautomatic method combines the advantage of human expertise with the power of

active contours in detecting and tracking a structure’s edges, aiming to reduce the time

necessary to accurately and reproducibly segment the selected structures.

A more advanced step would be to incorporate the knowledge of an expert anatomist into

models used to segment the structures from individual MR scans, eventually registered into the

same (Talairach) space.

There are still discrepancies across laboratories which perform segmentation of the same

structures. These are due to the use of different: a) definitions of boundaries b) segmentation

protocols and data parameters (slice thickness, orientation) but also c) methods for volume

quantification. The most widely used seems to be the manual tracing of boundaries in

subsequent slices and calculating the volume within the structure. Arndt [Arndt et al., 1994]

investigated the use of surface tessellation for estimating the volume and compared it to the

simple pixel counting technique which proved to be more robust. Others [Cserrnanasky et al.,

1994; Wang et al., 2001] used high dimensional brain mapping.

A natural follow up of this study would be the extension of the method to 3D. We plan to use a

segmented structure as an initial model fed to the active contours algorithm. The model would

undergo deformation to find the true boundary of the structure in a second brain, after

registration and normalization of the two brains.

The normal brain is subject to a large variability and the shape characteristics of its structures

may be quantified and included into a statistical atlas as reference. The methods currently used

for analyzing those differences involve laborious manual tracing of the contours of anatomical

structures derived from MRI scans. Repeatability, inter rater variability and lengthy times

required are problems characteristic to the manual segmentation. Automated procedures, like

the one reported by Spinks and colleagues [Spinks et al., 2002], are very promising and would

increase both the number of regions as well as the number of individuals who could be

investigated in any one study. The normal variability of structures, the multiple edges, the fact

that neuroanatomy experts make decisions based on external landmarks and the need for a

close initialization of deformable models are challenges for the automatic methods. The

possible errors involved in the automatic segmentation of subcortical structures are important

for the small dimensions of these structures. A semi-supervised approach like the one we

adopted is simple and yet accurate since it allows the control of the results of segmentation.

Chapter 5 Visualization of surface activation

83

Chapter 5. Visualization of surface activation

Chapter 5. Visualization of surface activation .................................................................................83

5.1 Introduction.........................................................................................................................83

5.2 Methods ..............................................................................................................................85

5.2.1 Extracting the structural information ...........................................................................86

5.2.2 Computing the activation maps....................................................................................87

5.2.3 3 D Visualization..........................................................................................................88

5.2.4 Slice views....................................................................................................................90

5.2.5 The VISIO software features........................................................................................90

5.3 Results.................................................................................................................................92

5.3.1 Qualitative Evaluation of Segmentation ......................................................................93

5.3.2 Surface activation visualization ...................................................................................94

5.4 Discussion...........................................................................................................................99

5.1 Introduction

Visualization has played an important role in mathematics. While some people can see in their

mind’s eye the beauty and structure in mathematical or statistical relationships, most require a

visual representation to appreciate these dependencies. It is no wonder then that it is said: “the

most profound use of computers in mapping is in the field of visualization” [Toga, 1996].

Imaging the electrical activity of the brain is becoming one of the major challenges in functional

neuroimaging. The scope for visualizing the brain activation is to extract the meaningful

information from complex data sets, display, summarize and interact with these data. In the case of

MEG and EEG this information refers to changes in the brain activity, eventually linked to a

particular task.

MEG and EEG are the unique noninvasive modalities which provide a measure of brain activity at

the time scale of neuronal currents. The goal is to represent the functional information in the

background of the anatomy so as the reveal the spatiotemporal evolution of the activations and the

interplay between distinct activated areas. The efficient visualization of large MEG data sets in


84

combination with the anatomy is a challenging task partly because of the computational cost of

processing or simply manipulating the data, and the apparently low signal to noise ratio.

The multidimensional nature of brain data lends itself to a variety of visualization techniques

concerned with modeling, manipulation and display.

The visualization can be done using 2D (slices) or 3D (volume) representations. The classical

representation for visualization uses slices extracted from MR scans but may fail to capture the

true 3D nature of cortical paths and the relationship to the orientation of the local anatomy. The 3D

based approach for visualizing activation in the background of the anatomy presents the advantage

that it completes the picture with information missing from the slice representation. This

representation allows to better examine, in a single view, the timing of distinct sources and the

relationship between these sources to the topography of the anatomical structures.

Most of the existing software for brain activity visualization are 3D based (BrainVoyager,

MRI3dx, Freesurfer, Brainnstorm, etc). Furthermore these 3D surfaces can be viewed in a single

plane after flattening [Fishl et al, 1999 a and b; Hurdal 1999; Van Essen et al, 1998; Wandell et al,

2000]. The fully or partially flattened representations have the advantage that they expose

activated areas buried within sulci. However, the flattening methods introduce metric distortions in

the surface representation [Fischl et al, 1999b] while they hide anatomical relationships of

potential importance when studying the interactions of brain areas.

The structure being visualized can be the whole cortex, the white matter only, selected parts of the

cortex, or specific subcortical structures. New imaging technologies allow the visualization of

white matter tracts. The structural information provided in this way is combined with functional

data.

The functional data support different types of representation but they rely in our case on the

original raw data, as recorded from the EEG or MEG sensors. However the EEG or MEG raw

signals cannot be directly attributed to underlying cortical regions [Gross et al 2001; Dimitrov

1998]. Either the statistically processed data [Dimitrov, 1998] or the coherence measures [Gross et

al, 2001] are usually mapped on the brain. The explanation lies in the complex relationship

between a signal source and the recorded signal and this is obtained solving the forward

electromagnetic problem. Especially for EEG the potentials are smeared because of the

inhomogenous conductivity of the head. The activity from even small cortical patches is therefore

likely to be recorded by several sensors. The sources of signals can be modeled as single or

multiple dipoles, multipoles [Mosher et al. 1999] or continuous [Malmivuo, 1995; Hämäleinen,


85

1993]. These sources can be remapped on the cortex to the vertices, elementary patches or

collections of patches [Mosher et al 1999; Kincses et al, 1999; Kincses et al 2000] on the

tessellated brain structure’s surface. The intensity or magnitude of a response is conveyed by the

value of a voxel or patch, associated to the physiological measurement and pseudocolored to

enhance differences. The original structural data are thus texture mapped with values

corresponding to the activity.

A combination of these data representations can be used. For example dipoles, or maximum

current density vectors for a region of interest [Ioannides et al, 2002 a and b] can be represented in

the background of the activity maps.

The volume representation provides the most data about the brain and the resolution can be

manipulated to use fewer voxels for less demanding applications but in general are more

computationally expensive than surface representation. The surface representation provides no

information on the inside of the brain while explicitly defining the geometry of the structure

exterior.

From the digital representation of a brain structure one can infer morphometric measurements such

as area, volume and shape related information. These calculations are more efficient for a surface

than for the volume representation of the same brain structure, because of the smaller number of

voxels involved. Furthermore statistical analyses can be done to compare and correlate selected

brain aspects from different modalities or from different subjects. The results can be visualized as

individual segmented structures or in relationship to the whole brain anatomy, which can be

rendered as a transparent surface.

In this thesis we choose as visualization method, a 3D surface representation of the anatomy,

pseudocolored in accordance with the activity values. We developed a software program for

visualization of activation data (VISIO), imported from source analysis programs like MFT or

BESA. This helps reveal the relationship between surface topography and function and helps the

recognition of possible interacting cortical areas.

5.2 Methods

We introduce VISIO, the third module in the software suite called SAV (Surface Activation

Visualization). Like the first two modules VISIO is implemented in IDL and follows an object

oriented approach in the software design. While the first two modules are concerned with the

processing of anatomical information in order to make it suitable for activation studies, this last


86

module is concerned with mapping the activation data on the brain structure, and the efficient

visualization of these combined data.

5.2.1 Extracting the structural information

The MRI data are suitable for our visualization only after performing several image processing

operations. In our case, the MRI data consist in T1 weighted scans, 256x256x256 almost isotropic

voxels. The scans are transformed to 8 bit/pixel. The background noise is reduced through the use

of low pass filtering. In the end only the selected segmented structure is retained from the MR

scan. The segmentation process is described in chapters 3 for the whole cortex and 4 for selected

brain structures.

In the segmentation modules the visualization pipeline starts from the tomographic volume which

is preprocessed with various operators in order to enhance the observation of meaningful

information (low pass filters, contrast, brightness, and threshold. The resulting 2D contours are

stacked in STRUCTSEG, while in CORTSEG a grey level volume is reconstructed directly. This

volume may contain other attributes, in addition to the grey level value, like the probability to

belong to a tissue type after classification into the grey matter, white matter or background. The

volume visualization pipeline ends with the surface based rendering.

The extracted volume is retained and used in slice representation and the boundary with

background voxels is extracted as an isosurface, tessellated and characterized in terms of vertices,

polygons and local normals.

If the resulting surface is noisy, a laplacian smoothing can be applied to each vertex on the

triangulated mesh.

)( )(00

)()()1( ni

M

jnjnini xx

Mxx −+= ∑

=+

λ

xi(n) is vertex i for iteration n

λ is the smoothing factor

M is the number of vertices that share a common edge with xi(n).

Another option is to examine a less complex surface, by decimating the mesh while reserving as

much of the original anatomical information.

These data are the basis for visualization of functional data, as described in the following section.


87

5.2.2 Computing the activation maps

The functional data are read from files which contain the result of MFT analysis [Ioannides, 1990]

on MEG data. The MFT solutions are computed within the source space covering the whole brain,

brainstem and cerebellum in the nodes of a lattice. The activity values to be displayed are

interpolated from the MFT solutions, at locations on the surface of the structure of interest, such as

the cortical mantle, brain stem, thalamus, etc. The surface data are of interest for us and therefore

only the grid points in the neighborhood of the extracted surface are considered. The neighborhood

size can be adjusted by the SAV user. In this way, the cortical current images are created,

representing the current density on the cortical surface at a single time point.

The user has the possibility to choose the neighborhood size, related to the depth measured from

surface (the cortex for ex.) where from the solutions can still be projected onto the surface. Once

the neighbors of a point on the cortex are known, the activation value at that point is calculated

using Sheppard’s method for the given neighborhood, as the weighted sum:

)()()(1

i

N

ii xfxwxs ∑

=

= ; where p

N

j

pji

ii

xx

xxw

−

=

−

∑ −

−=

1

Where N is the number of source points in a vicinity defined by the maximum distance to the point

x on the cortex. The weights are nonnegative and sum to 1 and the exponent (the p factor) used is

either 1 or 2. Additionally the condition that the interpolated values are equal to the “observed”

values in the nodes of the lattice must be satisfied.

The vector as well as the scalar properties of the activation data can be simultaneously displayed

by showing the contour plot of the power or current density modulus and the current density vector

map.

For the actual display, a part of the color scale is reserved for grey scale representation of the

anatomy. Information on the activation modulus is shown using color coding, for example in the

red-yellow color scale, while information on the current flow direction relative to the local

normals is conveyed by associating outgoing currents with colors in the red-yellow range and

incoming currents with colors in the blue-green range.

There are several possibilities to visualize with VISIO the distributed functional data.

The display may consist in contour plots of activation maps which represent:

a) the moduli of the cortical current distributions, usually represented in a color scale from yellow

to red, the red color being assigned to the highest values;


88

b) the signed values of the moduli of the cortical current distributions, usually represented in a

color scale from blue to red. The red color is assigned to the highest positive values and the blue

colors to the highest negative values. The small absolute values are in this case yellow if positive

or green if negative;

b) the statistical parameter maps derived after t-tests or Kolmogorov Smirnoff tests. In fact the

modified p values derived from these test are represented and they include the signed value from

the test, these being multiplied by (1-p). In this way the most significant values (for small p

values) are shown in red or blue.

Another way to present information on the current flow direction is to represent the current density

vectors or dipoles as a vector map on the structure surface. The otherwise dense mesh of vectors

(resolution 1mm) can be trimmed so that the user can make sense of the general behavior. The

area of maximal activity can be indicated by one vector only. The outgoing vectors are assigned

one color, while the incoming vectors are assigned a different color. The display of equivalent

current dipoles uses the same conventions and spheres replace the traditional arrow symbols.

In SAV, two visualization methods are possible: surface and slice representation. On each of these

representations are shown dipoles, vector maps or surface activation maps. Each of these

approaches is described in detail in the following subsections.

5.2.3 3 D Visualization

Our main goal is to visualize the activations on the surface of specific segmented structures,

ranging from the entire brain to selected subcortical structures or patches of cortex. The

segmentation program allows the user to define interfaces as (thin) surfaces or anatomically well-

defined parts, gyri or sulci, of the cortex like the somatosensory or visual cortex and display them

independently or in the background of the rest of the brain, or together with other structures.

SAV uses a surface representation of the anatomy data, the extracted isosurface based on the

segmentation performed with either CORTSEG or STRUCTSEG modules. One or more surface

objects can be visualized simultaneously by mapping a semitransparent texture image on one

surface.

The activation data are rendered onto the anatomy, which is so viewed as a pseudo colored

surface. Different types of rendering and shading are possible: from a (colored) mesh object to a

surface which can use flat/constant shading or interpolated/Gouraud shading.


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The choice of the color scale used to represent the activation values is left at the latitude of the

user who can simply choose from a predefined list. We preferred to use a color scale with “warm”

colors (from yellow to red) for outgoing currents or and for the statistical parameters indicating

hyperactivation and a “cold” color scale for “incoming” currents or for the statistical parameters

indicating hypoactivation. The color coding mentioned above is our usual choice but one may

choose other color scales with a smaller number of colors for example, which may be able to

reflect the changes in activity values from one region the neighboring one in a more striking

manner.

The color bar which accompanies the display indicates the range of activity values (min, max

values) for each time slice, in accordance to the time /spatial normalization options.

SAV allows the user to customize the viewing parameters interactively. Thus, the user can set the

object orientation, zoom factor, and position within the draw area. Three sliders are controlling the

rotation of the 3D model around each of the X, Y and Z axes, corresponding to left-right (LR),

anterior-posterior (AP), superior-inferior (SI) directions. Alternatively the object can be

interactively rotated by clicking and dragging with the left mouse button on the drawing area.

Outlines of sulci or structures of interest can be overlaid on the image to help a more precise

orientation relative to the specific anatomical landmarks (Figure 5.3-1, D). The outlines can be

traced in slices using the STRUCTSEG module or on the rendered surface using the VISIO

module.

All objects on the display area (anatomy, axes, wire frame box, color bar, etc) can be shown or

hidden. The displayed objects can be translated and rotated in order to better expose the areas of

interest. Also they can be made transparent or opaque to allow visualization of structures lying

inside or behind them as well as dipoles locations.

It is possible to select zoom factors and predefined viewing transforms: top view, left side view,

right side view, etc. User defined viewing transformations can be created and saved for later use,

to enable the observation of the activation for a different condition or latency from exactly the

same perspective and angle. Labels can be introduced in the images in positions defined by the

user to ad extra information on the experiment or anatomy.

The latency is displayed for each time slice analyzed to track time in successive data and

animations.


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5.2.4 Slice views

Brain slices can be visualized in one of the three orientations: coronal, sagittal or horizontal,

allowing quick browsing through the data for one time slice and inspection of activity buried deep

within the sulci. The data may be presented as clusters of 16 slices, each of which can be

individually zoomed. Alternatively one may prefer quick interactive browsing through the data by

moving the cut planes to the desired location [Figure5.3-4].

5.2.5 The VISIO software features

Surface representation and slice views

Current density maps are the main way to examine the time course of activation. The activation

values are interpolated in the nodes of the mesh which represent the selected brain structure. This

surface is pseuocolored according to the activation values. The activation data can also be

examined in slice cuts at any of the three stereotaxic planes through the brain volume. The

parameters of the analysis and the file name containing the activation data are indicated on the

display and can be saved for “offline” analysis.

Statistical data display

The same kind of contour maps used to illustrate the current density distributions are used to

illustrate differences between activations at two distinct latencies or between conditions. The

results of statistical analysis using the t –test and the Kolmogorov-Smirnov (KS) test can be

visualized in this way. The t or KS maps are pseudo-colored in accordance to the p factor in order

to identify areas where the activity is statistically different from the base line activity of the same

area or from the activity of the area during a specific condition. The color map uses red or blue for

the most significant changes. The same threshold buttons used for the classical contour maps or for

the vector maps are used for the p value displays

Animations

SAV makes possible the dynamic analysis of activation. Succesisve images of the type described

in the above paragraph can be assembled into animations, enabling the eye to peruse in its usual

function the temporal dimension of the data. The start and the end time for the animation analysis,

the normalization type and other display parameters are defined by the user and can be changed for

each analysis. Alternatively the parameters can be provided in a text file and the program may


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analyze automatically multiple slices with the same parameters and assemble the resulting images

in an html page. An example can be seen at the website

http://nucleus.med.upatras.gr/~alex/sav_results.html.

Reducing surface complexity

The large data sets describing the anatomy, combined with even larger data sets describing the

activations time course slow down the analysis and therefore any reduction in computing time is

important. For this reason one may choose at times to work on a simple mesh, obtained from

decimating the original one. The original mesh can be decimated and/or smoothed, in an attempt to

reduce its complexity while preserving as much detail as possible where this is important.

Thresholds, normalization, and regions of interest

The subtle differences in magnitude values may not be well noticeable when observing the whole

brain. We make use of thresholds to emphasize regions of high activity; and we use separate

thresholds for the modulus (displayed by the contour maps) of incoming and outgoing vectors. The

same thresholds are used for the vector maps. Additionally the vector maps are “trimmed” to a

user-defined percentage of the total.

The analysis can be done one time slice at a time to emphasize local maxima but it is most useful

to compare the time course of activations. For the analysis of succesive time slices the initial time

slice and the end time slice have to be specified by the user. The results will be normalized to the

minimum and maximum values from a given time moment, time range or for the whole time range

in the hyper file. The global normalization can be used for analyzing a specific time range, but the

normalization is done to the global minimum and maximum. The normalization can be done also

in space, to the range of values pertaining to a region of interest (an anatomical structure), or to the

entire source space. This is particularly useful since some subcortical activations may sustain

electrical fields which are orders of magnitude smaller than the cortical ones.

Activation curves

Activation curves can be produced to analyze the time course of activity for a selected voxel on

the brain surface. The voxel of interest is selected by mouse clicking on the 3D representation of

the segmented cortical surface. The activity then is computed using nearest neighbor interpolation.


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Output

Several types of output files can be generated from the VISIO program and these are listed below:

a. Static

The snapshot of the display window can be saved for further analysis as JPEG or GIF files. The

names of these files are automatically generated and comprise information on the functional data

like: time, view (anterior, posterior, etc), latency, threshold, normalization and weighting options,

or zoom factor.

b. 3D scene content -VRML

To add the third dimension of the data, the content of the scene can be saved as VRLM and viewed

later from within a web browser.

c. Dynamic

The dynamic analysis of activation data is done by creating animations from images generated for

successive time slices. The movie is presented online, while the activations are calculated. These

activation movies can be saved for “offline” analysis in MPEG format and played using any

MPEG player. For an easy interpretation of the images the time latency in ms is written onto each

frame of the movie.

d. Suitable for web

The results of automated analysis with a predefined set of parameters is presented to the user as an

HTML page, each image being accompanied by its self explanatory title, describing the

parameters used for analysis and the time moment or condition.

e. Exporting data

Data relative to the anatomy: vertices and local normals can be exported as text files for use

outside the SAV program.

5.3 Results

The results of this chapter are the different visualization options which are available within SAV

and in particular the VISIO module.

The speed performance was evaluated mainly on a PC, 1700 MHz AMD Athlon, 512 MB Ram,

under Windows 2000 operating system. The computing times are related to the number of vertices


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and triangles characterizing the anatomical surface on one hand and to the resolution of the grid

used by MFT and the number of time slices on the other hand.

The mesh which represents the surface of the segmented structure can be smoothed or decimated

to reduce its complexity. The duration of the decimation process depends on the geometry and size

of the structure of interest, and on how much the surface is to be decimated. In general, this is a

computationally expensive step for complex structures like the whole cortex. However for the

hippocampal- amygdala complex made of 10144 vertices, the decimation to 20% of the number of

vertices takes about 1.2s, and the resulting surface is made of 2011 vertices only.

In SAV, specifically in the VISIO module, the anatomical and functional data as well as various

operations applied to these constitute an object. We will refer to this as an active object. The

creation of an active object starts from brain anatomy and involves the extraction of vertices, local

normals and the polygons describing the connectivity. These steps take about 7s. The reading of

functional data and association of source points with vertices on the cortex found in a

neighborhood is the longest step and takes about 70 s for 234000 vertices.

The activation mapping involves interpolation, and processing like normalization in space and

time. For a subcortical segmented structure this processing is considerably faster than the analysis

for whole brain, due to the smaller number of voxels involved.

The uses of visualization and the applications of the methods introduced in SAV are: a) for

qualitative evaluation of the segmentation results; b) visualization of activation data; c)

quantitative measurements pertaining to the structure being visualized. Other applications are also

possible and will be discussed in the section 5.4.

Apart from visual information regarding the shape and location of selected brain structures

volumetric measurements can be obtained using SAV. The activation data are visualized and

assigned colors in various color ranges and the maximum and minimum activity values are

displayed on the screen, together with information regarding the activation file location and other

parameters used during analysis, including user defined labels.

5.3.1 Qualitative Evaluation of Segmentation

We have used visualization for the qualitative evaluation of segmentation results. The transparent

view of the segmented slice in the background of the original MR slice is used to determine if

different parameters need to be employed for the semiautomatic cortical extraction procedure (in

CORTSEG) or if manual editing is necessary (using STRUCTSEG).


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Figure 5.3-1(A) Segmented brain (pink) superimposed on original MRI image; (B) and (C) 3D

rendering of transparent segmented brain along with segmented amygdalo-hippocampal complex (B)

and thalamus (C); (D) Outline of central sulcus on a 3D brain; (E) Segmented central sulci and (F)

segmented pre and post central gyri. In inserts the brain is displayed at the same orientations as the

segmented structures (Subject JR).

Figure 5.3-1 A shows how the qualitative assessment of the segmentation accuracy is done in

CORTSEG by continuously seeing the segmented slice in the background of the original slice, at

any one step of the segmentation procedure.

The appreciation of the structure shape and location relative to the cortex is done using the

representation of multiple volumetric objects in the same view [Figure 5.3-1, B, C and D]. The

outline of the central sulcus was traced onto the surface representation of the cortex, using VISIO.

Visualization of segmented cortical structures is necessary to compare its shape against other

segmentations or atlas images [Figure 5.3-1B -F].

5.3.2 Surface activation visualization

The main display of SAV consists of contour maps. The surface representing the selected

anatomical structure is peusdocolored in accordance to the activation values. We analyzed in this

way the cerebrum and distinct cortical and subcortical structures in an attempt to visualize sources


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of activity reconstructed from MEG with MFT. The cerebellum and brain stem have been

analyzed in this manner for the study of eye movements’ electrophysiology.

The structure’s surface is textured with activity values and represented either a smooth surface

with Gouraud shading or as a colored mesh, as shown in Figure 5.3-2

Figure 5.3-2. The smooth surface (a) and mesh representation (b) of the anatomy. The example shows the

amygdala activated during REM, 181 ms after eye movement to the left. The axes are: X for anterior-posterior,

Y for superior-inferior, Z for left right. Subject JR.

Smoothing, mesh decimating or various qualities of rendering can be used to increase the speed of

analysis.

Selected cortical patches like the pre and postcentral gyri were analyzed in studies of evoked fields

through stimulation of the median nerve (at the wrist). The study revealed activation on Brodmann

area (BA) 3b, hard to visualize if looking from outside the cortex [Figure 7.4.2 d]. The central

sulcus was selected for exposing activation in an area close to the bottom of the sulcus, BA3a

[Figure 7.4.2 g]. The areas around the calcarine sulcus (V1, V2) were used for visual stimulation

studies.

We have focused on the analysis of activation following arm stimulation and used current density

maps and statistical parametric

maps [Figure 5.3-3], calculated

with software developed in the

laboratory of Human Brain

Dynamics, RIKEN, BSI, led by

Dr. A. Ioannides.

Figure 5.3-3.The main display of SAV.

A) Current density map evoked by left

arm stimulation of the median nerve

R L4.3x10-

-4.3x10-

a) b)


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at the wrist for subject J.R. at 22ms after stimulus onset, threshold at 25%/ The outline of the central sulcus is

shown in pink. B), C) and D) show statistical parametric maps obtained for right arm stimulation of subject

R.B. at latencies: 27 ms (B) and 87 ms in (C) and (D). The hyperactivated areas are shown in a warm color scale

and the hypoactivated in a cold colorscale.

If the surface representation cannot reveal enough information, i.e. a source may be buried in the

folds of the cortex then the slice representation is used, as illustrated in Figure5.3-4. The whole

volume of the cerebrum is presented and not only the boundary GM/CSF, used for calculations.

Figure5.3-4. Slice

representations reveal

sources of activations for the

hand area buried within the

sulci (subject JR). Two

coronal cuts are presented.

The center image (A2) is at

the level of SI and the right

image (A3) at the level of SII.

The brain activation can be represented using SAV in various ways, as exemplified in Figure 5.3.5

for the left arm stimulation, at 31ms.


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Figure 5.3-5. The brain activation following left arm stimulation , at 31 ms can be represented using SAV as: a)

contour maps and vector maps on the cerebrum (threshold 11%; vector density 2/1000); b) the density of the

vector map can be selected by the user (threshold 11%; vector density 8/1000); c) the maximum activity vector

(points to the hand area in SI) in the background of the anatomy, which is pseudocolored in accordance to

activation values. Multiple views are necessary to infer the vector orientation (d) contour maps and vector

maps on the segmented region of interest, i.e. postcentral gyri (e) or the central sulcus (f). The dipoles solutions

derived from MEG and EEG data model the activity in BA1 and BA 3b; they are shown relative to the central

sulcus, in a top view. The pink ball indicates the dipole sense, the mauve indicates the origin. The same colorbar

as in (a) applies to all the next figurines.

In (a) contour maps texture the brain surface and vector maps convey the direction of current

density vectors. The dense mesh of activation vectors can be “trimmed” at values selected by the

user. In (a) a value of 2/1000 has been used. In (b) this value has been increased to 8/1000. The

maximum activity vector is shown in (c) in the background of the anatomical surface,

e

d

ba

c

f


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pseudocolored in accordance to activation values. Sometimes multiple views are necessary to infer

the true orientation of the maximum activation vector, as shown in (d). The activation data

following left arm stimulation can be shown in the background of the segmented cerebrum or

analyzed relative to the anatomy of the postcentral gyri (e) or the central sulcus, seen in (f). The

choice of using the segmented postcentral gyri is justified not only by the fast computation time

but because the source is relatively deep and difficult to visualize on the extracted cortex The

dipoles solutions extracted from MEG and EEG analysis are shown in (f) relative to the anatomy

of the central sulcus.

Visualization can be applied to identify how pathology is correlated with morphological changes,

as it is the case shown in Figure 5.3-6.

Figure 5.3-6. The arrows point to the medial ending of the central sulcus in the left column and to the

upper/medial part of the central sulcus in the center. This is where is expected to be the representation of the

lower part of the body, and in the paraplegics can be seen areas of marked atrophy. In the right column are

shown the gyral walls anterior and posterior to the central sulcus. The arrows indicate an area where the

central sulcus appears enlarged in the paraplegic (subject EP).

The region where the lower part of the body is represented in the normal and paraplegic subject

appears atrophied in the paraplegic subject compared to the normal one, age matched.


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5.4 Discussion

Visualization is an essential part of the segmentation process and of the functional data analysis. In

the functional imaging context visualization makes possible a unified data representation: function

in a structural context. In this way it enhances data readability and helps interpreting it by making

available structural information and spatial relationships.

The surface constrained current density analysis that we use, helps to relate the location and spread

of activity to the brain structure anatomy. The combination of these static frames into animations

enhances the understanding of how the activity evolves in time and may help reveal the

interaction/coupling between distinct sources of activations. A distributed source model, like the

one used in MFT is a prerequisite to the analysis of spatiotemporal spread of activity [Ossenblock,

et al. 1999].

It is worth noting that the slice representation of the cortical map is not directly comparable to the

surface activation map produced with SAV. The later one presents only sources in the immediate

proximity to the cortex. However the two representations are strongly correlated, especially for

superficial sources.

For deeper structures the segmented structures themselves can be used for mapping the surface

data and the activation values may be normalized to the maximum value over both time and space.

These results shall always be compared with the global view of the activation map, for it is the

main goal to establish the relationship between distinct area activations, the way they interact in

time and the path of communication.

The activation analysis is more computationally demanding if it is done for the whole cortex

[Figure 5.3.5, a -d] than if performed for the postcentral gyrus [Figure 5.3.5, e] or central sulcus

[Figure 5.3.5, f] only but is essential for identifying all the activated regions on the cortical

surface, especially in cognitive experiments, where the number of sources is not known

beforehand. The maximal activity vector gives an indication on the location and orientation of the

source but the surface representation is important for appreciating the spatial extent of the sources.

The cerebrum is thus always used in the analysis of activation data, even if a selected structure is

of particular interest. It was found that this surface based analysis works best for superficially

located and focal sources.

Distinct generators are displayed as spatially separated sources of activation, as those in the SI and

SII areas in the hand stimulation example shown in [Figure 5.3-3]. The separation power is limited


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by the solution grid spacing and the spatial resolution of the input data describing the distribution

of activity.

The computation time is large and the surface representation can be noisy if the data are kept in the

original form. Smoothing, mesh decimating or using a low quality rendering can all be used to

increase the speed of analysis.

In this thesis a surface representation was used, in order to help identify the relationship between

surface topography and function and the interactions between cortical areas. However even if these

methodology would be able to generate a single story on a particular aspect of brain function one

could not say that visualization has done all it could to help understand the particular problem. An

integrated view would probably need to combine information from multiple sources. The use of

color, contours, and other visual clues to differentiate receptor densities, metabolic rates, electrical

potentials, magnetic fields, and other attributes of structure and function could be used to produce

multimodal views of the brain of increased complexity with overall patterns or relationships which

may tell a different story from that told individually [Toga and Mazziotta, 1996; Roland et al.,

2001].

Chapter 6. Applications in neurophysiology

101

Chapter 6. Applications in Neurophysiology

Chapter 6. Applications in Neurophysiology........................................................................... 101

6.1. Introduction .............................................................................................................. 101

6.1.1. The somatosensory system................................................................................... 102

6.1.2. Background on the early somatosensory evoked potentials/fields....................... 105

6.2. Methods.................................................................................................................... 107

6.3. Results ...................................................................................................................... 108

6.1.3. Electrical stimulation of nerves in the limbs of normal subjects ......................... 108

6.1.4. Electrical stimulation of the limbs for a paraplegic subject ................................. 115

6.1.5. The primary visual cortex - a combined fMRI and MEG analysis ...................... 117

6.1.6. Use of anatomical constraints for EEG dipole localization. Application to central

sulcus .............................................................................................................................. 118

6.4. Discussion ................................................................................................................ 121

6.1 Introduction

The present chapter is concerned with the use of SAV in studying the early sensory processing

in the somatosensory and visual systems. The novelty in comparison with early studies which

use activation data projected onto MR slices consists in the ability to expose the relationship

between time development of activation parameters and the cortical topography.

Our main goal is to study in parallel the temporal and spatial development of the main response

in the primary somatosensory cortex (SI) as well as of lesser activations surrounding the

primary focus.

Section 6.1.1 summarizes the principles of the neurophysiology of the somatosensory system.

Our methods are described in 6.2 and our results in 6.3. Our methodology in SEF studies on

several normal subjects and a paraplegic patient are described in sections 6.3.1 and 6.3.2

respectively. Additionally SAV was used in a study on the visual system (Section 6.3.3).

The information on the anatomy which can be obtained with SAV can be useful in many other

ways and we give an example where anatomical constraints were used to aid in EEG dipole

localization (Section 6.3.4).

Section 6.3.4 consists in discussions.


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6.1.1 The somatosensory system

Although sensory perception differs for each of the senses, there are three steps common to all:

1) a physical stimulus, 2) a set of events which leads to the transduction of the former into a

neuronal signal 3) a response of the nervous system which leads to perception or inner

representation of the sensation [Kandel, 1995]. A similar principle of organization is present in

all sensory systems, but the somatosensory system appears to be simpler and it was therefore

preferred as the initial study done with SAV. The general organization of the sensory system is

reflected by the sensory pathway for touch, which crosses to the brain side contralateral to the

stimulation side (at the medulla level), as shown in Figure 6.1-1.

Figure 6.1-1 a) The somatosensory pathways for touch illustrates the general organization of the sensory

system. From the peripheral receptor to the cortex the signal is transmitted to the cortex: i) the primary

sensory neuron ending transduces the stimulus into a patter of action potentials which is conducted via its

axons through the spinal cord (the dorsal column) to the medulla ii) the secondary neuron’s axon carries

the signal through the brain stem (the medial lemniscus) to the thalamus iii) the tertiary neuron carries the

information from thalamus to the cortex. b) the somatosensory cortex has been localized and divided in

several areas. The primary somatosensory cortex is located in the posterior bank of the central sulcus and

the postcentral gyrus and includes Brodmann areas 1,2,3. In the posterior parietal cortex areas 5 and 7b

are also somatosensory regions. The second somatosensory cortex (SII) is located in the upper bank of the

lateral sulcus, at about the lower end of SI. [a and b figures are from Johansenberg, 2002]. c) SI contains

four distinct sub regions organized in parallel strips, each of which contains a somatotopic representation

of the body [from Bear and Connors, 1995]

b)

a) c)


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Besides touch the somatosensory system is concerned with proprioception, temperature and

nociception.

The sense of touch is mediated by mechanoreceptors in the skin, which are either: a) superficial

or deep, b) with large or small receptive fields and c) fast or slowly adapting. [Table 6.1-1 ].

Small receptive fields

cutaneous

Large receptive fields

Subcutaneous

Fast adapting Meissner Pacini, Golgi

Slowly adapting Merkel Ruffini

Table 6.1-1. Receptor types and characteristics of afferent fibers from the skin of the hand (adapted from

Frackowiak, 1997)

Different combinations of receptors are usually stimulated at once and generate distinct

sensations according to the spatiotemporal pattern of the stimulus and the type of fibers which

is stimulated.

The somatic sensory information is processed in the human brain in several distinct cortical

areas, defined by criteria related to their cytoarchitecture, pattern of connectivity, neuronal

response properties, receptive field size, and the effect of lesions on perceptual capabilities

[Kaas, 1983]. Nine areas are considered to have primarily somatosensory function and these

include [Halgren, 1990; Frackowiak et. al., 1997; Mountcastle, 1998].

a) the primary sensory areas (SI) located the posterior gyri of the central sulcus. The SI is

organized in an orderly somatotopic way, the “homunculus” representation of the body surface.

SI consists of Brodmann areas (BA) 3a, 3b, 1 and 2. These areas are arranged into parallel

strips, perpendicular to the somatotopic map. Each of them contains the orderly representation

of the body.

b) the secondary sensory areas (SII) in the ventrolateral region of the parietal cortex, along the

upper bank of the lateral sulcus. The SII overlies the insula and because of this is considered as

being part of the parietal operculum [Woolsey 1946; Maeda, 1999].

c) the areas 5 and 7b of the somatosensory association cortex, located in the posterior parietal

cortex. Neurons in area 5 are responsive to passive and active limb movements and specific

combinations of positions of joints .The anterior part of area 7, area 7b, is involved with higher

order integration. These areas may have a role in visual guidance of movement [Frackowiak et

al., 1990].

d) the granular insular and retroinsular cortex [Schneider 1993; Mountcastle, 1998; Hendry et.

al., 1999;]. The insula is an anatomical target of area SII and appears to be important in

establishing associations that involve tactile information. The posterior insula contains a


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granular-isocortical area which is functionally linked to somatomotor systems, with

connections to neocortical areas, thalamus, and basal ganglia.

In addition to the well-documented motor representation, the precentral gyrus receives also

somatosensory input. Tactile and proprioceptive maps have been reported in the anterior bank

of the central sulcus (areas 4p and a) [Tanji and Wise 1981; Strick and Preston 1982; Geyer at

al. 1995]. Penfield and Rasmussen [1950], Gentilucci at al. [1998], reported that BA6 also

possesses a tactile map.

The somatosensoy areas receive information via afferents from ventro-basal thalamus (nucleus

ventro posterior lateralis) coming from cutaneous receptors (primary for the areas 3b and 1)

and deep receptors in muscles and joints (primarily for areas3a and 2). A schematic

representation of the discriminative touch path is given in Figure 6.1-1, showing how the

information from the receptor reaches the spinal cord and travels along the dorsal columns in

the spinal cord to the dorsal column nuclei in medulla, where from reaches the ventroposterior

thalamus which projects to the sensory areas in the parietal cortex.

Sensory processing in cortex is organized in cortical columns which span all layers

[Mountcastle, 1998].

All somatosensory sub modalities are finally represented at the contralateral SI, although the

level of crossing may differ from that shown in Figure 6.1-1 for discriminative touch.

The different areas differ in the proportions of cells associated with specific kinds of channels

and modalities however they are not totally distinct in function. Area 3b, for example, contains

both slowly adapting and rapidly adapting neurons, and evidence exists of their being present

in alternating columns (like the ocular dominance columns in the visual cortex) [Sur et al.

1981]. Also the input to the cortical layers of the somatosensory system is segregated, with

different channels being associated with different layers.

SII receives input from both sides of the body via afferents from the ventro-basal nuclei of the

thalamus. The SI and SII receive independent input from thalamus but they also have rich

interconnections. SI has mainly “forward” connections to both contralateral and ispilateral SII,

whereas SII has “feedback” connections to the same side SI and calossal connections with SI

and SII of the other side [Felleman and Van Essen, 1991].

As a consequence the SI is activated mainly contralaterally by unilateral touch stimulation

while in SII and insula it is common to see bilateral activation.

The evoked responses appear later and are of increasing complexity starting from the SI area 3

to 2 and 5 and then to the SII area. This supports the view that part of the somatosensory


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information is serially processed from SI to SII, considered a higher ordered sensory node

compared to SI. However SII receives also independent input from thalamus [Jones, 1985].

The two posterior parietal regions associated with somatosensory processing both contain cells

responsive to tactile input. Area 5 is immediately posterior to area 2. The receptive fields of

area 5 cells can be distinguished by their size, which represent larger areas of the body surface

than the cells of SI. The area 5 cells are responsive to particular movements.

Area 7, considered an endpoint of a spatial processing system, is responsive to sensory input

from more than one modality (e.g. visual stimuli as well as somatosensory) and is considered

to be a multimodal site. It contains a lateral region, area 7b which is responsive to

somatosensory stimuli.

6.1.2 Background on the early somatosensory evoked potentials/fields

A main idea of the study using evoked potentials/fields is to extract from the recorded signal

(EEG/MEG) the activity of a functional entity and the relationship of this activity to the

applied stimulus and its properties. This activity is considered to be either: a) the result of

activation of a group of interconnected neurons in response to the event, which must be

segregated from the ongoing activity (background) or b) the reorganization part of the ongoing

activity [da Silva, 1999]. These two points of view on the nature of the evoked activity gave

rise to two directions of studying them. The traditional approach relies on averaging over many

trials the signals recorded for a given time interval before and after the stimulus in order to

emphasize the response which appears consistently following the stimulus application and thus

increase the “signal to noise ratio”. The second approach attempts to preserve information

found in single trials and study the reorganization of phase spectra of the activity. The recent

work of Ioannides and coworkers [Ioannides et al., 2002a] emphasizes the importance of

studying information present in single trial activity.

The pattern of activation allows the division of the evoked response into components according

to topography, polarity, latency [Halgren, 1990], frequency or statistical properties

SEPs are produced by synaptic relays or tracts in the specific somatosensory pathway: dorsal

column in the spinal cord, medial lemniscus in brainstem, nucleus posteromedialis and nucleus

ventralis posterolateralis in thalamus, and the rolandic cortex.

The stimuli for evoking SEP are usually 0.2ms shocks repeated several hundred times for

producing the average signal with its salient features The median nerve is stimulated at the

wrist and the posterior tibial nerve at the knee or ankle.

Early work using direct electrical stimulation of the cortical surface [Foerster, 1936; Penfield

and Boldray, 1937] demonstrated the orderly representation on the cerebral cortex (SI) of


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sensation coming from various parts of the body. Newer work done by Kakigi and coworkers

[Kakigi et al, 2000] found this somatotopic representation in SI but also a more diffuse one in

SII using SEF. They reported SII components at 80-100ms after stimulation and a bilateral

function of SII.

The EEG evoked responses are described in [Halgren, 1990] and classified as: short latency

subcortical components (latency earlier than 20ms), middle latency components from the

primary cortex and supplemented by related areas (20-80 ms), and long latency components,

which overlap with multimodal cognitive components. It is considered that the early

components are not affected by repetition, attention or sleep while the later are affected to

some degree.

The EEG components and generators for the SEP following median nerve stimulation are

illustrated in Figure 6.1-2

Figure 6.1-2. SEP time course of activation for the pre and postcentral sources [from

Halgren, 1990]. For a detailed explanation see text.

The ascending action potential arrives at the brachial plexus at a latency of about 9ms. The first

clearly identified scalp component appears at 13 ms, probably generated by the termination of

dorsal column fibers in the cuneate nucleus. It is followed by N18.

The predominant potentials are a parietaly negative, frontally positive potential peaking at

about 20 ms, followed by a parietaly positive, frontally negative potential at 30ms. The parietal

and frontal peaks may not be exactly synchronous, and smaller peaks may be present in

between those two parietal: N20-P30, or frontal P20-N30. All peaks are contralateral at the

cortex [Halgren, 1990]. Because of the latency differences some authors refer to the parietal

N20-P27 and frontal P22-N30.

The generators of these potentials have been the subject of discussion. Some consider a single

generating dipole located in the posterior bank of the CS, resulting in opposite polarities frontal

versus parietal. Another theory postulates separate sources: one radial dipole in the crown of


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the postcentral gyrus accounting for the parietal peaks and one radial dipole in the crown of the

precentral gyrus for the precentral

MEG studies identify many of these sensory areas [Hari and Forss, 1999; Kakigi et al., 2000;

Kakigi et al., 2002] and reveal a complex cortical network, widely distributed, activated in

response to simple somatosensory stimuli. The first SI activation is robustly identified at about

20ms [Hari and Fross 1999], and this peak is identified as N20/M20 in EEG/MEG

respectively.

A major advantage of MEG is than it can easily identify through SEF the activities in SII,

where it is difficult for SEP to detect them because of the location and orientation of dipole

sources [Hari e al., 1990, Hari e al 1993, Mima et al. 1997]. The SII responses are detected

usually 70-100 ms after stimulation [Simoes and Hari 1999, Kakigi 2000; Disbrow et al, 2001,]

but some earlier responses have been reported as well [Karhu and Teshe, 1999; Korvenoja et

al. 1999]. The relative timing, interactivity and characteristics as plasticity in SI and SII areas

have been recently studied using single trial SEF [Ioannides et al. 2002a]. The pathophysiology

of somatic sensation also started to be explored [Ioannides et al. 2002b].

6.2 Methods

Our main goal is to study in parallel the temporal and spatial development of the main

somatosensory response as well as the smaller activations surrounding the primary focus in the

rest of SI area bilaterally. For this purpose we analyze the spatiotemporal distribution of

current density following arm and foot stimulation around BA3b and for the second

somatosensory area. We examine the activations at latencies between 10 ms and 31 ms after

stimulus onset for the arm stimulation and up to 100 ms for foot stimulation.

The activation data are provided by MFT analysis [Ioannides et al, 1990] of the MEG signals

or BESA analysis [Scherg, 1990] of EEG signals. Tomographic reconstructions of the current

source distributions are obtained in general from MEG but also a dipole solution is used for

comparison purposes. Independent software was used also for generating dipole solutions from

EEG data, as well as for generating the MEG cortical and statistical parametric maps.

The cortical segmentation is performed using CORTSEG.

Areas normally not visible on the surface representation of the segmented brain can be exposed

if instead of the cortical surface, selected brain structures or cortical patches are used. Such

selected structures were the central sulcus (CS), the pre- and postcentral gyri (PreCG and

PostCG), the area around the calcarine sulcus, etc. For the first study, the STRUCTSEG

module has been used to segment the sensory and motor strip, as well as the central sulcus. For


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the second study the area around the calcarine sulcus, including the V1 visual cortex has been

segmented. The VISIO module has been used to display and analyze the activation data,

resulting from MFT analysis of MEG data or BESA analysis of EEG data.

The activation maps may consist in: a) either cortical current density maps or b) statistical

parametric maps, basically the p factors obtained following t tests or Kolmogorov-Smirnov

tests between two conditions. The values of the modulus for the current density vectors is

shown in a color scale ranging from yellow to red, where red are the most significant activation

values. A different colorscale is used to convey information on the direction of the cortical

density, by means of color coding. Current having the same direction with the surface normal

are shown in the yellow to red scale. Those in opposite direction are shown in the green to blue

scale. The same color scale is used for the statistical parametric maps. The red and the blue

colors correspond to the most significant values for positive or negative values respectively.

At first we examine the electrical activity on the background of the segmented cortex surface

and then on the background of the segmented brain structures. We will use a very low

threshold for the activations to emphasize the distributed nature of the solutions. Note however

that in all the reported investigations a much higher threshold was used and statistical

significance was computed for each grid point activations. Since we are primarily concerned

with the display capabilities of SAV we chose the lower threshold to allow the reader to

inspect the ability of SAV to extract widely distributed forms of activations. We will then

discuss the relative advantages of these alternative visualization modes for different case

studies.

6.3 Results

6.3.1 Electrical stimulation of nerves in the limbs of normal subjects

Numerous studies have demonstrated that the first significant volley of activity after

somatosensory stimulation arrives about 20 ms after the onset of median nerve stimulation and

in about twice as long for foot stimulation [see section 6.2]. The main activation is in

Brodmann area 3b (BA3b), on the anterior bank of post central gyrus, buried inside the central

sulcus, so usually it is difficult to study with precision its topography and temporal evolution.

In earlier studies it was enough to show that the activity was in the vicinity of BA3b at roughly

the right place according to the homunculus representation of the body.

In the first application we study the activation maps obtained following stimulation of the

median nerve at the wrist. The current density map overlapped on the cerebrum surface [Figure

6.3-1] shows that in response to left arm (LA) stimulation of the hand at the wrist a strongly


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activated area is centered around the contralateral SI hand area and that there is also a lower

activated area, possibly corresponding to SII in subject 1. For subject 2 the statistical maps

following right arm (RA) stimulation at the hand indicate again high activity in the SI at early

latencies (B), hint to the possibility of an early activation in SII. At later latency (87ms) strong

activations appear in both SI and SII contralaterally, while some activation of the lower

postcentral gyrus, possibly corresponding to the ipsilateral SII, may be activated (C) in addition

to the contralateral somatosensory cortex (D).

Figure 6.3-1. (a) Current density map evoked by left arm (LA) stimulation at the median nerve for subject

J.R., 22ms after stimulus onset, threshold 25%, normalized per time slice. The activation values are

expressed in arbitrary units (au). The outline of the CS is shown in pink. b), c) and d) statistical

(Kolmogorov-Smirnov) KS parametric maps obtained for RA of subject R.B. at latencies 27ms, (b) and 87

ms (c from a right lateral view, and d from the left lateral view). The colorscale indicates the hyperactivity

in the “warm” color range and the hypoactivity in the “cold” color range. The signed, modified p factors

are displayed. A low threshold is used: 10% corresponding to p < 0.09.

We expect the main generator to be buried within the sulcus, probably in area BA3b and

therefore not directly visible from such a view. The projected activation does not appear very

focal at this threshold and the deeper the source is the larger the cortical area where it projects.

For this reason we aim to obtain a better insight by studying the same activation data in the

background of anatomy restricted to the segmented somatosensory strip. Specifically the

anatomy represents the anterior wall of the postcentral gyrus, the central sulcus and the

posterior wall of the precentral gyrus, segmented as shown in Figure 6.3-2 a. This figure shows

features extracted from the time course of activity following median nerve stimulation at four


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latencies, selected from averaged activation curves. Figure 6.3-2 b shows that at 15 ms there is

no significant (> 20%) activation present on the postcentral gyrus (PostCG) wall facing the

central sulcus (CS). The first response above 20% is visible at 22 ms after stimulus onset

(Figure 6.3-2 c). The hand area and particularly the area BA3b appear activated (Figure 6.3-2 c

and d), in the contralateral primary somatosensory cortex (SI). At 31 ms the response is

stronger and more widespread though still highly localized in area BA3b on the anterior wall of

the PostCG (Figure 6.3-2 d). The current direction is approximately perpendicular to the

surface of the post central gyrus. The color coding helps notice the current reversal (blue-green

for currents entering and red-yellow for currents exiting the cortex) for the 31ms activation

peak relative to the one at 22ms.

An analysis using lower activity threshold (15%) reveals that a more posterior parietal source

appears active at 22 ms (Figure 6.3-2 e), while no activation is noticeable on the anterior wall

at this threshold. Since the posterior wall of the pre central gyrus is activated at 22 ms

following left arm stimulation (threshold at 25%), this indicates that there may be an anterior

activation source (Figure 6.3-2 f).

A smaller activity is noticed in the postcentral gyrus at 26 ms and indicates that the source of

activation in the hand area is located at the top of the gyrus, in the Brodmann area BA1 (Figure

6.3-2 g). A posterior view of the postcentral gyrus, at 31 ms, exposes the two distinct,

separated sources of activation located in SI and SII (Figure 6.3-2 h).

We present results for the left arm stimulation but similar studies have been done for the right

arm stimulation and the last two figures (Figure 6.3-2 i and j) show the central sulci activated

contralaterally to the stimulation. The inferior view is chosen to expose the area BA3a.


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Current density maps (in arbitrary units (au)) over the

postcentral gyrus (PostCG), precentral gyrus (PreCG) or

central sulci (CS), elicited by arm stimulation at median nerve

from averaged data. The data are normalized across all time

moments and for 4 runs for the left arm (LA) and right arm

(RA) stimulation individually. For the latency corresponding

to the maximal activation value, obtained at 31ms the

maximal activity vector is shown (Subject 1). The color scale

of current density at the bottom applies to all images.

a) Details on the segmentation of the PostCG, CS and PreCG;

b) PostCG in anterior, top and right view show no significant

activation (threshold at 20%) at 15ms following left arm

stimulation. The thalamus is included for orientation

purposes.

c) PostCG in anterior view at 22 ms following LA stimulation

show activation (threshold at 20%) in the right hand area,

BA3b

d) PostCG in anterior view at 31 ms following LA stimulation

show maximal activation (threshold at 63%) in the right hand

area. Notice the current reversal for the 31ms activation peak

relative to the one at 22ms.

e) PostCG in posterior view at 22 ms following LA stimulation

(threshold at 15%). A more posterior parietal source appears

active while no activation is noticeable on the anterior wall at

this threshold.

f) PreCG in posterior view at 22 ms following LA stimulation

(threshold at 25%) indicates that there is an anterior

activation source.

g) PostCG in anterior view at 26 ms following LA stimulation

(threshold is 9%) indicate that Brodmann area BA1 is

activated.

h) PostCG in anterior view at 31 ms following LA stimulation

(threshold is 12%) show two distinct, separated sources of

activation located in SI and SII.

i) Central sulcus in inferior view at 31 ms following left arm

stimulation (threshold is 63%) indicate activation in the

contralateral hand area, here the area BA 3a is exposed.

j) Central sulcus in inferior view at 31 ms following right arm

stimulation (threshold is 70%) indicate activation in the

contralateral hand area, here the area BA 3a is exposed.

Figure 6.3-2. Early SEF over the PostCS,

PreCG and CS (subject J.R.).


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Figure 6.3-3 shows the same time sequence of activations (current density maps) as Figure

6.3-2 but from the same (anterior) view and with constant threshold (5%). The data refer to left

arm stimulation at median nerve from averaged data, from 10 to 30 ms after stimulation. The

data are normalized across all time moments and for 4 runs. For the latency corresponding to

the maximal activation value (at 31 ms) the maximal activity vector is shown in Figure 6.3-3.

Figure 6.3-3. Spatio temporal course of activation over the PostCG following LA

stimulation. The time latencies are 1, 15, 22, 26, and 31 ms after stimulation and the

threshold is 5%. Above 20% the first significant activation is seen at 22ms. (Subject J.R.)

Figure 6.3-3 and Figure 6.3-4 show distinct foci of activations after stimulation of the median

and tibial nerves. The foci at the early latencies are at the expected locations for SI for the hand

and foot, on the contralateral post-central gyrus to the stimulated limb. In both figures

activations are seen in locations where the second somatosensory area is expected, at the base

of the postcentral gyrus.

R

L


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Figure 6.3-4. Current density maps (modulus only), following hand and leg stimulation.

The first row shows the results for left arm (LA) stimulation at 22ms after stimulus onset

and 48 and 95 ms for the foot stimulation (LF) respectively. The second row shows the

results for right arm (RA) stimulation at 36ms after stimulus onset and 46 and 75 ms for

the foot stimulation (RF) respectively. (Subject R.B.).

These SII activations are seen either ipsi- or contra-laterally to the simulated limb and usually

at longer latencies, starting at 31 ms for arm stimulation and at 75 and 95 ms for right and left

foot stimulation respectively.

Several studies have addressed the question of serial versus parallel activation of SII (see

[Ioannides et al. 2000a] and references therein) allowing the visualization of very early

activation (Figure 6.3-5).

Using the complementary views available in SAV the SI and SII activations at the earlier

latency of 22 ms are better delineated, showing a distinct SII activation which is much weaker

than the SI activation. The whole brain view on the left column provides only a hint of separate

activations in SI and SII since the main SI focus is deep in the sulcus while secondary

activations appear on the brain surface in a more wide spread fashion. Strategic placing of

coronal, axial and sagittal slices provide an accurate alternative representation of brain

LA 24 ms LF 48 ms LF 95 ms

RA 36 ms RF 46 ms RF 75 ms

1.98*10(-3) au 2.60*10(-4) au 4.80*10(-4) au

2.36*10(-4) au 3.03*10(-4) au 4.06*10(-4) au

4.88*10(-6) au 4.88*10(-6) au 4.88*10(-6) au

4.88*10(-6) au 4.88*10(-6) au 4.88*10(-6) au


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activation data and show conclusively coactivation of SI and SII at the early latency of 22 ms.

The cuts placed to capture best the SI activity are in the middle column of Figure 6.3-5. The

slices placed over the SII area are in the right column of Figure 6.3-5. They show activation in

SII which is weaker than the one in SI but clearly discernible if using a low threshold (25%).

Figure 6.3-5. 3D view with coronal (A), horizontal (B) and sagittal (C) cuts at the level of

SI at the hand area detailed in the middle image column. The current density maps are

elicited by left arm stimulation (the median nerve) at the wrist. The right column shows

slices cut at the level where SII activation may be expected. At this early latency (22ms)

the SII activation is much weaker than the SI but of distinct localization. The threshold is

25%. For color scale see Figure 6.3-3 (Subject J.R.).

SI

SI

SI SII

SII

SII

A1

B1

C1

A2 A3

B2 B3

C2 C3


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6.3.2 Electrical stimulation of the limbs for a paraplegic subject

In a recent study [Ioannides et al., 2002b] the MEG signal was recorded following electrical

stimulation of upper and lower limbs in both normal and paraplegic subjects, with clinically

diagnosed complete paraplegia.

MFT analysis identified foci just behind the central sulcus consistent in location with SI for

foot and arm and SII areas. Activation of the SI foot area was identified in normal and

paraplegic subjects when the arms (above the lesion) were stimulated. Activations were also

identified following electrical stimulation of the lower limbs in both normal and paraplegic

subjects. The surprising activations with stimulation below the lesion in paraplegic subjects

previously diagnosed as complete were found in the SI foot area and/or nearby cortical areas,

they were weak and not well time-locked. Statistical analysis of the arm and foot activations

from patients identified statistically significant activations in extended areas which included

the expected location of the SI area for the arm and foot. In general, the activations in the

paraplegic subjects were extended outside the primary area and for foot stimulation were

identified when large time windows were used in the statistics.

We used SAV and displayed activation data imported from the above study [Ioannides et al.,

2002b] in the background of the segmented post- and pre-central gyri. These displays show the

spread of the activations beyond the postcentral sulcus. The contours bounding statistically

significant activations (p<0.05 or p<0.001) along perpendicular cuts for the arm (red contours)

and foot (yellow contours) are displayed on the whole brain and on the pre-and postcentral gyri

segmentations. The displays show the results at the latencies where the most significant

activations for each case are. For arm stimulation (median nerve, above the lesion) the

significant activation is at similar latencies as for normal subjects, 24ms, but the activated area

extends beyond the expected area for SI. For foot stimulation (tibial nerve, at the ankle) the

significant activation is delayed by 30 to 50ms compared to normal subjects (Figure 6.3-6 ).


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Figure 6.3-6. Contours outlining areas of statistically significant activations are shown

following arm and foot stimulation in a paraplegic patient (subject EP). The first row (A,

D) shows the contours relative to the cortical anatomy (the contours intersect a

transparent brain surface). The central sulcus has been outlined by placing a blue

marking point at the bottom of the sulcus in each MRI slice. Depending on the aspect of

viewing the 3D rendered brain parts of this blue outline may appear as covered by

cortical folding. The lower rows (B, C, E, F) show the same contours overlaid on the


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central sulcus walls (pre- and postcentral gyri segmented together). Shown in red are the

contours obtained at 24ms following right arm (RA) stimulation (p<0.05) and left arm

(LA) stimulation (p<0.001). In yellow are the contours obtained following left foot (LF)

stimulation (75ms, p<0.05) and right foot (RF) stimulation (99ms, p<0.001). The contours

were traced in three planes (sagittal, axial and coronal) for LA; two planes (axial and

coronal) for each of the two activated areas (approximating BA3a and BA3b) for RA and

two planes (axial and sagittal) for LF and RF. B shows the gyri viewed from an anterior-

right perspective with contours from LF and LA stimulation. C presents the same data,

from a posterior-right perspective; the gyri were made transparent to allow visualization

of the areas where the contours intersect the gyri. E shows the gyri viewed from an

anterior-left perspective with contours from LF and LA stimulation. F presents the same

data on transparent gyri, from a posterior-left perspective. Note that the contours extent

into and also posteriorly to the postcentral gyrus. An animation of above figure can be

found in http://nucleus.med.upatras.gr/~alex/sav_results.html.

6.3.3 The primary visual cortex - a combined fMRI and MEG analysis

In a recent study [Moradi et al 2002] the tomographic localization of activity within human

primary visual cortex (striate cortex or V1) was examined using whole-head MEG and 4-Telsa

functional magnetic resonance imaging (fMRI). Circular checkerboard pattern stimuli confined

to part of the lower quadrant of the visual field were used designed to excite the dorsal part of

V1 which is distant from the V1/V2 border and from the fundus of the calcarine sulcus. Both

fMRI and MEG identified spatially well-overlapped activity within the targeted area in each

subject. The mean separation between V1 activation centers identified by fMRI and the earliest

MEG activations around 40 ms after stimulus onset was only 3-5 mm.

SAV was used with activation data from the above study [Moradi et al, 2003] and made

apparent the remarkable agreement between the two tomographic reconstructions in both

location and shape when the MEG and fMRI solutions were displayed together with the V1/V2

boundary on the background of anatomy, as shown in Figure 6.3-7.

The figure shows the activation following the presentation of the stimulus on the lower right

visual field for one subject. For both MEG and fMRI maps of statistical significance are

displayed compared to a baseline condition when no stimulus is presented. The MEG result is

displayed as a filled red (blue) contour for increase (decrease) of activity at a p<0.01 level.

The computation of the statistical significance compares 6 ms windows with similar windows

from the baseline. The comparison is made throughout the post-stimulus period by advancing

the center of the active window in 3.2 ms steps. The latency used in the display is 40 ms and it


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corresponds to the first significant increase in activity in V1. The fMRI activation at p<0.01 is

bounded by the yellow contour which surrounds the red area (MEG area). The yellow line

above the calcarine surface corresponds to the V1/V2 boundary. The display demonstrates that

the fMRI and MEG estimates of increase activity coincide and that the area of precise

correspondence is on the contralateral upper part of the calcarine entirely within V1. Note also

the hypoactivation seen in the MEG statistical comparison on the ipsilateral V1.

Figure 6.3-7.Post-MFT statistical map of current density for the onset of M50 (at 40 ms,

stimulus size 5.2 degrees). Data from [Moradi et al, 2003]. The map is superimposed on

3D rendering of the upper banks of the calcarine sulcus. The highly significant increase of

activity (p < 0.001) is on the left V1 (shown in red), contralateral to the stimulated visual

field. Note also the hypoactivation (shown in blue) on the ipsilateral side. The yellow

outlines represent fMRI contours for statistically significant increases in activity and

V1/V2 contours (Subject V.P.).

6.3.4 Use of anatomical constraints for EEG dipole localization. Application to

central sulcus

We have used SAV to display EEG dipole solutions from plain EEG or simultaneous

MEG/EEG recordings, using BESA and MFT [Figure 6.3-8]. This helps to conceptualize the

relationship between the direction and location of the dipoles to the local anatomy i.e. that of

the somatosensory evoked dipoles in posterior central sulcus to the convoluted surface of the

sulcus.


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Figure 6.3-8. Dipole sources resulting from analysis of MEG and EEG signals presented

in the background of the CS anatomy. The posterior view is at the left, right view at

center and top view at the right. The sources correspond to activation of the BA3b and

BA1 areas. Subject J.R.

In preliminary one case study [Zainea et al., 2002], EEG somatosensory evoked responses

following electrical stimulation of the left median nerve have been recorded using a 64 channel

Neuroscan machine. SAV was used in an effort to provide restrictions of the solutions in terms

of locations and orientations [Figure 6.3-9] for an independent analysis. One thousand epochs

recorded from -50 to 200 ms around the stimulus were filtered (1-290Hz), cleaned from eye

movement and muscle artifacts using ICA (independent component analysis) and averaged.

The electrodes locations were digitized with a Polhemus FastTrack device and converted to

MRI coordinates. The postcentral gyri contralateral to the stimulation site have been segmented

and a smooth mesh of the surface has been obtained using SAV. The locations of the surface

vertices and the normals characterizing the local anatomy have been brought into the same

coordinate system as the functional data. Source localization has been performed linearly

fitting over the middle latency interval (20-50) ms of the average SEPs the amplitudes of any

two dipoles taken from the set of surface normals. Two major sources of mid-latency electrical

activity were found as a result of an exhaustive search over the dipole candidates provided by

the local normals. The sources locations (Figure 6.3-10) correspond to the anatomical brain

areas BA3b and BA1 of contralateral primary somatosensory cortex.

EEG

EEG MEG

EEG

MEG

EEG

ME

EEG


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Figure 6.3-9:Segmented postcentral gyrus with associated local normals. Subject O.Z.

Figure 6.3-10: The dipole solutions corresponding to sources in area BA3b (upper row) and area BA1

(lower row). Images produced with independent software produced by Marc Schellens and modified by

Ovidiu Zainea. Subject O.Z.

We made use of the detailed knowledge of anatomy to refine the accuracy of the localization

process by confining the search within the area of interest, the contralateral postcentral gyrus,

and the orientation to the columnar orientations characteristic to the cortical sheet. The use of

an exhaustive search method instead of a local nonlinear search avoids the local minima,

leading to a more accurate result.


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6.4 Discussion

High resolution imaging of structures extracted from MRI is helpful in analyzing activation

patterns within the hand area of the central sulcus, or at the larger scale of the somatosensory

strip, because the distinct cortical areas involved can span less than 1cm in the anterior-

posterior plane [White et al., 1997]. The 3D visualization offsets the ambiguity introduced by

the curved path of the central sulcus and the neighboring gyri, which penetrates 2D slices at a

variety of angles, and make the precise assignment of activation to specific sulcal or gyral

regions difficult [Gelnar, 1998] .

Because cortical morphology is organized along circumvolutions one can use SAV to avoid

restricting the analysis of the functional topography along the surface of the gyrii and exploit

the information located on deeper structures like sulcus walls and fundii. These have potential

to reveal some stronger inter-individual regularity in their spatial organization rather than

according to their traces along the cortical surface only [Sastre-Janer et al. 1998].

We have used SAV to help the conceptualization of the spatial relationship between equivalent

current dipoles (ECD) and the local anatomy and we give an example from the combined

MEG/EEG study on the somatosensory system. The local anatomy refers in this case to the CS

anatomy.

The previous studies using the slice representation of activity data projected onto the anatomy

often fail or have difficulty to convey the spatial relationship and the time interactions between

various areas involved in the sensory processing. The separation of activations in adjacent

areas requires careful selection of slices, but even the best choice of slice views often fails to

represent the large scale relationship between the activated brain areas. In recent studies of SI

and SII activations in normal [Ioannides et al., 2002a] and paraplegic [Ioannides et al., 2002b]

subjects early activation of SII was in evidence but the distinction and relationship between the

SI and SII activations so identified was not easy to make in terms of images of activations in

any one or more slices. We demonstrate here the advantage of the SAV representations for the

visualization of separate activations in SI and SII somatosensory areas and in the parts of SI

representing arm and foot from these two studies.

The idea of serial processing in the somatosensory cortex is supported by the organization of

the cortex into large, somatotopically organized primary receiving cortices which project

information to smaller association areas. In support of this idea come the patterns of responses

to median nerve stimulation recorded by MEG sensor arrays. Initial activation in SI about 20

ms after stimulation, followed by processing in SII at approximately 100 ms. However, the


122

anatomical connectivity of the somatosensory system also suggests simultaneous participation

of widely separated cortical areas in the early processing of sensory input.

We have analyzed the early evoked somatosensory fields and shown focal activations in the

hand area BA 3b with peaks at 22ms and 31ms, in between which happens a reversal of current

The SI activation is accompanied by a weaker activation in the SII area or the parietal

operculum as seen from a lateral view of the cortical surface. These areas, usually viewed as

higher order processing areas for somatosensory perception appear coactivated with SI during

the early processing of somatosensory input.

While SII appears activated bilaterally [Simoes and Hari, 1999; Ioannides et al, 2002a] it

maintains a clear contralateral dominance, supporting the view that it plays a role in supporting

a unitary body scheme by integrating information from the two body halves.

The combination of high resolution MR imaging with the MEG data, as implemented in SAV,

has shown the possibility to discriminate between closely situated but highly localized, distinct

sources of activity in the somatosensory (SI/SII and BA3b/BA3a) and visual system (V1).

Finally, we examined the potential of SAV in pathophysiological studies, specifically in a

study on paraplegia.

Mainly the current density maps but also statistical maps are used with SAV. Multimodal

information, extracted for example from fMRI, can be incorporated and allows comparing the

localization of activities across these modalities. We use the contour options in different ways

to demonstrate how different types of functional information can be superimposed on the

relevant 3D anatomical context.

Chapter 7. A Software Tool for Interactive Determination of the Plane of Cut through the Rat

Brain

123

Chapter 7. A Software Tool for Interactive Determination of

the Plane of Cut through the Rat Brain


Brain ......................................................................................................................................... 123

7.1. Introduction .......................................................................................................... 124

7.2. Methods and materials ......................................................................................... 124

7.2.1. Reconstructing the rat brain (structures) based on atlas images .................. 124

7.2.2. Search protocol............................................................................................. 126

7.3. Results .................................................................................................................. 127

7.4. Practical solution .................................................................................................. 128

7.5. Discussion ............................................................................................................ 129

7.6. Conclusions .......................................................................................................... 130

The aim of the present work is to use visualization tools to help planning an

electrophysiological experiment on rat brain slices. Namely we aim to provide an interactive

tool for finding the plane of cut through the rat brain which yields the maximum section

through a selected brain structure or a combination of structures, i.e. a path of nerve fibers and

the area they are known to connect to.

A set of two-dimensional coronal images from a rat brain atlas is preprocessed. The structures

of interest, landmark structures, and the bulk of the brain are assigned specific gray levels, and

the images are registered.

Any rat brain atlas can be used to provide the desired set of two-dimensional data used to

reconstruct the brain volume. Selected rat brain structures can be analyzed with this method,

provided that the preprocessing of the atlas images is performed. As a first application of this

method we have identified a set of parameters for which a 0.5 mm thick slice can be cut

through the rat brain so as to include as many as possible intact connections between the fornix

fibers and the mammillary bodies.

The same type of application could be extended to visualization of histochemical and

autoradiographic data.


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7.1. Introduction

The classical rat brain atlases provide images at either coronal, sagittal, or horizontal planes.

Frequently a different orientation of sectioning is desired for optimal visualization of specific

brain areas and pathways stained with specific markers (including Horse Radish Peroxidase

staining of axons, receptor autoradiography, etc.). This need is also currently emerging with the

advent of experimental electrophysiology studies in vitro [Agmon and Conners, 1991;

Dingledine et al., 1980].

We aimed to develop an interactive tool for finding the plane of cut through the rat brain which

would yield the maximum size section through a selected brain structure or a combination of

structures, i.e. a path of nerve fibers along with the area they are known to connect to. Such a

tool would assist in the planning of in vitro electrophysiological studies in brain slices, by

determining and graphically illustrating the optimal plane of brain sectioning. The latter should

be better defined interactively and expressed in an operational way, i.e. the position and angle

of the plane in reference to the three planes of stereotaxic coordinates as well as to visible brain

landmarks.

An important circuitry within the limbic system relies on the transmission of information from

hippocampus via fornix fibers to the mammillary bodies (MB), a paired nuclear mass in the

basal part of the brain [Kostopoulos and Phillis, 1977]. In vivo studies established the

excitatory nature of the fornix input to the MB; but were unable to investigate the detailed

synaptic mechanisms and possible plasticity of this connection. Such precise studies demand

the experimental convenience of brain slices maintained in vitro. However, due to the tortuous

course of fornix (see Figure 7.3-1), slices cut at any of the three conventional stereotaxic planes

do not yield functional connections between fornix and MB neurons. We therefore tested the

ability of the developed software to help finding the plane of cut through the rat brain, which

would preserve as much as possible intact connections between the fornix fibers and the MB.

7.2. Methods and materials

7.2.1. Reconstructing the rat brain (structures) based on atlas images

The present approach involves two initial steps, the preprocessing of the atlas images and the

volume reconstruction in order to determine the proposed plane of cut, offered to the

experimenter for further interactive refinement of the optimal plane.

The resources employed consist of a stereotaxic rat brain atlas [Paxinos and Watson, 1997], an

image processing software (Paint Shop Pro) and the IDL computing environment.


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76 coronal images spaced at an average distance of 0.3mm along the anterior to posterior axis,

from the Paxinos and Watson rat brain atlas are the input data. A prerequisite for using these

images is that they should be in digital format and registered. The first condition is fulfilled by

the atlas images, while registration of the images is achieved with reference to grid points of

known stereotaxical coordinate, which are identified in all used atlas images. The images are

assumed to be aligned.

Figure 7.2-1. The reconstruction of the brain and selected brain structures starts with: (a) the original atlas

image (from [Paxinos, 1997]); (b) This is preprocessed and the structures of interest are assigned specific

gray levels (F = fornix, MB = mammillary bodies, H= hippocampus, R = the reference point used for

registration; The results consist in (c) reconstructed cerebrum and , (d) mamillary bodies and part of the

fornix. A=anterior, P=posterior.

In the next preprocessing step the brain contour and the structures of interest (MB, fornix, and

hippocampus) are isolated and specific gray levels are assigned to each of them [Figure 7.2-1 a

and b].

The resulting contours are stacked and used for creating two volumetric data sets: the whole

brain (with a low resolution), and the volume of interest comprising the fornix and MB (with

high resolution).


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126

Paint Shop Pro is used in the preprocessing step. IDL programming is used to reconstruct the

outline of the selected structures (Fig. 7.2-1 c and d) and planes of arbitrary orientations

through the volume.

Bilinear interpolation is used to generate intermediary slices in order to obtain isotropic voxels.

We developed software to interactively find the plane of cut that contains the maximum area of

selected structures, in this application the MB and fornix.

7.2.2. Search protocol

The process of finding the plane that preserves the maximum intact connections between the

fornix and the MB is summarized in Figure 7.2-2. The problem is reduced to finding the plane

that (a) contains the maximum area of fornix, and (b) intersects the MB. Another search can be

made for obtaining a plane that contains the maximum cross-section with the MB.

Figure 7.2-2 The search protocol

The data set containing the whole brain is used in the beginning, for selecting the structure of

interest. The purpose is to find the sectioning plane including a maximum area of this structure.

The viewing parameters can be varied to get information about the shape and location of the

structures

The actual search is performed within the volume of interest containing the fornix and the MB.

The first sectioning plane is selected intuitively, placing the mouse cursor at the desired

location. The software returns the plane parameters with reference to the bregma, a landmark

Automatic orthogonalscanning

Selected plane with max area

Mouse selectrotation center

Select varyingparameter

Automatic oblique scanning

Stop

Select thestructure of interest

Volume of interest

Draw plane through brain volume

Brain volumeStart

Area=max? (Y/N)

YN

Oblique scanning


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on the skull. These parameters are the three coordinates of the plane normal origin, and the

angles made by the plane normal with the axes.

The volume is scanned with a fixed step, parallel to one axis, and the plane containing the

maximum area of the structure is redrawn. The maximum area is held in memory for future

reference.

An oblique scan is performed starting from the plane previously found: the origin of the plane

normal on the cut plane is selected with the mouse and the plane can be rotated around either

axis or translated with a fixed step. The cut areas are compared with the maximum value and

the plane with maximum area is held in memory, with its associated parameters.

In case the suggested sectioning plane is considered not feasible or convenient to be cut by the

experimenter in practice the search procedure is restarted.

Once the best orientation of the sectioning plane is found in the volume of interest, the same

plane is drawn through the whole brain volume.

The sectioning plane parameters can be varied as follows: a) varying the plane orientation,

while keeping constant the distance from the plane to the origin of the coordinates system

(bregma in the present case); b) varying the distance from the plane to the origin of the

coordinates system, while keeping the same plane orientation; c) automatically scanning the

volume along a chosen axis; d) automatically scanning by rotation around a chosen axis.

7.3. Results

The proposed software tool was used for three-dimensional visualization of the brain and

structures within the brain: MB, hippocampus and fornix fibers, providing visual clues about

the spatial orientation of the selected structures (Figure 7.3-1).

Figure 7.3-1: View of the volume of interest (A =

anterior, P = posterior, L = left, R = right, D = dorsal, V

= ventral; F = fornix, MB = mammillary bodies, H =

hippocampus)

H

F MB


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We have applied the method to analyze the fornix and MB. We have identified the parameters

for which a 0.5mm thick slice can be cut through the rat brain so as to include as many as

possible intact connections between the fibers in the fornix and the MB. Suggested parameters

of the cut plane are given relative to the bregma and with reference to the right-to–left (RL),

dorsal-to-ventral (DV), posterior to–anterior (PA) axes: the coordinates of the plane normal

origin: 1.4mm left of bregma, 8.8mm ventral and 4.8mm posterior, angles made by the plane

normal with the axes: 113o relative to RL, 148o relative to DV, 71o relative to PA. These angles

correspond to the angles made by the cut plane with the sagittal plane, the horizontal plane and

the coronal plane respectively.

Figure 7.3-2: Suggested cut plane containing MB and the maximum area of the right fornix column

connected to MB: a) ventral view, b) dorsal view, exposed plane (F = fornix, MB = mammillary bodies, H =

hippocampus)

7.4. Practical solution

In order to facilitate the precise sectioning at the desired orientation we can now suggest:

a) To construct two prisms from Plexiglas, of 25mm height. Both should have as bases right

angled triangles, and two sides, while perpendicular to the bases, should form between them

angles of 320, and 190 respectively for the second prism. These angles are derived taking into

account: a) the fact that the cut plane is perpendicular to the local normal and b) the position of

the brain as it is going to lye on the prisms.

b) To perform first a midsagittal cut, and for the right hemisphere (to which our results pertain)

another parasagittal cut (Figure 7.3-2 a).

Lay the right half of the brain on the parasagittal plane, the medial plane facing upwards as

shown in Figure 7.3-2 b.

Place the brain, oriented as described previously, on the first prism with angle equal to the

angle made by the cut plane with the ventral to dorsal axis, 320 in this case (see Figure 7.3-2 b).


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The cut is made vertically, parallel to the edge of the prism whose direction coincides now with

the posterior to anterior axis. Using the second prism, placed upon the first, the sagittal plane is

rotated around the left to right axis by an angle equal to 900 minus the angle made by the cut

plane with the posterior to anterior axis (710), 190 in this case. The dorsal part of the brain is

made parallel with the hypotenuse of the prism, as shown in Figure 7.3-2 b. Several slices are

to be cut, as indicated by the direction of the dashed line (i) in Figure 7.3-2 b in order to find

the plane with maximum right fornix area, and passing through MB.

Figure 7.4-1.a) Initial cuts through the rat brain are made midsagittaly and parasagittaly (image mdifed

from [Paxinos, 1995]); b) The right half of the brain is laid on a prism whose angle is the angle the

sectioning plane makes with the ventral to dorsal axis (the midsagittal plane (M) facing upwards, and the

parasagittal plane downwards), and rotated around the left to right axis by placing the dorsal part of the

brain tangent to the hypotenuse of the prism whose angle is equal to 90o minus the angle the sectioning

plane makes with the anterior to posterior axis. The dashed line (i) indicates how the sectioning is done.

7.5. Discussion

An interactive software tool is proposed to find the plane of cut through the rat brain, which

would preserve as much as possible intact connections between the fornix fibers and the MB.

The interactivity is advantageous since there might be no unique solution to this problem, and

the solution needs to be practical for the experimenter.

Te sectioning plane coordinates are given relative to bregma but, since this landmark is not

visible anymore after extracting the brain from the skull, the results would be better computed

relative to anatomical landmarks visible on the brain surface.

a) b)


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Insufficient data and unequally spaced images have been acknowledged as sources of

inaccuracy. The coronal slices are spaced at an average of 0.3 mm, leading to a poor resolution

on the anterior-posterior axis compared to the coronal plane. Small structures are therefore

likely to be misrepresented. Atlases often provide unequally spaced slices. This reduces the

accuracy, since one has to interpolate between existing slices thus introducing interpolation

errors.

7.6. Conclusions

The proposed software tool would be of help in electrophysiological studies of synaptic

relationships in brain slices maintained in vitro. Before a real experiment, the plane of cut

would be determined and illustrated using the proposed software tool.

Any rat brain atlas can be used to provide the set of two-dimensional data used to reconstruct

the brain volume. The limitations of a 2D viewing system are overcome by the use of software

tools. Planes of arbitrary orientations can be reconstructed, offering visual clues not evident in

classical two-dimensional atlases.

Selected rat brain structures can be analyzed with this method, if the preprocessing of the atlas

images is performed. This includes segmentation and cleaning of the background. A further

application would be to construct devices to support the rat brain in the desired orientation

[Dingledine et al, 1980], improving the precision of sectioning at the specified parameters.

Further development of the application will focus on automating the process and it is possible

to extend it to other structures.

Actual experiments on rat brain slices are planned in order to validate the approach. We also

plan to extend the use of the present software tool to visualization of combined histochemical

and receptor autoradiography data.

Chapter 8.General Discussion

131

Chapter 8. General Discussion

Chapter 8. General Discussion..................................................................................................131

8.1 Programs design ........................................................................................................132

8.2 Contributions of SAV to understanding brain function, complementing and

integrating the relevant techniques........................................................................................135

8.2.1 Source localization and extent...........................................................................135

8.2.2 Source separation ..............................................................................................135

8.2.3 Spatial resolution of electrophysiological techniques.......................................136

8.2.4 Comparison of multimodal data........................................................................136

8.2.5 Morphometric studies........................................................................................136

8.3 Applications ..............................................................................................................136

8.4 Outlook......................................................................................................................137

The problem of understanding brain function and its relation to the cortical topography has

challenged people with different backgrounds and various methods have been developed to

approach this question. The development of these methods was boosted by improvements in

techniques like EEG, MEG, fMRI, or PET. The use of increasingly powerful computers

reduced the time required for analysis of large amounts of data pertaining to brain activation.

Historically EEG was the first technique used for monitoring brain function, either invasively

or noninvasively using electrodes pasted onto the head. At a different level the electrical

potentials recorded from brain slices give useful information regarding the normal or

pathological functioning of specific circuits. In the minor part of this thesis we developed an

application (Chapter 7) designed for facilitating such an experiment on the rat brain by

providing an optimal plane of cut which would preserve as much as possible intact connections

from the fornix to the mammilary bodies. Even if the practical realization of this slices proved

to be difficult it was useful to construct a tool for visualizing the tortuous circuitry of the fornix

and the relationship to the mammilary bodies. A similar effort like the one we have put into

analyzing the circuitry from the hippocampus, via fornix to the mammilary bodies can be put


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into constructing a complete 3D rat brain. Eventually the same methodology we proposed for

the mammillary bodies can be applied for the study of other brain structures’ roles.

Besides time series analysis one needs visualization tools to be able to relate the time

development of activation to the cortical topography. In the major part of this thesis we present

methods for imaging and analyzing the human brain based on structural information from MRI

and functional data from MEG and EEG signal analysis. These methods are implemented into a

software package called SAV, its name being the acronym for Surface Activation Visualization.

SAV has been designed specifically, but not exclusively, for the display of vector fields like the

current density vector extracted by MFT analysis of MEG data, allowing the observation and

comparative evaluation of the space-time characteristics of activations in specified brain areas.

The following section discusses aspects related to the software design (Section 8.1) which

implements methods for brain structure segmentation and visualization of surface activation.

The choice of the methods and the relative advantages and disadvantages are discussed. Section

8.2 discusses how SAV can contribute to enhancing the understanding of brain function

through the use of information on the brain topography. Finally Section 8.3 discusses the

specific applications where we have used SAV.

8.1 Programs design

The present thesis introduces a collection of software programs (SAV) for analysis of brain

activation. SAV is intended to comply with three main requests: a) portability across platforms

and this was achieved by selecting IDL as the programming language; b) an object oriented

design and a modular structure to allow reusability of functions and the easy addition of new

modules; c) high interactivity so that a user can adjust the parameters of analysis and expose the

results in a way that provides significant information. A multitude of options for analysis exists

and they can be combined in various ways for a maximum flexibility.

Two of the software modules are dedicated to brain segmentation: (i) semi-automated

segmentation of the brain from MRI scans with CORTSEG and (ii) manual or semi-automated

segmentation of identifiable cortical or subcortical structures with STRUCTSEG.

The problem of brain segmentation remains challenging due to the complexity of the structure

of interest and the imperfect nature of images. Manual segmentation is extremely tedious, prone

to operator bias and has relatively low reproducibility. Still it is considered the gold standard in

most cases when the accuracy of a segmentation algorithm is evaluated. We have adopted an


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interactive approach, which enables to successfully segment the brain from MRI. The

combination with the editing program allows correction of eventual errors from the

semiautomatic segmentation. Also, the editing procedures available in STRUCTSEG could be

used for eliminating topological defects (handles or holes) in view of further processing of the

surface, for example for flattening.

The third SAV module is VISIO. This allows visualization of electrophysiological activation

data on the background of the anatomy provided by the other modules. We normally employ

the gray matter surface for visualization but also the white matter visualization has advantages

when the sources are buried within cortical folds. To extract the white matter surface (the

WM/GM interface) we use a fuzzy c means approach, modified so as to include neighborhood

information and compensate for inhomogeneity artifacts. The negative effects of the

inhomogeneity on the segmentation can also be alleviated by the use of anisotropic diffusion

filtering.

We chose a surface based approach since it works faster compared to a volume based approach.

The data enclosed by the brain boundary are not used for 3D rendered but are available for

inspection in slices cut through the brain.

SAV has been tested and used in two laboratories and its results have been compared with the

parallel 2D representation of activations produced with other software.

A couple of limitations should be acknowledged. First, it is in the nature of this method to

display just the surface representation and therefore relatively deeper lying sources (provided

they are strong enough to “reach” the surface) will project on a larger area, in a more diffuse

way, compared to equally strong but superficial sources. It is therefore acknowledged that the

accuracy is best for superficial sources, like those we demonstrate in the calcarine gyrus. On the

other hand, SI activations displayed on the surface of the 3D-rendered whole brain can be

misleading. Complementary approaches like the display on the segmented posterior wall of CS

or the display in orthogonal slices can resolve the ambiguities.

Depending on the nature of the deeper sources SAV affords the use of a set of complementary

approaches towards an accurate representation of their activity. Important for the performance

of these approaches are the functions of normalization, thresholding and color coding of

activity across different sets of data, along with the possibility to set the depth from which a

source can still project onto the surface, so that one can compare surface and deep activations.


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A second limitation is the fact that the surface activations are mapped onto a dense mesh, of

resolution comparable to this of the MRI and therefore greater than the one of functional data.

The interpolation causes smearing of the data. The amount of smearing can be controlled by

adjusting the depth parameter, which represents the maximum distance from the surface vertex

where from activity can be picked up. Different interpolation options allow for a sharper or

smoother point spread function.

Among the advantages offered by SAV we count the multitude and complementarity of

approaches allowing a more global view on the relationships – both biophysical and

physiological – between the displayed activations.

Activation data consisting in cortical current density maps can be mapped onto the surface

taking into consideration the local surface orientation or just the scalar properties. Also

statistical parametric maps can be displayed on the brain.

The current density vectors can be displayed as they are, or restricted to their projection along

the local normals. The latter approach carries potential in several directions: exploring the

biophysical basis of evoked potentials generators, deciding whether the displayed source is a

pickup from a remote strong source or is indeed generated in the analyzed region, and

differentiating sets of independent components of the response possibly linked to independent

generators etc.

The analysis of cortical activation data is helpful in identifying the multiple sources involved in

a specific task but requires a lengthy analysis. This can be complemented with the analysis

restricted to areas of interest, occasionally buried within sulci or lying deeper in identifiable

subcortical areas or even lying outside cerebrum (cerebellum, brain stem, etc.).

The anatomical information contained in SAV can be used to generate models for the conductor

and for restricting the solutions to the inverse problem.

SAV demonstrates the feasibility of a diverse range of display capabilities of structure and

function through the features and options implemented, while offering a high level interactivity

with the user. We believe that careful manipulation of the visualization and analysis parameters

may reveal information which otherwise will be lost if using standard, fixed parameters and

analyzing only the statistical maps.


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8.2 Contributions of SAV to understanding brain function,

complementing and integrating the relevant techniques

The main focus in SAV is on using information on the brain topography to enhance the

understanding of its function in several ways: a) source localization; b) separation of

concurrently, simultaneously activated sources (i.e. SI and SII); c) evaluation of the spatial

resolution of electrophysiological techniques; d) comparison of multimodal data, i.e. MEG,

EEG and fMRI .

8.2.1 Source localization and extent

The source localization problem can be addressed by examining the cortical current density or

the statistical maps in the background of the segmented brain or for better access to areas

hidden otherwise from the viewer in the background of a selected anatomical region. The two

separate modules dedicated to segmentation, CORTSEG and STRUCTSEG can be used for

best results alternatively (i.e. the ventricles can be segmented based on morphology, using

CORTSEG) or in combination (i.e. to edit the segmented brain using STRUCTSEG).

The anatomical information which can be extracted from SAV can be used: a) into the model

used for the inverse/forward problem; b) to incorporate constraints into the inverse problem.

The color coding helps identify the areas of high activation and thresholding enhances the

appreciation of the source location and extent. Superficial and focal sources are most accurately

represented using SAV in comparison with deeper sources which are more imprecisely

localized, and project their activity onto the cortex in a more diffuse way, spread over an area

increasingly larger.

8.2.2 Source separation

The separation of sources benefits from the use of high resolution representation of the

anatomy, as is the one obtained from MRI. Various interpolation functions help discriminating

between close sources. Two distinct activation sources can be separated based on information

on the source orientation. This is possible since the activation data can be mapped from the

volume onto the surface taking into consideration the scalar and also the orientation with

reference to the local surface normals.


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8.2.3 Spatial resolution of electrophysiological techniques

One can directly compare the location and extent of sources in the background of anatomy and

decide on the power of different techniques to discriminate between distinct sources.

8.2.4 Comparison of multimodal data

Besides the use of distributed or dipoles solutions for the MEG or EEG sources we imported

into SAV contours delimiting areas of statistically significant activations from fMRI studies.

These can be presented in the background of the segmented brain structures (i.e. central sulci or

the area around the calcarine fissure) and overlaid onto the MEG based activation maps. These

studies complement each other and give indications on the possibility of achieving high

resolution in localizing sources with MEG (see Chapter 7).

Additionally landmark points or structures derived from the anatomy can be generated within

SAV and kept in the background as a reference (the use of central sulcus outline or nerve exits

from the brain stem to point to the relevance of the activated areas).

8.2.5 Morphometric studies

Simple morphometric studies on the segmented brain structures are feasible using SAV and

allow qualitative but also quantitative comparison between normal and pathology affected

brains in terms of the total volumes or surface areas of the segmented structures.

8.3 Applications

The areas where we have applied SAV encompass both normal and pathological aspects of

brain functioning. We addressed the issue of normal brain processing of sensory information in:

a) somatosensory evoked fields/potentials elicited by median nerve and tibial nerve stimulation

and b) visual evoked fields. We used SAV to illustrate aspects of somatosensory processing in a

paraplegic patient.

It is essential to be able to use complementary approaches, as those offered by SAV. It is often

the case that the high values of the activations are hidden beneath the visible cortical surface. In

such cases a combination of displays on surfaces in all three of (a) 3D whole brain renderings,

(b) appropriately cut brain slices and (c) segmented specific brain structures may be needed in

order to expose the gyri, sulci or nuclei whose view may be otherwise partially obstructed and


137

thus visualize the separate sources. We have shown this with the example of the SI and SII

activations.

Another application makes use of the surface description in terms of the local geometry and the

orientation of the patches.

SAV was found as potentially useful in studying questions of pathophysiology. Paraplegic

patients clinically evaluated as having suffered a complete spinal cord injury surprisingly do

display evoked potentials following stimulation of the body below the lesion, and the responses

reach statistical significance in the appropriate contralateral SI leg area [Ioannides et al.,

2000b]. The responses are however much weaker than normal and appear rather irregularly,

possibly representing post lesion plasticity mechanisms of the brain operating on scarce

remaining original or alternative afferents. Displaying the space-time development of stimulus

evoked activations with SAV offers therefore an opportunity for studying how human brain

copes with massive deafferentation of a specific cortical node and more specifically the extend

of reorganization and partial takeover by projections from healthy parts of the body

[Bruehlmeier et al., 1998]. Such knowledge is critical for developing new approaches to

rehabilitation of paraplegic patients. Specifically, SAV is well suited to help such studies by

providing comprehensible visualization of the ms by ms extend of activations evoked by

stimulating body parts below and above the lesion along with the statistics of these activations

and aid their comparison on the background of the gross brain anatomy which may or may not

be deformed. One trend is evidently amenable to scrutiny with SAV, that of more wide spread

representation of feet responses in the posterior to CS parietal cortex in the paraplegic patients.

To the extend that this trend has prognostic value [Green et al., 1998], SAV may help in

patients screening and progress follow-up.

8.4 Outlook

Most of the problems in source localization arise from the fact that there are usually several of

them contributing to the field of the same brain area in overlapping time periods and therefore

besides their identification and separation in space one is called to disentangle the biophysical

and physiological interactions of these sources. Different modeling, statistical, correlational and

other neuroinformatics methodologies can be employed for this goal as long as a common

framework of reference and correlation is used. The latter can be provided by visualization of

all results on the anatomy of the brain, and here SAV can be useful.


138

One of the possible applications of an integrated environment, comprising segmentation,

functional data analysis and visualization is based on the observation that it is possible to

discriminate (based on a mask of the segmented brain) between which source points fall within

the brain volume and which fall outside. We have developed within SAV a software routine

that can perform the intersection between the segmented anatomy and the source space and we

hope this could help define the source space boundaries.

Further refinement of our segmentation approach, like correcting for topological errors would

allow us to proceed to producing flat mesh representations of the surface, exposing thus the

activity buried in the sulci.

Starting from the initially segmented structures one may study morphological differences across

normal and pathological cases in diseases like Alzheimer’s disease, schizophrenia, epilepsy,

paraplegia, etc., as well as in longitudinal studies. Right now a simple volumetric (or surface)

analysis is straightforward using SAV.

We have used a description in terms of curvatures of the extracted anatomy (see Chapter 3).

These descriptions might be useful for registration or shape analysis purposes, in comparing

healthy versus pathology affected brains.

The normalization across multiple activation data files can help comparing studies across brain

areas, time slices, experiments and subjects, if the data are properly transformed.

The information on the vector properties of functional data could help deciding whether the

displayed source is a pickup from a remote source or is indeed generated in the analyzed region.

A further direction of work would be to examine the effect of using finer grids with current

density sources. SAV may also be found useful in the study of some physiological questions

which involve space-time relationships, like that of surround inhibition in sensory projections

[Mountcastle and Powell, 1959; Welker et al., 1993].

Among the complementary approaches, that of visualizing the projection of currents along the

local normals of segmented surfaces is the most dynamic approach to many source localization

problems and has been a major impetus in developing the present software tool. It is based on

the idea that most MEG dipoles are created by currents running in the same direction as the

apical dendrites of cortical pyramidal neurons, i.e. normal to cortical surface [Ioannides 2001;

Michel et al., 2001; Hari 1999; Stern and Silbersweig, 2001]. Given the columnar organization

of the cortex, this may be a natural constraint on the cortical MEG sources. Indeed in the

presented example, the direction of the main current flow at the peak activation latency in SI


139

(entering the cortex perpendicularly to its surface, as seen in Figure 7.3.2) is consistent with an

intracellular current flowing from top to bottom of apical dendrites of pyramidal neurons in

Brodmann area 3b (tangential dipole in Fig. 2.2.1) [Halgren, 1990]. This could result from

synchronous excitatory postsynaptic potentials in the upper end of the apical dendrites, close to

the cortical surface. Given the columnar organization of cerebral cortex, the currents tangential

to the whole brain surface are supposed to generate the strongest MEG dipoles in the paradigm

used [Hari, 1999]. It would be challenging to investigate whether the EEG source at the same

place and moment in time would be in the direction exiting the surface as suggested by its

attribution to the returning extracellular branch of the same circuit, i.e. a dipole with negative

pole near the cortical surface. To the extent that this proposed biophysical constraint is

vindicated in many other evoked potential examples, it may acquire significance as an a priori

constraint in MEG reverse problem solutions. SAV may thus be added to the tools, which are

currently trying to decipher the exact biophysical basis of MEG.

Chapter 9.Conclusion

140

Chapter 9. Conclusion

In the framework of this thesis several methods have been developed for processing anatomical data

in view of their use for functional studies on the brain.

Chronologically, the first application has been the use of anatomical images from a digital rat brain

atlas to reconstruct the brain and selected brain structures. This anatomical information was used to

help design an electrophysiological experiment on slices in vitro, under the request to select a cut

through the brain which would preserve as much as possible intact connections to a selected structure.

The main goals of the first part of the thesis are: a) to increase the appreciation of the anatomy of

specific brain structures, namely the mammilary bodies and the fornix, in relationship with the

hippocampus and the whole rat brain; b) to help planning an experiment in vitro on rat brain slices by

providing the optimal slicing plane which would keep intact most of the connections between the two

structures.

The next task has been to develop a method allowing the realistic visualization of brain electrical

activity i.e. display it on the surface of the brain areas actually sustaining the electric current, We

developed an object-oriented software tool, SAV, for visualization of spatio-temporal brain activity,

which allows the interplay of geometry and vector properties of the current density directly in the

representations. The main goal in SAV is to increase the understanding of the spatiotemporal

characteristics of brain current sources associated with distinct phenomena.

We investigate the use of methods based on mathematical morphology for cerebrum segmentation

and make use of interactivity to alleviate some of the problems in this type of approach (setting

appropriate thresholds, establishing the number of times the morphological operators are applied,

etc). The combination of segmentation methods dedicated in principle to different tasks like

segmenting the cerebrum or segmenting selected brain structures can improve the performance in

terms of quality for the semiautomatic method on one hand and also helps improving the speed in the

second case on the other hand.

We have developed software tools displays of activation maps in the background of the anatomy to

gain insight into the wealth of information on brain function that MFT analysis has unlocked from the

MEG data. Especially our tools allow evaluating regional and directional specificity of the

unconstrained solutions, as they allow quick and comfortable browsing through the data, including

generation of activation curves. The software’s design and capabilities are consistent with the

Chapter 9.Conclusion

141

philosophy to introduce the minimum necessary constraints in the inverse problem, and to contrast

the minimally constrained solutions to the anatomy [Ioannides 1989].

We have added a wealth of options into SAV for various types of visualization and analysis, while

leaving as much freedom to the program user to change and adjust the parameters of the analysis.

This principles aim to maximize the potential of the technique for understanding the complex cortical

structure- function relationship.

The problem of efficient analysis of large amounts of data produced with high resolution imaging and

functional techniques remains challenging while their interpretation is still a matter of both art and

science. An interdisciplinary effort is highly valuable in advancing our knowledge on the always

intriguing brain.

i

Publications related to the present thesis

• Badea A1., Kostopoulos G.K. and Ioannides A.A., “Surface Visualization of

Electromagnetic Brain Activity”, accepted for publication in the Journal of

Neuroscience Methods, 2003

Meeting presentations related to the present thesis

• Kostopoulos G.K., Fenwick P.B.C., Schellens M., Badea A., Zainea O. and Ioannides

A.A., “The relationship between cortical, cerebellum and brainstem activity from

average and single trial MEG and EEG data”, proceedings of the 32nd Annual Meeting

of the Society for Neuroscience, Orlando, Florida, November 2-7, 2002

• Badea A., Kostopoulos G.K, Ioannides A. A., “White–gray matter segmentation and

visualization of cortical activations”, proceedings of the 3-rd European Symposium on

Biomedical Engineering and Medical Physics, Patras, 30.08-1.09, 2002

• Zainea O., Badea A., Kostopoulos G.K., Ioannides A.A., “Identification of primary

somatosensory cortex subdivisions using anatomically constrained dipoles”,

proceedings of the 3-rd European Symposium on Biomedical Engineering and Medical

Physics, Patras, 30.08-1.09, 2002

• Bocioaca A., Ioannides A. A., Kostopoulos G. K., "A Semiautomatic Method for

Segmentation and Volumetry of the Hippocampus from MRI"; Proceedings of the 15th

Meeting of the Hellenic Society for Neuroscience, Patras, Greece, October 27-29, 2000.

• Bocioaca A., Schellens M., Kostopoulos G.K., Ioannides A. A., "Visualization of

distributed source solutions in electrophysiology: Examples from MEG"; proceedings

of 12th International Conference on Biomagnetism, Biomag2000, Helsinki University

of Technology, Espoo, Finland, 13 to 17 August , 2000. An oral presentation with a

similar name was held at the European Symposium for Biomedical Engineering, Patras

2001.

• Bocioaca A., Badea C., Papatheodoropoulos C., Kostopoulos G. K., "A Software Tool

to Assist the Planning of Electrophysiological Experiments on Rat Brain Slices",

1 Badea A. and Bocioaca A. are one and the same person.

ii

Medical Informatics Europe 1999 (MIE '99) congress, Ljubljana, Slovenia, 22-26

August 1999.

• Bocioaca A., Badea C., Kostopoulos G., Ioannides A., "A software for brain

segmentation from MRI data - a morphology based approach", Proceedings of the 14th

Meeting of the Hellenic Society for Neuroscience, pp.3-4, Volos, Greece, 14-16 May

1999; (paper awarded the second prize for best poster presentations ); presented then at

the 20th European Winter Conference on Brain Research, 11-17 March 2000, Villars-

sur-Ollon , Switzerland).

• Bocioaca A., Badea C., Kostopoulos G. K., "A Software Tool for Interactive

Determination of the Plane of Cut through the Rat Brain", Proceedings of the First

European Symposium in Biomedical Technology and Medical Physics, 28-29 August

1998, Patras, Greece.

iii

• Abbreviation list

3v: third ventricle

a: alveus

ac: ambient cistern

AFCM: adaptive fuzzy c means

AG: amygdala

au: arbitrary units

BA: Brodmann area

BS: brain stem

CA: cornu ammonis

ca: cerebral aqueduct

cc: crus cerebri

CT: computed tomography

cc:crus cerebri, cerebral penduncles

cf: crus fornices

CM=callosomarginal sulcus

CS: central sulcus, rolandic sulcus

CSF: cerebro spinal fluid

EC=enthorinal cortex

ECD: equivalent current dipole

ECG: electrocardiogram

EEG: electro encephalogram

EM: expectation maximization

EOG: electrooculogram

EPSP: excitatory postsynaptic potential

f1: the superior frontal sulcus

f2: the inferior frontal sulcus

FDA: federal drug administration

FCM : fuzzy c means

fMRI: functional magnetic resonance tomography

GB: globus palidus

GM: gray matter

H: hypothalaus

HH: hippocampus head

iv

ic: internal capsule

ica: independent component analysis

ips: intraparietal sulcus

IPSP: inhibitory postsynaptic potential

KS: Kolmogorov-Smirnov

LA: left arm

LF: left foot

LH: left hippocampus

LGN: lateral geniculate nucleus

lv: lateral ventricle

mb: mammillary bodies

MEG: magnetoencephalogram

MFT: magnetic field tomography

MGN: medial geniculate nucleus

ML: medial lemniscus

MR: magnetic resonance

MRI: magnetic resonance imaging

MM: mathematical morphology

MS: multiple sclerosis

MTS: medial temporal sclerosis

MUSIC: multiple signal classification

NMR: nuclear magnetic resonance

PreCG: precentral gyrus

PostCG: postcentral gyrus

RA: right arm

RF: right foot

rf: radio frequency

RH: right hippocampus

ROI: region of interest

Rol: rolandic sulcus (central sulcus)

TLE: temporal lobe epilepsy

t1: superior temporal

P: putamen

PD: proton density

v

PET: positron emission tomography

PSF: point spread function

PVA: partial volume effects

pu: pulvinar nucleus

RH: right hippocampus

SI: primary somatosensory cortex

SII: secondary somatosensory cortex

s: splenium of corpus calossum

SAV: surface activation vizualizer

sc: superior colliculi

sn: substantia nigra

SEF: somatosensory evoked fields

SEP: somatosensory evoked potetials

SNR: signal to noise ratio

SPECT: single photon emission computed tomography

SQUID: superconducting cuantum interference device

T1: inversion time

TE: echo time

th: temporal (inferior) horn of lateral ventricle

V1: primary visual cortex

V2: secondary visual cortex

VA: ventralis anterior (nucleus of thalamus)

ZI: zona incerta

VRML: virtual reality markup language

WM: white matter

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