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Automated detection of abnormal changes in cortical
thickness: A tool to help diagnosis in neocortical focal epilepsy
Inês Monteiro Souta
Dissertação para obtenção de Grau de Mestre em
Engenharia Física e Tecnológica
Júri
Presidente: Prof. João Seixas
Orientador: Prof. Eduardo Ducla-Soares
Co-Orientador: Prof. Alexandre Andrade
Vogais: Dr. Alberto Leal
Outubro 2007
Resumo
As epilepsias focais sem lesões demonstradas na Ressonância Magnética (RM) apresentam
problemas significativos na avaliação para cirurgia da epilepsia, frequentemente requerendo
monitorização invasiva. Um delineamento preciso da localização da lesão é crucial para o
planeamento do EEG e da cirurgia. Na avaliação pré-cirúrgica da epilepsia, o diagnóstico de
displasia é frequentemente suspeitado. Alterações da espessura cortical e um esbatimento da
interface entre a matéria branca/cinzenta são fortes indícios desta patologia. Contudo, em
alguns casos, as lesões são difíceis de identificar devido às suas subtilezas e à ausência de
uma separação evidente entre estas e o cortéx saudável, o que torna o seu delineamento um
verdadeiro desafio.
Neste trabalho, desenvolveu-se um método semi-automatizado para determinação da
espessura cortical e da área epileptogénica em pacientes com epilepsia neocortical. O método
proposto foi testado em 2 casos de epilepsia refractária focal, com perspectivas de sucesso
como ferramenta complementar para uma avaliação pré-cirúrgica destes pacientes mais
precisa e eficaz.
Palavras-Chave: Espessura Cortical, Epilepsia Focal, Ressonância Magnética Nuclear
1
Abstract
Focal epilepsies with no lesions demonstrated in Ressonance Magnetic Imaging (MRI) present
significant problems in the surgical evaluation for epilepsy surgery, often requiring invasive
monitoring. However, the outcome is closely related to the resection of the whole lesion. Their
precise delineation is thus crucial for surgical planning in epilepsy. In presurgical evaluation for
epilepsy surgery, the diagnosis of cortical dysplasia is often suspected. Changes in cortical
thickness and blurring of the gray/white matter interface are strong evidence of dysplasia.
Nevertheless, in some cases, the lesions are hard to distinguish from healthy cortex because of
their subtleties, and the absence of evident boundaries makes their delineation challenging.
This thesis presents a semi-automatic method of cortical thickness evaluation to
improve determination of the epileptic area in patients with neocortical focal epilepsy. The
proposed method was tested in 2 patients suffering from epilepsy. This approach may become
a useful tool for the presurgical evaluation of these patients.
Keywords: Cortical Thickness, Focal Epilepsy, Magnetic Resonance Imaging
2
Acknowledgments
I would first, and foremost, like to thank Alexandre Andrade, my (co)supervisor mostly for all I
have learned with him and for the full support given during the development of the thesis. I
would also like to thank Dr. Alberto Leal which accompanied at close distance the whole work
and made possible many aspects therein. I have learned a lot from their great scientific
knowledge and, above all, I have really appreciated their availability. I would also acknowledge
gratefully my supervisor Ducla-Soares who first introduced me to the wonders for biophysics.
The thesis was entirely developed in Lisbon at Instituto de Biofísica e Engenharia Biomédica
(IBEB), to which I am very grateful for the exceptional research conditions ofered. I am also
grateful to the Centre of Caselas for entrusting the magnetic resonance images used on this
thesis.
From a more personal point of view, I would like to thank Liza for all the support and
encouragement during the last few years. Last but not the least, I am grateful to my family and
friends, specially my parents, my sister, André, Shrika, Hugo, Francisco and Lino for everything
they have done.
3
Table of Contents
1. Introduction.............................................................................................................................. 9
2. Magnetic Resonance Imaging ............................................................................................. 11
2.1. A Brief History of Magnetic Resonance Imaging ............................................................. 11
2.2. Physics of MRI ................................................................................................................. 13
2.2.1. Imaging ..................................................................................................................... 15
2.2.2. T1 and T2 relaxation in tissue ................................................................................. 16
3. Presurgical evaluation of epilepsy ...................................................................................... 19
3.1. Overview of epilepsy........................................................................................................ 19
3.1.1. Epidemiology of epilepsy.......................................................................................... 21
3.2. Aims and concepts in surgery for epilepsy ...................................................................... 21
3.3. Definition of cortical zones: The epileptogenic lesion...................................................... 22
4. Measuring Cortical Thickness ............................................................................................. 25
4.1. Cortical Thickness Metric: A literature review.................................................................. 25
4.2. Computational Considerations and Methods................................................................... 27
4.3. Measuring Cortical Thickness with Freesufer: Overview................................................. 27
5. FreeSurfer and Image Analysis ........................................................................................... 29
5.1. Purpose and Participants ................................................................................................ 29
5.2. Individual Subjects Analysis: Image Processing Stages ................................................. 30
5.2.1. Surface and Volume Automated Reconstruction ..................................................... 30
5.2.2. Workflow and Manual Edits ..................................................................................... 33
5.2.2.1. Troubleshooting ............................................................................................. 33
5.3. Surface-based Group Analysis ....................................................................................... 39
5.3.1 Processing Stages..................................................................................................... 39
5.3.1.1. Assemble Data I.............................................................................................. 40
5.3.1.2. Assemble Data II............................................................................................. 40
5.3.1.3. Group Linear Model ........................................................................................ 41
6. Results..................................................................................................................................... 42
6.1. Validation……………………………………………………………………………………..42
6.2. Control Group .............................................................................................................. 43
6.3. Overall picture of the approach ................................................................................... 45
6.3.1. Patient 1 ............................................................................................................ 45
6.3.2. Patient 2 ............................................................................................................. 53
6.4. Discussion ............................................................................................................................ 63
4
7. Discussion and conclusions................................................................................................ 65
7.1 General discussion and conclusions ................................................................................ 65
7.2 Future work ....................................................................................................................... 66
Bibliography............................................................................................................................... 67
Appendix A .................................................................................................................................. 68
Appendix B ................................................................................................................................. 69
5
List of Figures
Figure 2.1 - Energy levels induced by an external magnetic field…………………………………….. 13
Figure 2.2 - Polarization vector due to Boltzmann distribution of energy states and flipping of the
magnetization around an angle of α due to an RF pulse………………………………………………….
14
Figure 2.3 - Simple pulse sequence diagram……………………………………………………………… 17
Figure 2.4 – T1-, T2- and PD-weighted images of the human brain …..……………………………….. 18
Figure 3.1 - EEG of a 46-year-old patient. ……………………………………………………...…………
20
Figure 5.1 – Volumes generated by FreeSurfer workflow. ……………………………………………….
31
Figure 5.2 – Pial surface. ……………………………………………………………………………………. 31
Figure 5.3 – Inflated Surface………………………………………………………………………………… 32
Figure 5.4 – A. Volume-based labeling……………………………………………………………………... 33
Figure 5.5 – Example of a group of voxels included as white matter……………………………………. 34
Figure 5.6 – Example of a skull stripping error…………………………………………………………….. 35
Figure 5.7 – Intensity normalization failure…………………………………………………………………. 36
Figure 5.8 – Pial surface error……………………………………………………………………………….. 37
Figure 5.9 - A diagrammatic overview of the main FreeSurfer process, proceeding generally from
top to bottom……………………………………………………………………………………………………
38
Figure 5.10 – Intersubject averaging processing stages…………………………………………………. 39
Figure 6.1 – Validation of significance maps……………………………………………………………… 42
Figure 6.2 – Inflated surface with thickness map of the average control group………………………. 43
Figure 6.3 - Map of the standard deviations of the thickness measurements across 23 subjects….. 44
Figure 6.4 – Cortical Thickness map of Patient 1………………………………………………………... 46
Figure 6.5 – Cortical Thickness map of Patient 1 vs Control Group………………………………….... 47
Figure 6.6 – Abnormal cortical thickness pattern in the volumetric MRI. ……………………………... 48
Figure 6.7 – Label location. ………………………………………………………………………………... 48
Figure 6.8 – Mean Cortical Thickness distribution within the defined label…………………………… 49
Figure 6.9 – Atrophy label in the inflated surface………………………………………………………… 50
Figure 6.10 – Mean cortical thickness values within the new found label………………………………. 50
6
Figure 6.11 – Significance map……………………………………………………………………………… 51
Figure 6.12 – Abnormal areas (in green) in the volumetric MRI…………………………………………. 51
Figure 6.13 – Cortical Thickness Maps in inflated surface of the control group (left) and patient 2
(right)……………………………………………………………………………………………………………
52
Figure 6.14 - Cortical Thickness Maps in inflated surface of the control group (left) and patient 2
(right)………………………………………………………………………………………………………........
53
Figure 6.15 – Cortical Thickness Maps in inflated surface of the control group (left) and patient 2
(right)……………………………………………………………………………………………………………
54
Figure 6.16 and 6.17 – Significance map (left) and mean cortical thickness distribution within the
delimitated label (right)………………………………………………………………………………………..
54
Figure 6.18 – Patient 2 volumetric MRI. The red circled area is a clear heterogeneous lesion in the
occipital left lobe……………………………………………………………………………………………….
55
Figure 6.19 - Patient’s 2 volumetric MRI……………………………………………………………………. 56
Figure 2.20 – New label definition…………………………………………………………………………… 57
Figure 6.21 – Significance map with new label……………………………………………………………. 57
Figure 6.22 – Mean cortical thickness distribution within the potential lesion
region…………………..........................................................................................................................
58
Figure 6.23 – Label location in the MRI volume…………………………………………………………… 58
Figure 6.24 – Cuneus label in inflated surface and mean thickness values distribution………………. 60
Figure 6.25 - Lingual label in inflated surface and mean thickness values distribution……………….. 60
Figure 6.26 – Pericalcerine label in inflated surface and mean thickness values distribution………... 61
Figure 6.27 – Lesion label in inflated surface……………………………………………………………… 61
7
List of Tables
Table 2.1 - Representative values of relaxation parameters T1, T2 and PD for water ……………….. 18
Table 3.1 – Descriptions of zones and lesions of the cortex ……………………………………………. 22
Table 6.1 – Cortical Thickness measures within FreeSurfer’s several parcelation labels …...……… 59
8
Chapter 1
Introduction
Epilepsy is a common neurological disorder, which affects about 1 % of the population in
industrialized and even more in less developed countries. It is characterized by recurrent
epileptic seizures, which are sudden excessive discharges of brain cells. Many factors can
produce the epileptic state, for example head injuries, vascular damages, tumours or genetic
factors; it is in fact more proper to make reference to the epilepsies. Seizures are classified into
partial (focal or local), which start in a limited part of the brain, and generalized, where most of
the brain is involved from the onset.
In most patients, epilepsy can be treated with medication that typically aims at reducing
the neuronal excitability, with efficiency depending on the type and causes of epilepsy.
However, medication is inefficient in approximately 20 % of the patients: these patients are said
to have refractory or “pharmacoresistant” epilepsy. When epilepsy is focal, i.e. when seizures
start in a very limited part of the brain, a surgical procedure can be considered in order to
remove the part of the brain responsible for the seizures. This removal (resection) of a part of
the brain may appear like a drastic option, but one has to consider the severe handicap that
arises from the epileptic condition.
Also, an effort is made to avoid resecting regions that would lead to too severe post-
surgical losses. Moreover, the resected region is often damaged, and the brain may have
already compensated by involving other areas. Presurgical evaluation consists in combining
many sources of information on the patient’s epilepsy in order to define as precisely as possible
the zone to be removed. The demonstration of a cortical lesion can significantly change the
outcome of the surgery for epilepsy in neocortical cases. For small focal atrophies, cortical
thickness is one of the most important parameters to be evaluated because it may point to
possible destructive lesions or possible dysplasia.
This thesis presents a semi-automated method for identifying and segmenting the
lesions on T1-weighted MRI based on FreeSurfer software analysis
(http://surfer.nmr.mgh.harvard.edu/). FreeSurfer, a software package, developed at the A.
Martinos Center for Biomedical Imaging at Harvard Medical School was used for the analysis of
the MR-data. Several questions were considered. How to identify a structure that is ill-defined,
heterogeneous and unshaped? How to deal with thickness values in gyrus and sulcus? How
reliable the FreeSurfer group analysis is?
9
In chapters 2 and 3, we will provide background information on MRI and presurgical
evaluation. Then, we will present different methods for measuring cortical thickness and
computational approach (chapters 4). In chapter 5, we will present FreeSurfer, our method and
results as well as a brief analysis. Finally, we will conclude and present possible new lines of
research in chapter 6.
10
Chapter 2
Magnetic Resonance Imaging
2.1. A Brief History of Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) has become the primary technique whole-body in the
routine diagnosis of many disease processes, replacing and sometimes surpassing computed
tomography (CT). MRI has particular advantages in that it is non-invasive, using non-ionising
radiation, and has a high soft-tissue resolution and discrimination in any imaging plane. It may
also provide both morphological and functional information. The resultant MR image is based on
multiple tissue parameters any of which can modify tissue contrast. In its development, MRI has
incorporated a multidisciplinary team of radiologists, technicians, clinicians and scientists who
have made, and are continuing to make, combined efforts in further extending the clinical
usefulness and effectiveness of this technique.
The development of magnetic resonance imaging (MRI) began with discoveries in
nuclear magnetic resonance (NMR) in the early 1900s. At this time, scientists had just started to
figure out the structure of the atom and the nature of visible light and ultraviolet radiation emitted
by certain substances. The magnetic properties of an atom's nucleus, which is the basis for
NMR, were demonstrated by Wolfgang Pauli in 1924.The first basic NMR device was developed
by I. I. Rabi in 1938. This device was able to provide data related to the magnetic properties of
certain substances. However, it suffered from two major limitations. Firstly, the device could
analyze only gaseous materials, and secondly, it could only provide indirect measurements of
these materials. These limitations were overcome independently in 1946 by two scientists in the
United States.
Felix Bloch, working at Stanford University, and Edward Purcell, from Harvard
University, found that when certain nuclei were placed in a magnetic field they absorbed energy
in the radiofrequency range of the electromagnetic spectrum, and re-emitted this energy when
the nuclei transferred to their original state. The strength of the magnetic field and the
radiofrequency matched each other as earlier demonstrated by Sir Joseph Larmor (Irish
physicist 1857-1942) and is known as the Larmor relationship (i.e., the angular frequency of
precession of the nuclear spins being proportional to the strength of the magnetic field). This
phenomenon was termed nuclear magnetic resonance or NMR.
With this discovery NMR spectroscopy was born and soon became an important
analytical method in the study of the composition of chemical compounds. For this discovery
11
Bloch and Purcell were awarded the Nobel Prize for Physics in 1952. During the 50's and 60's
NMR spectroscopy became a widely used technique for the non-destructive analysis of small
samples. Many of its applications were at the microscopic level using small (a few centimetres)
bore high field magnets.
In the late 60's and early 70's Raymond Damadian, an American medical doctor at the
State University of New York in Brooklyn, demonstrated that a NMR tissue parameter (termed
T1 relaxation time) of tumour samples, measured in vitro, was significantly higher than normal
tissue. Although not confirmed by other workers, Damadian intended to use this and other NMR
tissue parameters not for imaging but for tissue characterisation (i.e., separating benign from
malignant tissue). This has remained the Holy Grail of NMR yet to be achieved due mainly to
the heterogeneity of tissue. Although criticism has been raised about his scientific acumen it
should not overshadow the fact that his description of relaxation time changes in cancer tissue
was one of the main impetuses for the introduction of NMR into medicine.
In 1973, Paul Lauterbur, a Professor of Chemistry at the State University of New York at
Stony Brook, described an imaging technique that removed the usual resolution limits due to the
wavelength of the imaging field. The short paper describing this technique was entitled "Image
formation by induced local interaction; examples employing magnetic resonance" and was
nearly not published, having been initially rejected by the editor of Nature as not of sufficiently
wide significance for inclusion in the journal. He used two fields: one interacting with the object
under investigation, the other restricting this interaction to a small region. Rotation of the fields
relative to the object produces a series of one-dimensional projections of the interacting regions,
from which two- or three-dimensional images of their spatial distribution can be reconstructed.
This imaging experiment moved from the single dimension of NMR spectroscopy to the second
dimension of spatial orientation which is the foundation of MRI.
MR also owes a debt to computed tomography (CT) as it was developed initially on the
back of CT but quickly outpaced that technique. The impact that CT had in the medical
community is not to be disregarded as it stimulated interest both of clinicians and manufacturers
to the potential impact that this new technique could have. It had already demonstrated the
advantage of tomographic sections through the head or body of a patient allowing diagnosis of
disease processes in a non-invasive way. In the late 70's and early 80's a number of groups,
including manufacturers, in the US and UK showed promising results of MRI in vivo. It was, and
still is, a technological challenge to produce wide bore magnets of sufficient uniformity to image
the human body.
The focus of current MRI research is in areas that include improving the scan resolution,
reducing scan time, and improving MRI system design. The methods for improving resolution
and decreasing scan time involve reducing the signal to noise ratio. In an MRI system, noise is
caused by randomly generated signals that interfere with the signal of interest. One method for
reducing it is by using a high magnetic field strength. Improved designs for MRI systems will
also help reduce this interference and decrease the noise associated with electromagnets.
12
2.2. Physics of MRI
Magnetic resonance imaging is based on the fact that some nuclei (with an odd number of
protons, neutrons, or both) possess an intrinsic magnetic moment.
From a quantum mechanics perspective nuclear magnetic resonance can be explained
by the concept of energy quantization. When an external magnetic field is applied to a spin ½
system (for example a proton), the energy level of this system will split up in two energy states
+½, (‘alignment with / lowest energy’), and –½ (‘alignment against / highest energy’), with an
energy difference:
ωπ2
hE =∆ (1)
Where h is Planck’s quantum constant ω (in rad/s) is the frequency corresponding to a
photon energy that can be absorbed by the system.
Figure 2.1 - Energy levels induced by an external magnetic field
Classically, the interaction of a nuclear spin with an external magnetic field is described
by the concept of precession. The individual proton spins precess about the field direction with
the so called Larmor frequency, which is proportional to the magnetic field:
0Bγω = (2)
For each type of spin (such as 1H, 13C, etc.) γ is a constant called the gyromagnetic
ratio and B0 is the magnitude of the external magnetic field. Hydrogen nuclei have a γ value of
2.675 ·108 rad/s/T.
Because of the Boltzmann distribution of quantum states - that is valid for the above
described Zeeman splitting - there are, at room temperature, more protons in the lower energy
13
state (spin up) than in the higher energy state. The distribution is given by the Boltzmann’s
equation:
Tk
E
BeN
N∆
−
+
− = (3)
where T is the temperature and k is Boltzmann’s constant. At macroscopic level this
population difference (at 1.5 T this difference is about 1 : 1 ·106 ) is seen as a net polarization,
which is a vector quantity and points in the direction of the magnetic field (figure 2.3). This total
net magnetic moment at equilibrium is called the magnetization M0.
Figure 2.2 - Polarization vector due to Boltzmann distribution of energy states and
flipping of the magnetization around an angle of α due to an RF pulse
When the radio frequency pulse is applied, the system can reach a higher energy level.
A classical description of this process is as follows: a radio frequency (RF) pulse is applied (an
oscillating electromagnetic wave) to the system at exactly the Larmor frequency of the
precessing spin (‘on-resonance’). For hydrogen atoms this RF pulse has a frequency of 64 MHz
for a magnetic field of 1.5 T. The magnetization vector now starts to precess about the effective
magnetic field, i.e. the field composed of the B0 field and the RF pulse field.
When the RF pulse is switched off, the magnetization vector starts to regrow to its initial
value. This process of realigning with the magnetic vector along the static magnetic field is
called relaxation, i.e. the process of returning to the thermal equilibrium after a perturbation.
During this process the signal intensity is detected with a receiver coil. Applying energy with the
RF pulse can be understood as a flipping of the magnetization around an angle α (also shown in
figure 2.3). Applying more energy will result in a larger flip angle.
14
The components perpendicular (Mx and My) and parallel (Mz) to the thermal equilibrium
magnetization vector M0 return (relax) with different time constant. As mentioned before, it is
possible to use classical mechanics to describe these relaxation processes. Bloch formulated
the following differential equations that describe a group of nuclei in a magnetic field in a
rotating frame of reference:
1
0
2
2
))((
)(
)(
T
tMMM
dt
d
T
tMM
dt
d
T
tMxM
dt
d
zz
y
y
x
−=
−=
=
(4)
in which T1 and T2 are the relaxation time constants. T1 is the longitudinal relaxation
time (or spin-lattice relaxation time) and describes the return of the longitudinal magnetization
after a perturbation. T1 relaxation is a process in which energy from the spins is transferred to
the surrounding ‘lattice’; which can be either solid or liquid. T2 is the transverse relaxation time
(or spin-spin relaxation time) and describes the disappearance of transverse magnetization. T2
relaxation is an entropy process, since spins exchange energy between themselves (there is no
net energy transport) causing a decrease in phase coherence: i.e. an increase in global entropy.
Because of field inhomogeneities the dephasing will go faster than could be expected from spin-
spin interaction alone. The T2 relaxation due to both spin-spin interaction and field influences is
called T2*.
These Bloch equations can be extended for more complicated situations, for example
during an RF pulse or with magnetization transfer (and not only during free precession, as in
equations (4)). By solving these differential equations with certain boundary conditions a signal
equation could be found. This signal equation can be used in the theoretical approach of
imaging.
2.2.1. Imaging
Spatial information in MR imaging is provided by creating a small additional gradient field along
one direction. As a consequence different points in space become identified by different
resonance frequencies, which allows the location of nuclear spins emitting RF fields to be
determined by their frequency. The magnetic field gradient is applied independently of the static
field by means of a specially shaped coil. When a linear gradient (Gz) is applied along the z-
direction, the Larmor frequency will depend on the position along the z-axis as shown by:
15
zGB zγγω += 0 (5)
When an RF pulse of a certain bandwidth is applied, only the slice of the sample that is
in the ‘right’ frequency domain is excited by the pulse. These slices have in general a thickness
of several millimetres. When this slice is chosen, the x and y directions are discriminated by
again applying a gradient: The x-gradient is applied during the read-out pulse, so that the
different pixels in the x-direction are now characterized by a different frequency:
xGx
γ
ωω 0−= (6)
Spatial information for the y-direction is provided by creating a gradient in the y
direction. This gradient is turned on before the Gx gradient and causes a different phase shift
for the different pixels. This phase gradient is changed in every repetition to a different level.
This process is called phase encoding.
Consequently each pixel in a slice is characterized by a distinct frequency and a distinct
phase, which are unique and encode for the x and y coordinates for that pixel. Each time we do
a phase encoding step followed by a frequency encoding step we get a signal. This signal is
sampled and fills a data space, called k-space. Each slice has its own k-space Because the
received signal is in the time domain (it is a timevarying signal) and we are interested in the
frequency (because that gives us spatial information), several signal processing steps have to
be made before an image is created. The most important is to make a Fourier Transform of the
k-space, which yields the signal distribution in the frequency domain.
A pulse sequence diagram illustrates the sequence of events that occur during MR
imaging. An example of a sequence diagram is given in figure 2.3.
The sequence of RF pulses and gradients is repeated several times to acquire a
complete image. Two important sequence parameters are the repetition time (TR) and the time
between the excitation pulse and the echo: the echotime (TE). Most clinical scanners are set up
for imaging the protons of ‘free’ water. These protons have a long enough T2 (i.e. greater than
10 ms) to be detected. Protons with a shorter T2 decay before the receiver can detect their
signal.
2.2.2. T1 and T2 relaxation in tissue
The MR signal is a complex function of proton density, T2, T1, flow, diffusion and other
sample properties. The signal intensity in an image is also influenced by the variance of RF
pulses and gradients. The use of different RF pulse sequences creates different appearance for
various tissues on the MR image. The signal intensity (SI) in the case of a simple tissue, only
reflecting T1 and T2 relaxation, is given by:
16
Figure 2.3 - Simple pulse sequence diagram with a 90 degrees RF pulse, a slice select
gradient (Gz) , a phase encoding gradient (Gy) and a frequency encoding gradient (Gx). The
phase encoding gradient has a different strength in every repetition time.
)1)()(( 1*21 T
TR
T
TE
eeHNSI −= (7)
in which N(1H) is the proton density (PD).
Because of the different relaxation times for different tissues, the appearance of the
tissue on an image depends strongly on the chosen system parameters. For example a
sequence that uses a short TR and a short TE will enhance the T1 contrast (i.e. the different T1
relaxation times of different tissues determine the image contrast). This is called a T1-weighted
image, because the contrast is predominantly determined by the T1 (however T2 and PD also
has some influence).
On the contrary a long TR and a long TE gives us a T2-weigthed image. A third
possibility is to take a long TR and a short TE. The difference in intensity now reflects the proton
density difference. Approximate relaxation values for different brain tissues are given in table
2.1 and examples of T1-, T2- and PD-weighted images are given in figure 2.4.
17
Table 2.1 - Representative values of relaxation parameters T1, T2 and PD for water in
brain tissues and CSF at B0 = 1.5 T and human body temperature . The proton density is given
relative to the proton density of CSF.
Figure 2.4 - T1-, T2- and PD-weighted images of the human brain. All
images have a different sequence setup: image 1: TR/TE = 250/20 ms, image 2: TR/TE
= 2000/80 ms, image 3: TR/TE = 2000/20 ms
18
Chapter 3
Presurgical evaluation of epilepsy
The goal of presurgical evaluation is to gain information on the zone of the brain that can be
resected in order to render the patient seizure-free. Considerable knowledge has been
accumulated on patterns of interictal spiking and of ictal onset and propagation, which can be
related to the clinical manifestations. This is complemented by a whole spectrum of techniques,
from magnetic resonance to neuropsychology tests.
3.1. Overview of epilepsy
Epilepsy had to wait the end of the nineteenth century to be recognized as a neurological
disease. At that time, neurology was only an emerging field and its main objective was the
localization of cortical functions to specific brain regions. In 1873, a physician of the National
Hospital for Neurology and Neuro-surgery of London, John Hughlings Jackson, described for
the first time epileptic seizures as “occasional sudden excessive, rapid and local discharges of
gray matter”, that is as local abnormal activity of the brain. This intuition had important
consequences on the understanding of epilepsy. Brain functions began to be localized and a
close link appeared between clinical characteristics of the seizures and the localization of the
site of origin. Besides, thanks to the observation of these symptoms, William Macewen, a
neurosurgeon of Glasgow, localized and removed in 1879 a tumor from the brain of one of his
patients with epilepsy. Following this experiment, other such attempts were led but often with
bad outcomes because of the poor medical means at that time. However, the principles of
epilepsy surgery were established and the removal of the seizure focus began to be a viable
way to treat the disease. It became then quickly evident that a good localization of these foci
was critical to get the best possible outcome.
During the twentieth century, epilepsy treatment and brain understanding improved as
new techniques such as electroencephalography (EEG) arose. Developed by Jackson in 1873,
it allowed the tracking and the measurement of the electrical brain activity by a totally non-
invasive method; the emitted waves were picked up by electrodes placed to the scalp. In that
way, abnormal activations of the brain were easily detected and the localization of epileptiform
foci could be achieved by a totally objective method. Moreover, a new type of epilepsy, the
temporal lobe epilepsy, was brought to light. Indeed, the absence of explicit symptoms made
19
this form of epilepsy very difficult to diagnose and the affected area of the brain was almost
impossible to find before the coming of EEG. In the same way, epilepsy surgery became a
viable way to treat epilepsy, curing successfully an increasing number of patients with
pharmacologically intractable epilepsy.
Figure 3.1 - EEG of a 46-year-old patient. Each line corresponds with a specific
electrode. Horizontal axis represents time, vertical axis voltage.
Magnetic resonance imaging (MRI) is the second key technology nowadays widely
applied to human studies. This technique allows the analysis of precise structures of the brain,
managing the detection of small tumors, cortical malformations and even hippocampal
sclerosis. The electric localization can then be associated in many cases with a structural
abnormality. However, the structural anomalies are sometimes not consistent with the electric
focus. The question is then which region a neurosurgeon has to remove in order to treat the
patient, the abnormal tissues or the seizure focus detected by the EEG. The answer remains
unclear but it seems that the removal of the structural anomaly is critical for a good post surgical
outcome.
Nowadays, MRI is still in progress and the understanding of brain functions follows
close behind it. Recently for example, functional MRI (fMRI) confirmed the vascular changes
that occur during seizures, as suspected by Wilder Penfield years before. Other new MR
techniques, like diffusion tensor imaging (DTI), seem to be promising as well by revealing
relevant connections between two given cortical regions. The understanding of the influence of
20
an abnormal region on its neighbourhood for example may greatly benefit from such
technologies. Moreover, the increase of computer power allowed the use of advanced image
processing on MR images, introducing in this manner new diagnosis-aid systems for
neurosurgery but also permitting quantitative analysis of brain structures. One thing is for sure,
MRI and image processing will be crucial in the development of brain understanding and
epilepsy treatment.
3.1.1. Epidemiology of epilepsy
Epilepsy is a neurological disorder that affects people in every country throughout the world.
Epilepsy is also one of the oldest conditions known to mankind and the most common
neurological affection after migraine. It is characterized by a tendency to recurrent seizures and
it defined by two or more unprovoked seizures. A patient is said to have epilepsy if he has
suffered from two epileptic seizures at least. In that way, approximately 1% of the world
population is really affected, in comparison with the 5% who may have only one single epileptic
seizure in the course of their life. This rate, called prevalence, is quite constant in all over the
world despite the different statistical protocols used by the various countries. The prevalence
depends on the age and the type of crisis. In most cases, it tends to increase from childhood to
adolescence, it is quite constant next and finally increases again, slightly, after age 70. The
number of new detected cases follows as for it an opposite trend. It is high during childhood and
decreases until adolescence. Nevertheless, The causes of these new cases are often unknown
but they may sometimes involve some genetic disorders. During adulthood, the rate stays
stable and after middle age it increases again, as tumors, strokes or other degenerative
diseases such as Alzheimer’s disease arise [1]. In short, 30 to 50 new cases amongst 100,000
persons are detected every year. It should be mentioned that other factors such as acquired
disorders, head trauma or other infections can also provoke epilepsy, but in most cases, and
more especially among young patients with generalized epilepsy, they are not well identified,
indeed unknown. Among the newly diagnosed patients, about 70% of them can be successfully
treated with, or even without, medication. On the other hand, there are approximately 30% of
them who suffer from pharmacologically intractable epilepsy [2], i.e. there is no treatment for
their seizures (about 10%) or the current drugs are partially or completely inefficient. Surgery
can then constitute a viable solution: 64% of patients who had an operation have become
seizure-free or, at worst, have presented relevant clinical improvement [2].
3.2. Aims and concepts in surgery for epilepsy
Approximately 60% of all patients with epilepsy (0.4% of the population of industrialized
countries) suffer from focal epilepsy syndromes. In ~15%of these patients, the condition is not
adequately controlled with anticonvulsive drugs. Under the assumption that 50% of such
21
patients are potential candidates for surgical epilepsy treatment, 4.5% of all patients with
epilepsy (0.03% of the population) could potentially profit from epilepsy surgery [3]. Depending
on the epilepsy syndrome and the ability to define clearly and resect completely the
epileptogenic zone, 30-85% of epilepsy patients operated on remain seizure-free. The larger
epilepsy centres report average seizure-free rates of ~60% [3]. Therefore, considering the
severity of the epilepsy in the population operated on, epilepsy surgery can be considered a
very successful therapy.
The objective of resective epilepsy surgery is the complete resection or complete
disconnection of the epileptogenic zone, which is defined as the area of cortex indispensable for
the generation of clinical seizures. This aim is to be achieved with preservation of the ‘eloquent’
cortex. Modern epileptologists use a variety of diagnostic tools, such as analysis of seizure
semiology, electrophysiological recordings, functional testing neuroimaging techniques to define
the location and boundaries of the epileptogenic zone. These diagnostic methods define
different cortical zones (symptomagenic zone, irritative zone, ictal onset zone, functional deficit
zone and epileptogenic lesion).
Epileptogenic zone Region of cortex that can generate epileptic seizures. By definition,
total removal or disconnection of the epileptogenic zone is necessary
and sufficient for seizure-freedom
Irritative zone Region of the cortex that generates interictal epileptiform discharges
in EEG or MEG
Seizure onset zone Region where the clinical seizures originate
Epileptogenic lesion Structural lesion that is casually related to the epilepsy
Ictal symptomatogenic
zone
Region of cortex that generates the initial seizure symptoms
Functional deficit zone Region of cortex that in interictal period is functionally abnormal, as
indicated by neurological examination, neuropsychological testing
and functional imaging or non-epileptiform EEG or MEG abnormalities
Eloquent cortex Region of cortex that is indispensable for defined cortical functions
Table 3.1 – Descriptions of zones and lesions of the cortex
3.3. Definition of cortical zones: The epileptogenic lesion
This lesion is a theoretical concept that is the cause of the epileptic seizures. The best way to
define this today is by high-resolution MRI. However, not all lesions seen in a patient epileptic
seizures are epileptogenic. Some radiographic lesions may be unrelated to the clinical seizures.
For this reason, even when we see a lesion on the MRI we still have to use other methods to
22
verify (usually by video-EEG monitoring and/or seizure semiology) that the radiographic lesion
is indeed responsible for the patient’s seizures. A related problem is the definition of
epileptogenicity in cases with dual or multiple pathology. Here again, additional testing is
necessary to define which of the lesions are epileptogenic. In those cases in which two or more
lesions are in close spatial proximity, the problem of attributing epileptogenicity to one lesion or
another can frequently only be resolved with the use of invasive EEG technology.
The spatial relationship of the epileptogenic zone with the epileptogenic lesion is similar
to its relationship with the seizure onset zone that was discussed above. It has been thought
that complete resection of the radiographic epileptogenic lesion is necessary to obtain seizure-
freedom. This is not always true, however there are some cases in which only partial lesion
resection was possible (because of its location in eloquent cortex) that resulted in complete
seizure-freedom. This implies that the remainder of the radiographic lesion was either never
epileptogenic or was dependent on the resected tissue to elicit seizures. A more common
clinical scenario, however, occurs when seizures persist in spite of complete resection of the
lesion visible on MRI. This is frequently the case in patients with cortical dysplasia or post-
traumatic epilepsy. There are two possible explanations for this phenomenon. Many lesions are
not intrinsically epileptogenic but induce seizures by generating reactions in the surrounding
brain tissue with which they are in contact. Some of these lesions may include microchanges in
the brain tissue located at a significant distance from the epileptogenic lesion visible on MRI.
These microchanges are epileptogenic and, therefore, in these cases selective resection of the
MRI-visible epileptogenic lesion will frequently not be sufficient to abolish all seizures. Another
explanation addresses the sensitivity of MRI in detecting the complete lesion. Brain tissue,
adjacent to a radiographic lesion may consist of lesional tissue of lesser pathological severity.
This tissue, while being potentially epileptogenic, may remain invisible on MRI. This is
frequently the situation with cortical dysplasia in which only the ‘tip of the iceberg’ is visible on
mri. Failure to resect these MRI-invisible lesions can lead to persistence of seizures after
epilepsy surgery. This is the most likely explanation for the relatively high frequency of surgical
failure in patients with neocortical dysplasia.
How can we predict if, in any given patient, total resection of a lesion will lead to
seizure-freedom? There is no direct method to determine if an additional, epileptogenic zone
that is invisible on MRI surrounds any given epileptogenic lesion.
However, we can try to predict
the presence of a perilesional epileptogenic rim by understanding the nature of the MRI-visible
lesion. We know, for example, that well delineated brain tumours and cavernous angiomas tend
to produce epileptogenicity only in the MRI-visible lesion and its immediate surroundings.
Therefore, lesionectomy is usually successful in these cases. On the other
hand, as mentioned
above, cases with cortical dysplasia or post-traumatic epilepsy typically require more extensive
resection for a successful outcome. In these cases, exact definition of the irritative
and seizure
onset zones may also help in determining if the epileptogenic zone extends outside the limits of
the epileptogenic lesion
23
3.3. Future perspectives of epilepsy surgery and in the
definition of the epileptogenic zone
As resective epilepsy surgery has proved to be a successful therapeutic approach in some
cases, rendering up to 80% of selected patients seizure-free, it will be used in the decades to
come. Other destructive therapies, such as radiosurgery, will increasingly
play a role, especially
in patients unsuitable for operative procedures, i.e. those with unresectable lesion, such as
some arteriovenous malformations [4], [5]. It appears unlikely that radiosurgery will replace
selective amygdalo-hippocampectomy as the standard treatment for mesial temporal lobectomy.
In patients in whom the epileptogenic zone cannot be resected, increasing use of the vagus
nerve stimulator and other stimulation techniques, such as deep-brain stimulation,
is likely in the
future.
All these treatments require the definition of the epileptogenic zone, which is, as
mentioned above, a theoretical concept. None of the available tests permits direct measurement
of the epileptogenic zone. In the future, we will have to look for new diagnostic
techniques that
will permit more direct definition of the epileptogenic
zone. It is very likely that these
developments will be in functional neuroimaging. All widely available functional neuroimaging
techniques (mainly FDG-PET and interictal SPECT) measure only non-specific
brain physiology,
such as regional metabolism and blood flow. On the other hand, further developments may
make it possible
to directly image the distribution of neurotransmitters involved
in the
pathogenesis of epilepsy. Not only may this allow us to define different types of epileptogenic
lesions based on
neurotransmitter and receptor physiology, but it may also give
us a
measurement of the potential epileptogenic zones that are
currently undetectable
preoperatively. Receptor PET may play a major role in the definition of the epileptogenic zone in
the future. In addition, refinements of the currently available diagnostic techniques may increase
the accuracy with which we define the different zones. This will give us some additional
power
even if it does not solve some of the essential theoretical limitations discussed above.
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Chapter 4
Measuring Cortical Thickness
Neuroscience has shown a long term interest in measuring cortical thickness. Through the
years different approaches have been proposed to estimate cortical thickness from Magnetic
Resonance Imaging (MRI). Manual measurements came first, but throughout the years,
automated methods were developed in order to achieve an accurate tool for diagnosing and
studying a variety of neurodegenerative and psychiatric disorders.
Measuring cortical thickness is an important task for both normal and abnormal
neuroanatomy. The cortical mantle varies in thickness depending on the region of the cortex,
with considerable variation between individual brains as well as between hemispheres of the
same brain. In normal brains the cortical thickness varies between 1 and 4,5 mm, with an
overall average of 2,5 mm. The regional variations of the cortex tend to be quite large. For
example, Brodmann’s area 3 on the posterior bank of the central sulcus is among the thinnest
of cortical regions, with an average thickness of less than 2 mm, whereas Brodmann’s area 4
on the anterior bank is one of the thickest regions, frequently exceeding 4 mm. The thickness of
the cortex is of great interest in both normal development as well as a wide variety of
neurodegenerative and psychiatric disorders. In pathological cases cortical morphology has
been known to vary in epilepsy [6] mental retardation [7], Schizophrenia [6], anorexia nervosa
[6], Huntington’s disease [6, 7] and Alzheimer’s disease [6, 8], amongst others. The cortical
thinning is usually regionally specific and the atrophy and its progress can, in some cases,
reveal much about the evolution of the disease.
Being able to accurately estimate the thickness of the entire cortex of individual
subjects, or group statistics for patient or control populations, is an important topic for
neuroscience.
4.1. Cortical Thickness Metric: A literature review
Several attempts have been made to measure cortical thickness, including both post-mortem
studies and computational studies using MRI. In post-mortem studies the measurements are
highly dependent on the cutting angle. Even using the same post-mortem slice, individual raters
can easily differ by over 0.5 mm at any one location due to the blurred cortical boundary at the
white matter surface [9]. Estimating the thickness through computational studies is a rather
complex process involving multiple image processing steps. In fact, in this approach the
25
thickness is measured by finding the shortest line from the cortical surface to the grey and white
matter boundary. In these post-mortem studies the investigator would either insert a probe
through the outer surface and measure the distance along the angle of the probe towards the
white matter, or else the investigator would examine a slice of cortex and use a jeweller’s
eyepiece to measure the distance between the white matter and the surface along the angle of
the slice cut.
In studies which measure cortical thickness from MR images the native data usually
consists of a T1 MRI per subject (alternatively it can include multiple acquisitions for a single
subject). Firstly, a spatial and intensity normalization must be performed in all images. Then,
these one or more images of the brain are used to provide an anatomic label for each voxel. In
other words, to classify each voxel as grey matter, white matter, CSF or non-brain. After this
classification (which in some cases is manual and in others automated), the inner and outer
cortical surfaces are extracted. The creation of these two surfaces then allows the measuring of
cortical thickness. Defining cortical thickness, even when models of the inner and outer surfaces
are present, is not trivial. Cortical thickness is a distance metric but there are multiple ways of
defining corresponding points on the two surfaces between which that distance is to be
measured. Moreover, the distance need not be measured in a straight line but can be the result
of a more complicated equation, such as fluid flow lines.
In this section the various definitions of cortical thickness measurements proposed in
the literature are presented and compared in terms of precision. A survey of the literature
reveals essentially five distinct types of metrics for measuring cortical thickness. The first
method, called tlink measures the distance between linked nodes in the inner and outer surface.
Expanding the outer surface from the inner surface, keeping the same topology and number of
vertices per polyhedron, it is possible to create a correspondence between nodes. Although
very robust, this method will produce a distance measure corresponding to what as anatomist
would choose. The tnormal finds the point that intersects the normal surface. The tnear method
simply searches for the nearest vertex on the opposite surface. While intuitive, this method has
the potential for gross errors, such as jumping across gyri. The tlaplace, first published in Jones et
al. (2000) measures the cortical thickness by solving Laplace’s equations. The last method is
taverage-near. This metric (applied by Freesurfer - the software used on this thesis) computes tnear
for the outer and for the inner surface. These two values are then averaged to produce a
thickness value.
All the five metric were compared and evaluated in terms of variation across the
population or a single subject in [10]. Ordering the different metrics from best to worst, the
ranking is as follows:
1. tlink
2. tlaplace
3. tnormal
4. taverage-near
5. tnear
26
This publication shows that cortical thickness is a reliable method and the most precise
method is tlink due to its ability to minimize variance. Nevertheless, all the metrics had a
specificity of 1. This index indicates a high degree of confidence in any result obtained
regardless of the metric employed.
Several reconstruction methods using these different metrics have been created and
applied in a number of free software packages that are currently available to the neuroscience
community. The goal of these software packages is to create accurate human cortical surface
reconstructions. These brain surface reconstruction software packages aim to create useful and
accurate models of cortical surfaces. While these surfaces are visually appealing, they are most
useful if characteristically accurate. Creating high quality surface reconstructions is a nontrivial
objective, for they must be topologically correct and accurately represent cerebral anatomy.
4.2. Computational Considerations and Methods
Computer graphic methods can be used to design, construct, and display digital three-
dimensional model cortical surfaces. Voxel-based and vector-based approaches are generally
implemented in these models. A MRI scan is composed of voxels, or volume elements. These
voxel cuboids are analogous to a rectangular pixel in a picture element. Each box is associated
with a data value corresponding to a tissue type. With these voxels, one can create a “solid”
volume representative of an object such as the brain. This solid volume image can be
electronically resectioned in any of the orthogonal planes with accurate alignment [11].
Disadvantages to voxel usage are low resolution and contrast relative to histological sections,
but allow for in vivo data collection [12].
Several computational tools have been developed that attempt to automate the image
processing steps required to construct a cortical surface from MRI data. These software
programs have enabled the study of primary motor areas, somatosensory and auditory areas,
perception of faces and objects and other research areas of interest in cortical research.
4.3. Measuring Cortical Thickness with Freesufer: Overview
Software package Freesurfer, developed at the A. Martinos Center for Biomedical Imaging at
Harvard Medical School was used for the analysis of the MR-data. Freesurfer is a set of
software tools for the study of cortical and subcortical anatomy. The software provides various
analysis tools including: representation of the cortical surface between white and gray matter,
representation of the pial surface, segmentation of white matter from the rest of the brain, skull
stripping, B1 bias field correction, nonlinear registration of the cortical surface of an individual
with an sterotaxic atlas, labeling of regions of the cortical surface, statistical analysis of group
morphometry differences, and labeling of subcortical brain structures.
27
From the comparison of different reconstruction methods and free software packages
currently available, FreeSurfer [13] was the one chosen for this study. Compared with INCsurf
[14], BrainVISA [15], BrainVoyager and other softwares FreeSurfer performed best overall but
also required the greatest amount of manual interaction. According to [16] the performance of
each surface extraction method varied within and between subjects, although packages tended
to perform consistently across most metrics. BrainVisa tended to have the highest variability,
but this may arise from the relatively low number of triangles in the final tessellations.
FreeSurfer tends to have the most consistent values for surface area and volume and a larger
brain rendering as indicated by WM sulcal lengths, WM and GM volumes, and WM and GM
surface areas.
Freesurfer is able to automatically measure the thickness of the gray matter of the
human cerebral cortex. The measurement of the thickness is enabled by generating models
both for pial and gray/white surfaces. The thickness of the gray matter at any point of the
surface is given by the distance between this two surfaces. In conjunction with automated
surface reconstruction [17-19] and high-resolution surface-averaging techniques [21], the
measurement of cortical thickness facilitates the use of powerful statistical methods in the
investigation of cortical lesions.
Processing steps include inhomogeneity correction, segmentation and cortical surface
reconstruction [13]. Further processing and analyses includes inflation, flattening, and the
maintenance of components of a surface-based coordinate system [13].
Segmentation invokes a program that classifies the voxel elements of the MRI scan
according to cranial tissue MRI threshold values into three categories: white matter, gray matter,
or unknown. These three membership classes are narrowed to two in a binary framework where
each voxel is either a member of the white matter or not. This graphic display of the binary
segmentation is a white matter filled in volume.
An example of a spherical surface-based coordinate system has been adapted to the
folding pattern of each unique subject by [22] and provides a high level of localization accuracy
of structural and functional features of the human brain. The white matter surface is morphed
onto a unit sphere. The curvature values from the white matter surface are then mapped onto
the sphere. The sphere is then aligned to a template that was created by averaging seven
subjects’ white matter surface reconstruction spheres. This alignment facilitates the parcellation,
or global identification of regions, of a subject’s white and gray matter surfaces. An average
surface can also be created for a set of subjects. This average surface could be useful in a
study investigating an experimental and control group.
28
Chapter 5
FreeSurfer and Image Analysis
FreeSurfer tools and brain image processing tools in general, are best understood in the context
of a workflow used to accomplish a certain task necessitated by a research program. The core
utility of the FreeSurfer tools is to reconstruct a 3D volume of the brain from MRI image slices,
and from that reconstruction, create a cortical surface, and to segment and label the subcortical
structures. It is possible to gather cortical thickness data from the surface structure as well.
Volumetric data on the subcortical structures can also be gathered. There are many other
examples of research data which may be gathered using the FreeSurfer tools in specialized
workflows.
5.1. Purpose and Participants
In this present study we used Freesurfer to address the question of whether the thickness of
cortical gray matter is reduced or increased in patients with neocortical focal epilepsy and, if so,
to determine the regional distribution of such abnormality in each case. A morphometric study
that yields measures of cortical gray matter thickness is applied so that homologous regions can
be averaged and compared within and between subject groups. In order to do so, individual
brains must be aligned, by registering to standardized volumetric space or by using
computational matching strategies that align corresponding locations on the cortical surface.
We present results from automated surface reconstruction, transformation, and high-
resolution intersubject alignment procedures for accurately measuring the thickness of the
cerebral cortex across the entire brain as well as for generating crosssubject statistics in a
coordinate system based on cortical anatomy in a cohort of patients with neocortical focal
dysplasia.
A group of two patients with medically refractory focal epilepsy and no lesion
demonstrated in high resolution brain MRIs were selected. All underwent long term video-EEG
monitoring (27 to 32 electrodes) to document the neurophysiological characteristics of their
epilepsy as part of a comprehensive evaluation for epilepsy surgery. The MRIs consisted of
high resolution (0.4x0.4x1.5 mm) volumetric T1 sequences including the whole brain.
The control group consisted of 23 normal subjects aged 20-40 years and submitted to a
standard high resolution volumetric MRI, as part of a program of normalization of imaging
studies for surgery of epilepsy at the Magnetic Ressonance Imaging center of Caselas.
29
Thickness maps from the 23 normal control subjects were averaged using Freesurfer’s
high-resolution surface-based averaging techniques and compared with the thickness
measurements from the thickness maps of each patient subjects individually. Mean cortical
thickness and variance of mean were calculated at each location. The statistical maps were
generated using a random effects model with 1 degree of freedom for each subject to generate
a t-test for each cortical location.
5.2. Individual Subjects Analysis: Image Processing Stages
5.2.1. Surface and Volume Automated Reconstruction
Before volume processing steps can begin, the raw data from the scan must be converted into a
format recognized by FreeSurfer and placed into a particular directory structure so that each
volume can be found by Freesurfer. This first step converts the MRI from the native scanner
format, to the mgz format.
After this initial conversion, the several stages of surface-based pipeline (described in
detail in [19 ]) can take place. The next few steps begin with the output file of the conversion,
the ORIG volume (orig.mgz). Several intensity normalization steps, along with transformation to
Talairach space are next. Likely white matter points are chosen based their locations in
Talairach space as well as their intensity and the local neighbourhood intensities.
MRI brain images are affected by nonuniform excitation fields, nonuniform reception
sensitivity, patient anatomy and eddy currents, all of which produce nonuniform signal
intensities across the image volume. Correction of such signal-intensity nonuniformities (bias
fields) is necessary before performing intensity-based image segmentation.The intensity at each
voxel is then divided by the estimated bias field at that location in order to remove the effect of
the bias field. The skull and any remaining background noise is removed from the intensity
corrected volume (T1.mgz) generating the BRAINMASK volume (Figure 5.1). This step is
followed by the segmentation of white matter (Figure 5.1) based on intensity constraints. Cutting
planes are chosen to separate the hemispheres from each other as well as to remove the
cerebellum and brain stem. The location of the cutting planes is based on the expected
Talairach location of the corpus callosum and pons, as well as several rules-based algorithms
that encode the expected shape of these structures. After all these steps, an initial surface is
generated for each hemisphere by tracing the outside of the white matter mass. This initial
surface (orig) is then refined correcting and fixing some topological defects. In order to follow
the intensity gradients and have a smooth surface, the orig surface is deformed generating the
white surface (Figure 5.1) The white surface is then nudged following T1 intensity gradients
between the grey matter and CSF creating the pial surface. (Figure 5.2). The distance between
the white and the pial gives us the thickness at each location of cortex.
30
Figure 5.1 – Volumes generated by FreeSurfer workflow. Orig.mgz is the original MRI
volume that undergoes intensity normalization generating T1.mgz volume. After this, the skull
stripping step removes all non-brain structures creating brainmask.mgz. The wm.mgz is the
white matter volume and filled.mgz contains every subcortical mass. Automatic volume label
segments subcortical structures labelling them in a colour volume, aseg.mgz
Figure 5.2 – Pial surface. The white surface (yellow) is nudged to follow the intensity of
gradients between the grey matter and CSF, generating the pial surface (red). The pial surface
shows the outer boundary of the gray matter/CSF. The image on the right is the same file that is
viewed in tkmedit, just represented as a surface image rather than the red outline on the
volume. It can be inspected by rotating it around as desired.
31
In addition to the thickness measures, local curvature and surface normal can also be computed
for each vertex. All these maps can be viewed as overlays in the 3D representations of the orig,
white, pial and inflated surfaced. The inflated surface is the inflation of the pial surface to show
the areas in the sulci (Figure 5.3). This surface can then be registered to the spherical atlas
based on the folding patterns [22].
When it comes to the volume-based stream, it is designed to preprocess MRI volumes
and label subcortical tissue classes. The stream consists of five stages (fully described in [22],
[23])
Figure 5.3 – Inflated Surface. The inflated surface is the inflation of pial surface. This allows a
full inspection of cortex, including areas hidden in the sulci.
The first stage is an affine registration with Talairach space specifically designed to be
insensitive to pathology and to maximize the accuracy of the final segmentation (a different
procedure than the one employed by the surface-based stream). This is followed by an initial
volumetric labeling. The variation in intensity due to the B1 bias field is corrected (again using a
different algorithm than the surface-based stream). Finally, a high dimensional nonlinear
volumetric alignment to the Talairach atlas is performed. After the preprocessing, the volume is
labeled. Both the cortical [23] and the subcortical [22] labeling use the same basic algorithm
based on both a subject-independent probabilistic atlas and subject-specific measured values.
32
The volume-based stream has evolved somewhat independently from the surface-based
stream. The atlas is built from a training set of subjects whose brains (surfaces or volumes)
have been labeled by hand. For each vertex exists the label that was assigned to each subject
and the measured value (or values) for each subject. Three types of probabilities are then
computed at each point. The classification of each point in space to a given label for a given
data set is achieved by finding the segmentation that maximizes the probability of input given
the prior probabilities from the training set. The results are shown in Figure 5.4. In 5.4A, the
volumetric labeling shows several subcortical structures (putamen, hypocampus, ventricles,
etc). Note that all of the white matter is considered a single label, as is all of the cortical gray
matter for each cortical hemisphere.
Figure 5.4 – A. Volume-based labeling. Note that cortical gray matter and white mater
are represented by single classes. Also note that there are separate labels for the structures in
each hemisphere. B. Surface-based labeling.
5.2.2. Workflow and Manual Edits
The reconstruction process is fully automated. Nevertheless, most data is prone to failures
during the reconstruction. Therefore it is convenient to break this total process into three smaller
pieces in order to check and correct errors along the way. The recon-all script is used to
process raw data scan, segment the white matter, generate surfaces from the segmented data,
and output spherical or flattened representations of the surfaces.
Tkmedit and tksurfer are the programs used to visually inspect the data at key points
during the reconstruction process. Tkmedit provides an interface to view and edit voxels on 2D
scan slices, and tksurfer is an interface to view the 3D generated surfaces. These programs
also provide the necessary tools to correct failures in reconstruction with manual edits. The
33
reconstruction steps can fail for many reasons including differing anatomy between individuals
and scan quality. A list of the most common problems in the output data is described below.
5.2.2.1. Troubleshooting
The most common problems belong to one of the following groups:
Segmentation errors: these include problems such as excluded or misclassified
white matter. The white matter is not segmented correctly: sometimes voxels that
should be white matter are excluded, and other times voxels that should not be white
matter are included in error. Either of these occurences can be fixed with simple manual
edits
Figure 5.5 – Example of a group of voxels included as white matter. They are surrounded by the
white surface (yellow line). This group lies outside of the pial surface (red line) and is clearly not
white matter. In order to correct this inaccuracy it is necessary to delete the voxels which are
not part of the white matter.
To fix this problem the missing/additional voxels in the wm.mgz volume (volume that includes all
the voxels classified as white matter) have to be filled/erased in tkmedit. The procedure has to
be done in every slice of the wm.mgz volume where the inaccuracy appears.
1. Skull strip errors: all the cases where the skull stripping step is not accurate (either
removes more than just the skull, causing part of the brain to be removed as well, or too little,
leaving behind portions of the skull). The watershed algorithm is used during the skull stripping
34
step to find a boundary between the brain and skull. This algorithm removes automatically the
skull and other non-brain tissues according to the preflooding height value. This parameter is
the watershed threshold which determines how aggressive the algorithm is.The mri_watershed
program uses a default preflooding height of 25 percent. Whenever the skull stripping process
fails, this parameter can be adjusted. If we want the algorithm to be more conservative (i.e. if
part of the brain has been removed), we will want to make that number larger than 25. If we
want the algorithm to be more aggressive (i.e. part of the skull has been left behind), we will
want to make the height less than 25. Nevertheless, sometimes this is not enough. When the
skull stripping process has left just a few slices with either missing brain regions or too much
skull the brainmask volume can be edited manually using tkmedit. This is done by adding or
deleting the missing or extra voxels in every affected slices.
Figure 5.6 – Example of a skull stripping error. An entire hemisphere of the cerebellum has
been stripped away along with the skull. The picture on the right is missing the right hemisphere
of the cerebellum and that it is present in the T1.mgz volume (the first picture).
2. Intensity normalization: when the intensity normalization step fails because it cannot
determine the proper intensity for white matter resulting in an erroneous white matter
segmentation. Figure 5.7. is an example of a subject that needs some control points in
order to ensure that the voxels are normalized correctly and then included in the
wm.mgz volume. Control points should be placed in a region where the wm intensity is
lower than it should be (that is, having a voxel value less than 110). This can be done
using tkmedit, selecting the Edit Control Points tool. Control points should be placed
around the trouble areas, spaced out throughout the brain on different slices.
35
3. Pial Surface: these include problems such as pial surface including non-cortex within
the boundaries or white and pial surfaces crossing each other. The pial surface is
created by expanding the white matter surface so that it closely follows the gray-CSF
intensity gradient as found in the brainmask.mgz volume. Once an accurate white
surface is created then we can work on correcting the pial surface if needed. To check
the pial surface, it may be loaded into tkmedit and viewed along with the brainmask.mgz
volume. If the surface appears not to follow the gray-CSF boundary in the volume, edits
may be required. To fix this type of errors the offending voxels from the brainmask.mgz
volume can simply be edited away. The “edit voxels” tool has to be selected, we need to
find the place in the image where the inaccuracy is, and add or delete voxels throughout
the slices.
Figure 5.7 – Intensity normalization failure. Here is an example of a subject that needs some
control points in order to ensure that the voxels are normalized correctly and then included in
the wm.mgz volume. The image on the right shows pial and white surfaces after adding control
points.
36
Figure 5.8 – Pial surface error. The bright diagonal line in this slice has caused the pial surface
to expand past the actual pial boundary. This is the result of a bad segmentation incorporating a
piece of the dura within the pial surface.
Generally most of the images require manual intervention. The cerebellum is removed
by automated processing and must be corrected manually as well as the fornix and optic nerve
is not removed most of the times. The intensity normalization also tends to fail resulting in
regions of excluded white matter and consequently gray matter. All these manual edits require a
user already familiarized with the software and are very time consuming. A single subject may
need as much as 1000 control points, which takes about one day to add them throughout the
slices, and another day to re-process the images.
37
Figure 5.9 - A diagrammatic overview of the main FreeSurfer process, proceeding
generally from top to bottom. Cube shapes: volume of voxels (or equivalently a stack of 2-D
images).Shapes with axis arrows: Surface files.
38
5.3. Surface-based Group Analysis
After all surface reconstruction has been completed for every subject in the study, it is possible
to perform inter-subject/group averaging and interference on cortical surface. Using the
spherical representation generated during the workflow for each subject, the folding patterns of
the individual are aligned with that of the average. Since the energy functional used to generate
the spherical transformation ensures that the transformation is invertible, a standard spherical
coordinate system is established to index a point uniquely on the cortical surface. After having
an average subject aligned, group analysis is performed using a general linear model technique
at each surface vertex.
5.3.1 Processing Stages
Mri_glmfit is a FreeSurfer tool which models the data as a linear combination of effects related
to variables of interest, confounds and errors, and permits statistical inferences to be made
about effects of interest in relation to error variance. For group analysis, this technique fits a
general linear model (GLM) at each surface vertex to explain the data from all subjects in the
study. In this section, a brief overview of linear modelling is presented and mri_glmfit is
described for estimating a linear model and testing hypotheses.
There are four main steps that have to be taken in consideration in group analysis:
1. Specifying the subjects and resample them into a common space: Assemble
Data I
2. Specifying the surface measures and smoothing: Assemble Data II
3. Specifying the model and Contrasts: Group Linear Model
4. Fitting model (estimate)
Figure 5.10 – Intersubject averaging processing stages
39
5.3.1.1. Assemble Data I
Regardless of the analysis that is being performed, an average subject has to be created from
all the participants in the study. This average will be used as a target subject upon which the
results of the group analysis can be output and viewed. All the participants have to be
registered in order to find this common space. The averaging process begins with the alignment
of the sulci across the subjects in the spherical space, after which is computed the average
coordinate for each vertex.
In this study we have created 3 average subjects: average_normais, average_patient1
and average_patient2. The first contains all the normal subjects, the second contains all the
normal subjects plus the Patient 1 and the last contains all the normal subjects plus the Patient
2.
5.3.1.2. Assemble Data II
Once the average subject is made the preprocessing steps can be followed. The first step is
creating a FreeSurfer Group Descriptor File (FSGDF). This file provides a way to describe a
group of subjects and their accompanying data. This includes the names of all the input
subjects as well as class membership. In this case, there were only two classes: Normal and
Dysplasia. The FSGD file is a way to specify the design matrix used in the group analysis, as
well as to keep track of the group membership and covariant definitions. The next step will use
mris_preproc, a FreeSurfer tool, to assemble the data into a single file in the common surface
space, the average subject. In this step the output file will have the average values of the
desired measure in the subjects’ common space. In our case, a map of average cortical
thickness across the subjects is created. This is of special interest for the average_normal
group, our control group. The final preprocessing step is to do surface smoothing. The
mri_surf2surf FreeSurfer tool is used along with the output from the mris_preproc and our
average subject. The smoothing applied is a small surface-based Gaussian blurring kernel of 7
mm. The output is a smoothed thickness map for the average surface.
5.3.1.3. Group Linear Model
Linear modeling describes the observed data as a linear combination of explanatory factors plus
noise, and determines how well that description explains the data being analyzed. For group
morphometric analysis, the observed data is comprised of a set of surface measures (in this
case, cortical thickness) at each vertex in a surface model, for each subject in the group. This
data can be organized as a set of vectors, each associated with a different vertex in the surface
model, and containing a surface measurement for every subject in the group at the
corresponding vertex. FreeSurfer uses a linear model given by y=X*beta, where y is the vector
observed data (thicknesses for each subject at a vertex), X is the known design matrix (class
Normal vs Dysplasia), and beta is the vector of unknown parameter estimates (PEs). Each
40
column of X corresponds to a different explanatory variable (also called a regressor or a
covariate).
The analysis/estimation is then the process of estimating beta given the data y and the
design matrix X. As typically formulated and solved, the estimation step produces a set of
estimates of the PEs, which in turn are used in hypothesis testing. Then, estimates of the PEs
can be converted into statistical parametric maps. A Null Hypothesis (H0) is constructed with a
user-specified contrast matrix C, which assigns a contrast weight to each column of the design
matrix. Depending on the contrast vector, the PE value of the column associated with the
design matrix at each vertex is computed, yielding a t-value. A t-value map is produced for
each explanatory variable of interest. In our case, the map indicates how strongly vertices on
the surface are related to the diagnosis, comparing normal subjects with the patient. One PE is
subtracted from another (Normal from Dysplasia) defining the contrast vector as [1 -1], then a
combined standard error is computed, and a t-map generated.
41
6. Results
6.1. Validation of Significance Maps
To ensure the reliability of this analysis, a fake constant thickness map was created. We
reproduced the same analysis that is applied to the patient. Defining the contrast vector as [ 1 -
1] in order to compare the fake subject with the control group, the significance t-maps were
generated. We would expect that areas far from the constant thickness value would appear in
the significance map.
Figure 6.1 – Validation of significance maps. The picture on the left is the output significance
map of the preformed t-test. The blue areas are negative significance values and red areas
positive ones. In this case, the blue areas should correspond to thickness values superior to 2
mm and red areas should correspond to inferior ones. The map on the right is the cortical
thickness map of the control group. In fact, grey zones in the thickness map appear as red
areas in the significance map, as well as grey thickness values are red significance values.
42
6.2. Control Group
After all the individual images were preprocessed by FreeSurfer, the individual
thickness estimates from the results of this analysis were combined across the 23 normal by
using the high-resolution, surface-based averaging technique that aligns cortical folding patterns
[24]. The 23 normal subjects were extracted out of a group of 31 subjects (16 M, 15 F) aged 19-
49 years and submitted to a standard high resolution volumetric MRI, as part of a program of
normalization of imaging studies for surgery of epilepsy at the Magnetic Ressonance Imaging
center of Caselas. A 7 mm fwhm Gaussian blurring kernel (recommended by [25] as being the
kernel which minimizes the standard deviation) was used for smoothing of the cortical thickness
measures surface. Thickness measures were mapped to the inflated surface of each
participant's brain reconstruction, allowing visualization of data across the entire cortical surface
(i.e.gyri and sulci) without being obscured by cortical folding.
The following images are average cortical thickness maps of this group. A first visual
inspection of this maps show a light asymmetry between the two hemispheres. In fact, the right
one appears to have, in general, an average cortical thickness inferior to the left one. It is clear
that the latter has a much darker appearance. This result is consistent with published findings
[26], which reveal global and regionally specific differences between hemispheres, with
generally thicker cortex in the left hemisphere.
Nevertheless, regardless of the hemisphere, most of the cortex shows a pattern of
patches of decreased and increased thickness. The primary sensory areas (more specifically
the auditory and somato-sensory areas) are among the thinnest in the cortex – light blue in the
map. These findings are also consistent with the literature [25], [27]
An illustration of the variability of these results across the cortex is given in Figure 6.3,
which shows the spatial distribution of the cross-subject standard deviations of the thickness
measurements. As can be seen, the measurements are quite consistent across subjects, with a
standard deviation of less than 0.5 mm over much of the cortex (grey areas). No major
asymmetries were apparent between hemispheres. Nevertheless, notice that the majority of the
variance is localized in areas of higher cortical thickness.
43
Figure 6.2 – Inflated surface with thickness map of the average control group.
Left Hemisphere Right Hemisphere
44
Figure 6.3 - Map of the standard deviations of the thickness measurements across 23 subjects
6.3. Overall picture of the approach
In this context of pre-surgical analysis for epilepsy, our main goal is to find thickness
abnormalities in well defined cortical areas which may lead us to the dysplasic lesions. These
lesions are malformations in the cortical development that can be recognized in the MRI as
areas of either increased or decreased cortical thickness values. Although the latter is less
frequent than the former, it is also common.
The principle underlying our approach consists in finding any abnormality regarding the
cortical thickness of the patient. Further analysis is required to successfully differentiate lesional
tissue from healthy cortex because of the poor difference in terms of grey level. For example, in
heterogeneous lesions, the white/grey matter interface can be blurry, which may lead to a bad
Left Hemisphere Right Hemisphere
45
white and pial surface generation. A simple visual inspection of cortical thickness maps is not
enough to delineate the abnormal area.
For that purpose, the method we use is made up of four different separate steps:
1. Visual inspection of the thickness patterns in order to identify areas of
asymmetry between hemispheres or through changes in the normal cortical
patch pattern
2. Analyze the detected abnormal areas in the 3D MRI, looking for eventual
problems during the surface reconstruction
3. Define Regions of Interest in the surface in order to perform an analysis in
terms of thickness values, variability differences, sulcal or gyral location, etc…
4. Define the appropriate statistic for the case
Flowchart in Appendix A.
6.3.1. Patient 1
Patient 1 is a 17 years old Caucasian female with a family history of migraines and personal
antecedents of febrile convulsions. Within the last five years she suffered from daily visual crisis
and cephalea. At age 9 the patient presented “visualization of bright ball” crisis, with deformed
images of “people inside the ball”, a year after she started presenting partial complex crisis.
From age 9 to 12 the cephalea became more frequent and Propanol resistant. Brain magnetic
resonance scan and EEG results were normal.
Visual inspection of the thickness patterns
The processing of the MRIs from both the patient and the control group produced a final colour
representation of the cortical thickness of each hemisphere. From the inspection of the patient’s
maps and its interhemispheric comparison, an abnormal pattern of reduced cortical thickness
was spotted in the right occipital lobe.
46
Figure 6.4 – Cortical Thickness map of Patient 1. From interhemispheric comparison a light blue
abnormal pattern is found in only one of the hemispheres.
The abnormal area was traced in order to compare it with the control group. This step is
of extreme relevance because it is indeed very hard to visually compare a single subject inflated
surface with an average group due to their surface’s topological differences.
Left Hemisphere Right Hemisphere
47
Figure 6.5 - Cortical Thickness map of Patient 1 vs Control Group. The previously spotted light
blue abnormal regional is also not present in the control group map.
Regional comparisons between the patient and the normal group confirm that the
former has a cortical thinning within the region of interest: the patient presents a single colour
instead of the patchy pattern found in the average normal subject. Notice that not all the region
inside the defined ROI is necessarily an atrophy. In fact, a great part of that light blue area in the
patient’s map corresponds to an also light blue area in the group’s one. In that, the defined
region must be shrunk and better determined in order to precisely delineate the lesion’s area.
48
Figure 6.6 – Abnormal cortical thickness pattern in the volumetric MRI. The previously defined
label (in yellow and purple in the cortical thickness maps) is here shown as the green area.
These magnetic resonance images seem to confirm the atrophy.
Define Regions of Interest
Delimitating the lesion is anything but linear inasmuch as cortical thickness varies with
geometry, more specifically, gyral regions are thicker than sulcal ones. In order to evaluate if
this fact has any effect in our defined region, the location of the potential atrophy was studied
and the region of interest divided in two different parts: the gyral and the sulcal one.
Figure 6.7 – Label location in the inflated surface. The pattern shows the sulcal depth where the
red regions are sulcal and the green regions are gyral.
49
The label’s sulcal region is identical in both hemispheres in terms of cortical thickness
pattern. This pattern is also very similar to the average normal group one. Therefore, this region
can be removed of the region of interest. Nevertheless, this simplification is not enough yet.
Actually, it is clear from the plot of the mean cortical thickness across subjects (Figure 6.8) that
this whole area does not define an atrophy. The patient has a mean cortical thickness within the
range of the control group mean cortical thickness. This means that the lesion has not been well
located yet.
Figure 6.8 – Mean Cortical Thickness distribution within the defined label. Red squares
represent normal subjects and the blue circle is Patient 1. Patient’s value of cortical thickness
does not fall out of the control group distribution. The abnormal pattern found in the thickness
map is not well defined, as it does not produce the expected statistical values for an atrophy.
Comparing cortical thickness pattern within the new defined gyral label with the control
group, a much smaller label is defined (Figure 6.9). This label contains only the vertexes where
the patient has inferior cortical thickness values than most of the normal subjects. These
vertexes were manually found by visual inspection of the mean cortical thickness distribution.
With this atrophic region delineated, the real boundaries of the lesion can be drawn by carefully
inflating the label. Transferring to the 3D MRI volume, it is possible to enlarge the initial area
and identify the whole region that strikes us as being part of the lesion. From this inspection, a
final label is found with acceptable mean cortical thickness values (Figure 6.10).
50
Figure 6.9 – Atrophy label in the inflated surface. The red/green regions are gyral and
sulcal areas. The former label, delimitated in purple, between gyrus and sulci. In yellow is the
new defined gyral label. This label contains only the vertexes where the patient has inferior
cortical thickness values than most of the normal subjects.
Figure 6.10 – Mean cortical thickness values within the new found label. The patient’s is below
most of the normal subjects. This fact associated with a low standard deviation value seems to
confirm the suspicions of an atrophic lesion in the defined area.
51
Figure 6.11 – Significance map. The atrophy is delineated in purple. Notice the high significance
values (red) within the label area. In other words, statistically patient 1 presents lower cortical
thickness values when compared with control group. However, this map has a very high
threshold, which explains the several other coloured areas.
Figure 6.12 – Abnormal areas (in green) in the volumetric MRI. The coloured area presented
here is the same area delimitated by label in the inflated surface. The pial (red) and white
(yellow) surfaces are also shown. From visual inspection it is clear this area is a lesion with
reduced cortical thickness.
52
6.3.2. Patient 2
Patient 2 is a 14 years old female product of a gemelar pregnancy. At the age of 1 she started
having partial complex epilepsy crisis, rarely with secondary generalization. During her first
years she showed signs of brief nausea or vomit, tonical deviation of the head and superior
member with or without fever, for periods as long as 15 minutes. These crises could happen 2
to 3 times a week or not happen for as long as 2 or 3 months. She developed recurrent seizures
with variable intensity at the age of 9 years. These seizures sometimes presented as dizziness
only, other times with activity interruption, vague stare and a tonical deviation of the head. The
frequency of these crisis was still variable, but with a clear relation with the menstrual period. In
2004 the patient had 4 to 11 crises/month, from 2005 to 2006 she had 3 to 15 brief crises per
moth, and during the last year the seizures became as frequent as 1 to crises per day for more
extended periods of time. Her electroencephalogram (EEG) showed electrogenesis of discreetly
slow base and abundant paroxistic activity, with abrupt spike-and-wave bursts over posterior
head regions in both hemispheres. A brain magnetic resonance scan showed a cortical-
subcortical occipital lesion.
Visual inspection of the thickness patterns
Comparing the patient’s individual thickness estimates with the control group, two different
areas were spotted as potential abnormal thickness patterns. The first one is a small area of
reduced cortical thickness in the left occipital lobe (Figure 6.13). The second one is a slightly
bigger area, also in the left hemisphere, in the interior occipital lobe (Figure 6.14).
Figure 6.13 – Cortical Thickness Maps in inflated surface of the control group (left) and patient 2
(right). From visual inspection, a darker blue pattern identified in the control group does not
seem to appear in the patient’s map
53
Figure 6.14 - Cortical Thickness Maps in inflated surface of the control group (left) and patient 2
(right). From visual inspection, a light blue pattern is identified in the control group does not
seem to appear in the patient’s map
In order to confirm these suspicions, the spotted abnormal pattern is marked out in both
regions. The first region was traced on the control group and transposed to the patient’s inflated
surface. After tracing the apparently different pattern on the control group thickness map, it is
possible to carry the traced region over to Patient’s 2 map. This darker blue area found on the
control group actually also corresponded to a darker blue area on the patient’s thickness map
(Figure 6.15). In other words, there may not be a difference between patterns. From mean
cortical thickness estimation no major discrepancy was found between the group and the
patient’s values. As the label has already a reduced size, there is no need to continue the
analysis on this area. The difference of pattern identified in the cortical thickness was not a
lesion.
When it comes to the second potential lesion, the significance map (Figure 6.16) seems
to be consistent with the suspicions. The patient presents a significant inferior cortical thickness
region within the delimitated label (red spot).
54
Figure 6.26 -
Figure 6.15 – Cortical Thickness Maps in inflated surface of the control group (left) and patient 2
(right). The potential lesion found by direct visual inspection (delineated in control group) was
transposed to patient 2 surface (yellow label in the image on the right). The pattern within the
patient’s label is also partially dark blue, eliminating the possibility of this being a lesion.
Figure 6.16 and 6.17 – Significance map (left) and mean cortical thickness distribution within the
delimitated label (right). The atrophy is delineated in purple. Notice the high significance values
(red) within the label area. In other words, statistically patient 2 presents lower cortical thickness
values when compared with control group. This is validated by the mean cortical thickness plot.
Within the label, patient 2 has lower cortical thickness values than every other normal subject.
However, notice the high standard deviation associated to this measure.
55
Analysis of the detected abnormal areas in the 3D MRI
Problems with respect to ROI analysis arise because of the manual way in which the ROIs are
placed. In the first step of this analysis the ROI is placed in a particular region, where a cortical
thickness abnormality seemed to be present. In order to confirm the location of the potentially
found lesion, visual inspection and analysis of its placement in the 3D MRI volume is required.
As the first region has already been excluded of our analysis, we will only focus on the
second. This region of interest, despite the unusual pattern found by comparison with the
normal group and the low mean cortical thickness value, had no visible lesion in the MRI. This
label can also be excluded of the analysis.
Although none of the former identified labels corresponded to a real lesion, another
potential atrophy was found by visual inspection of the MRI volume. Analyzing the MRI an
ambiguous region is identified as lesional. There is a clear heterogeneous lesion in the interior
face of the left occipital lobe. Cortical thickness changes were not identified in the first step
because of the blurred grey/white matter interface. FreeSurfer’s automated grey/white matter
segmentation seems to have failed and the generated surfaces are erroneous. Thus, it is not
possible to continue an analysis strictly based on cortical thickness maps comparisons.
Figure 6.18 – Patient 2 volumetric MRI. The red circled area is a clear
heterogeneous lesion in the occipital left lobe.
56
Figure 6.19 - Volumetric MRI of Patient 2. The pial (red) and white (yellow) surfaces are
shown. The heterogeneous lesion in the interior face of the left occipital lobe, is poorly
segmented. FreeSurfer’s automated grey/white matter segmentation seems to have failed and
the generated surfaces are erroneous.
As it is easily identified, the lesional area was delineated in the MRI. In order to define a
more reliable region, we analyzed the cortical thickness maps once more. From
interhemispherical visual comparison a smaller region of interest within the previously defined
lesional area is delimitated.
Define Regions of Interest
Comparing with the control group, no abnormal pattern is identified within the label. However, if
compared with any normal subject MRI, the patient’s label clearly delimitates an anomaly. In the
significance maps, there appear several regions with positive and negative values of
significance, however none of these are related to the label (Figure 6.21).
This result comes as no surprise, since the pial and white surfaces reconstruction
seems to have failed. Patient’s mean cortical thickness values also appear normal compared
with the control group (Figure 6.22).
57
Figure 6.20 – New label definition. From MRI analysis a new label is defined. Cortical thickness
patterns do not seem to differ when comparing patient 2 with control group thickness map.
Figure 6.21 – Significance map with new label. Even with a low threshold, no significance value
is related to the region of interest (in white).
.
58
Figure 6.22 – Mean cortical thickness distribution within the potential lesion region. The patient’s
value also seems normal when compared with the control group. Nevertheless, notice that the
standard deviation is extremely high.
Figure 6.23 – Label location in the MRI volume. The green area delimitates the defined label. It
is coincident with the heterogeneous lesion.
59
Parcelation
In order to confirm the weak boundary issue, the standard deviation was computed all across
the cortex. The basic idea here is to compute the dispersion associated to thickness
measurements in order to determine the liability of the white and pial surfaces determination in
the lesional area. FreeSurfer offers a completely automated cortex segmentation tool. It
segments the whole cortex in several anatomical regions. FreeSurfer can also estimate
thickness measures (average cortical thickness ± standard deviation) for each label. A list of all
the computed values is in Table 6.1.
Notice that the three areas stand out as having higher levels of dispersion. These
regions are all located near the lesion area confirming the suspicion of erroneous surface
determination. In cases of heterogeneous lesions such as this, this method can be very useful
to determine a rough location of the lesion since the algorithm seems to have troubles defining
these areas.
60
Cortex Region Thickness Stdv Stdv (%)
Medial Orbitofrontal 2.877 0.937 32.569
Caudal Anterior Cingulate 2.663 0.542 20.353
Caudal Middle Frontal 2.483 0.668 26.903
Cuneus 2.167 0.802 37.010
Entorhinal 3.781 0.502 13.277
Postcentral 2.077 0.743 35.773
Frontal Pole 4.071 0.557 13.682
Fusiform 2.867 0.705 24.590
Rostral Anterior Cingulate 2.840 0.998 35.141
Rostral Middle Frontal 2.755 0.744 27.005
Inferior Parietal 2.687 0.764 28.433
Inferior Temporal 2.869 0.673 23.458
Supramarfinal 2.481 0.751 30.270
Isthmuscingulate 3.156 0.770 24.398
Lateral Occipital 2.444 0.711 29.092
Lateral Orbitofrontal 2.797 0.650 23.239
Transverse Temporal 2.177 0.452 20.763
Lingual 2.110 0.867 41.090
Middle Temporal 3.121 0.684 21.916
Paracentral 2.527 0.749 29.640
Parahippocampal 2.693 0.582 21.612
Parsorbitalis 2.998 0.583 19.446
Pericalcarine 2.227 0.868 38.976
Posterior Cingulate 3.055 0.748 24.484
Precentral 2.317 0.636 27.449
Superior Frontal 2.995 0.812 27.112
Superior Temporal 2.674 0.674 25.206
Temporal Pole 3.001 0.724 24.125
Table 6.1 – Cortical Thickness measures within FreeSurfer’s several parcelation labels. Higher
standard deviation values are in bold.
61
Figure 6.24 – Cuneus label in inflated surface and mean thickness values distribution. Notice
the high standard deviation values.
Figure 6.25 - Lingual label in inflated surface and mean thickness values distribution. Notice the
high standard deviation values.
62
Figure 6.26 – Pericalcarine label in inflated surface and mean thickness values distribution.
Notice the high standard deviation values.
Figure 6.27 – Lesion label in inflated surface. Notice the label is near every high stdv values
areas.
6.4. Discussion
In this section, we proposed an approach to improve determination of the lesion location. The
method relies on MRI-feature knowledge and a surface-based analysis of cortical thickness
maps. The method requires a great amount of user interaction. Most of the analysis is based on
63
visual inspection and evaluation. This approach can be improved if instead of performing the
parcelation in the end of the analysis, we do it in the beginning. Evaluation of the parcelation
results can give us a hint of where the lesion is located. This would spare the user of looking
through the whole cortex in search of abnormal patterns. In that, our final proposal is that the
analysis is conducted according to the flowchart in Appendix B.
Although this analysis is time-consuming and very user-dependent it can be a helpful
tool to improve determination of the epileptic area in patients with neocortical focal epilepsy.
64
Chapter 7
Discussion and conclusions
7.1 General discussion and conclusions
In this manuscript we proposed and evaluated a novel semi-automated workflow for
locating small cortical lesions in patients with focal neocortical epilepsy on high-resolution MRI.
The precise delineation is crucial for clinical diagnosis and surgical planning, but their MRI
features make this task challenging. The blurred grey-white matter interface and the absence of
evident boundaries between dysplasic tissues and healthy cortex may lead to misdiagnoses;
moreover, because the cortex is only a few millimetres thick and is highly irregular, it is also
very difficult to study it directly by conventional MRI evaluation [28]. In addition, most of the
cortex lies hidden in sulci and is not visible in direct visual inspection at the time of the surgery.
To figure out these difficulties and successfully find the lesions, we made use of software
package Freesurfer and many of its tools. The procedures of surface cortical reconstruction
have already been proved reliable in several studies [29, 30] when using high quality datasets.
This surface based method improves visualization when compared with conventional volumetric
representation of the brain. The procedure of cortical surface inflation allows a complete
visualization of the cortex and eventual abnormalities along the surface are more easily
identified and evaluated. Furthermore, topological relations are preserved, allowing
simultaneous visualization of cortical point in the inflated/pial/white surface and in the volumetric
image, improving the ability to evaluate the significance of the findings.
Measuring thickness of the cortex, as well as the segmenting it for parcelation, are
completely automatic procedures, which gives a certain reliability to the method.
We proposed first to compute cortical thickness using a region-based analysis. Cortical
thickness values and related standard deviations were automatically estimated. Then, the
results were analysed so as to find regions with significant values. A careful inspection of the
different patterns of cortical thickness within the significant region in each hemisphere of the
patient was done by comparing visually with the control group. This type of analysis provides a
certain level of automatization, making the whole process of lesion search more precise and
less prone to visual errors. The visual inspection allows not only detection of local spots of
increased/decreased thickness, but also differences in the normal spatial pattern along the
cortex. Additional strategies, as sulcal/gyral location and definition of statistical analysis within
certain regions of interest were implemented in order to improve and assure lesion coverage.
65
The co- registration procedure employed here makes use of the pattern of folding
across the entire cortical surface, and is a fully automated process done by FreeSurfer. Once
the initial alignment of all the cortical surfaces has been accomplished, they are averaged in
order to generate a probabilistic atlas as the target for the final registration procedure. This is an
extremely powerful tool for group analysis and creation of a control group database.
Our results suggest that the high-resolution volumetric MRI datasets obtained in these
patients contain more information than the one that is extracted by the traditional visual
analysis, and that further computer analysis is able to demonstrate previously unknown cortical
lesions. The obtained results encourage us to consider this technique as a useful tool for visual
diagnosis. This semi-automated method can constitute an objective criterion and may unveil
subtle lesional areas that could have been overlooked by the expert. Moreover, with
improvements and further validation, this method may be part of a pre-surgical protocol defining
the extent of the tissue to resect.
7.2 Future work
Our results suggest that this methodology could be new tool for locating small cortical
lesions in patients with focal neocortical epilepsy. Nevertheless, it was only tested in two
patients and the approach was quite different for both of them. In fact, the initial proposed
methodology was changed and improved after analysing Patient 2. Further improvements could
definitely be accomplished if more patients were inspected and evaluated.
An additional study of the smoothing kernel applied may also constitute enhance the
results obtained and improve the performance of our proposed procedure. An optimal fwhm
value could be determined, instead of using the literature proposed one. The gausssian kernel
used during group analysis, has a huge influence in the obtained results. This could also give us
more encouraging statistical values.
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Appendix A
Flowchart 1
Lesion
Found
70
Appendix B
Flowchart 2
Lesion Found
