MNTP Magnetoencephalography (MEG) · What does MEG measure? Post-Synaptic Intracellular Current Summed over 10000 or more neurons Organized Tangentially to the Scalp . Action potentials:

MNTP

Magnetoencephalography (MEG)

Erika J C Laing, MS

MEG

magnetoencephalography

A changing

electric field

generates a

magnetic field

M is for Magnetism

Right Hand Rule

A changing

electric field

generates a

magnetic field

M is for Magnetism

What does MEG measure?

Post-Synaptic Intracellular Current

Summed over 10000 or more neurons Organized Tangentially to the Scalp

Action potentials:

fields diminish too rapidly to sum

Pre-synaptic Post-synaptic

Postsynaptic currents:

fields diminish gradually



Summed over 10000 or more neurons

Impressed or

Ionic Current

Intracellular or

Primary Current Extracellular or

Volume Current

MEG

+

-

Intracellular Current


Summed over 10000 or more neurons

~ 104-5

activated cells

Many neurons firing

in synchrony can

generate a magnetic

field observable at

the surface

9

MEG

MEG observes

currents

tangential to

the surface

best.

Magnetic fields from deep currents are

weaker at the surface


Organized Tangentially to the Scalp



Summed over 10000 or more neurons Organized Tangentially to the Scalp

EEG vs MEG

EEG = Electroencephalography

Post-Synaptic Extracellular Current

Summed over 10000 or more neurons Any orientation but signal is impeded by skull, skin, fat

EEG Vs MEG

12

Implications: EEG has lower spatial resolution

EEG can capture some signals MEG has poor sensitivity for

COMPLEMENTARY TOOLS

MEG and EEG Distributions

Noninvasive

2-3mm spatial

resolution

1ms temporal

resolution

Recording magnetic fields

SQUID Superconducting

QUantum

Interference

Device

SQUID works via

INDUCTION

to be superconductive,

must be cooled by

liquid helium


Induction


QUantum

Interference

Device



QUantum

Interference

Device

Types of sensors

Magnetometer - General magnetic fields

- Very sensitive

- Elekta, 4D

Planar Gradiometer - Focal magnetic fields

- Most sensitive to fields directly underneath it

- Elekta

Axial Gradiometer -Focal magnetic fields

- Most sensitive to fields directly underneath it

- CTF

MEG Sensor array

306 102 locations

Magnetic shielding

Reference system

Moving Magnetic Dipoles Power Lines orother current lines

Physical shielding

3-ply µ-metal room

Magnetically Shielded Room (MSR)

The Machine

22

No Magnet

Quiet Machine makes no noise

Participant can sit or

lay down

Can record 128 EEG

simultaneously

Acquisition

Co-registration

Preprocessing

And Averaging

Localization

Monitoring Physiological

Artifacts

Defining the Head Shape

Recording the Head

Position

Data acquisition and analysis steps

Acquisition procedure

Subject Preparation

HPI Coils

(Head Position Indicator)

+ 4 HPI Coils, glued to head

+ A brief electrical pulse is sent to

the coils during acquisition. The

sensors record the pulses and

interpolates the head position

relative to the sensor helmet.


Subject Preparation

EEG Electrodes

to collect artifacts

+ Vertical EOG

Above and below eyes – Eye Blinks

+ Horizontal EOG

Left and Right of eyes – Saccades

+ ECG

On chest – Heartbeat

+ EMG

On muscle of interest – Muscle Movement

+ don’t forget reference and ground


Digitization

Digitize to make a 3D digital head shape file

+ 3 Fiducial Landmarks

Nasion, Left and Right Preauricular (ear)

+ 4 HPI Coils

+ Any additional EEG

+ A bunch of extra ones to get a good head

shape!


Acquiring MEG Signals


Acquiring MEG Signals

Earphones

Electrical

Stimulator

Presentation

Screen (moved to front!)

Also:

Button Pads

Button Gloves

Manual Tapper

Stimulus delivered

by E-Prime,

PsychToolBox, etc.

Collecting Data

29

Collecting Data

30

Collecting Data

31

Collecting Data

32

Acquisition

Co-registration

Preprocessing

And Averaging

Localization

Fitting Digitization

Points to Structural MRI


Structural MRI

Fit the

Digitization

Points to

Structural MRI

Coregistration to MRI

Acquisition

Co-registration

Preprocessing

And Averaging

Localization

Filtering

Artifact Rejection

Selecting a time region

and baseline

Averaging by condition


Time frame: 10 seconds

Raw continuous data


Raw continuous data

cardiac

drift

eye blink Buzz =

Line

noise

60Hz


Raw continuous data

Buzz =

Line

noise

60Hz

Neuronal Oscillations

Delta: 1-4Hz

Theta: 5-7Hz

Alpha: 8-12Hz

Beta: 15-29Hz

Gamma1: 30-59Hz

Gamma2: 60-90Hz

Cognition

Sleep

Neuronal

Injury


Raw continuous data


Filtered 1-40 Hz

Reference system


Sources of noise

saccade

swallowing

alpha

eye blink

Types of preprocessing

44

Reference system


Constant Sources of Noise:

SSP with Empty Room

Measurement

Patient artifact:

ICA or simply reject

Random external artifact:

MaxFilter (SSS)

Signal Space Projection (SSP)

45

Reference system

Moving Magnetic Dipoles Power Lines orother current lines Constant Sources of Noise:

SSP with Empty Room

Measurement Do a components analysis

of the signal when no

person is in the room.

Those signals are

presumed to regularly be

present.

Use Signal Space

Projection (SSP) to

remove those components

from the data.


Filtered 1-40 Hz


Filtered 1-40 Hz & SSP of Empty Room


48

Reference system


Patient artifact:

ICA or simply reject

Artifact Removal: ICA

49

Isolates components based on independence

– Raw data from 306 channels transformed into component signals

– Visually inspect 306 components (or correlate with EOG/ECG channels)

– Compare with raw data

– Remove artifactual components

30 seconds of data

Blink?

Blink?

Cardiac?

Good?

Com

ponents

R

aw

Data

Chann

els


Filtered 1-40 Hz & SSP of Empty Room


Filt 1-40Hz; SSP of Empty Room; ICA components removed


52

Reference system


Random external artifact:

MaxFilter (SSS)

Signal Space Separation (SSS)

53

MaxFilter, created by Elekta Calculate an INNER sphere

around the head but inside

the sensor helmet

Calculate an OUTER

sphere around the outside

of the sensors

Calculate estimates of

sources as if they were on

the surface of the INNER

and OUTER spheres

Remove those that are

stronger on the OUTER

sphere, more likely to be

noise


Raw continuous data


Maxfiltered data

Temporal extension

56

MaxST (or tSSS)

Look at data in 4 second

chunks

Correlate source signals

across chunks

Any source signals with

high correlations are

assumed to be artifacts

Maxfilter with temporal extension

57

Collecting Data

58

Baseline:

200ms

1000ms :Time-frame

1 Trial

x 200 =

1 sensor’s average

Averaging trials

1 Trial

200 Trials

Averaging trials

averages

Beep!

words

Acquisition

Co-registration

Preprocessing

And Averaging

Localization

WHERE IS THE

BRAIN ACTIVE?


ELECTRIC! MAGNETIC!

From surface to localized

Maxwell’s Equations The Right Hand Rule

The problem

Forward

Problem

If we know this… We can calculate this!

Solvable!

? Inverse

Problem

The Problem?

We only know this….

Always an

estimate!

The estimation

The equivalent of triangulation

There are many kinds of localization strategies, here are two:

Dipole Fitting

Distributed Technique

Head models

Spherical

computationally

straightforward

unrealistic

Boundary

Element

computationally

difficult

realistic

1) Pick subset of sensors w/ peak 2) Pick Time Point; Observe Mag Field

4) Map to MRI 3) Measures of Quality

Goodness of Fit

% of activity explained by

forward solution based on

single dipole

Confidence Volume

volume within which you can be

95% confident that the dipole

exists

Equivalent

Current

Dipole

Technique

22ms 52ms 83ms

7 sensors

42 sensors

92 sensors

99.7% 99.2% 98.2%

84.6% 97.6% 85.8%

84.6% 97.6% 85.8%

Median Nerve

Dipole Fitting

Results

circle = size of

confidence volume

Equivalent Current Dipole

Best for a brain

response that does not

elicit a big network, or

for stimulus response

early in the time

course.

Heavily user

dependant

20 ms 44 ms 57 ms 88 ms 120 ms

Time course of SEF

20 ms 44 ms 57 ms 88 ms 120 ms

Time course of SEF

R L

L

Minimum Norm Estimate (MNE)

Realistic Head Model

Cortical surface

segmented into 5124

possible source

locations

1 location every 6.2 mm

(each 39 mm2)

* Can use spherical model

Estimate Current

Estimate a current

pattern from all sensors

to head model whose

forward solution

explains measured data

“Regularize”

Further constrain

estimate by suppressing

weak boundaries of a

source

Focuses

sources of

activity to

smaller

regions

Distributed

Minimum Norm

Estimate Dipole Fitting

Electrical Median Nerve Stimulation, 1Hz

SI: Post-Central

SII: Insula

Distributed Techniques

Great for brain responses

that elicit network activity,

like language

Much less user dependant

More automatable

Many Distributed Techniques

76

Distributed Methods

77

Many Techniques

MCE_2.mpeg

Beamformer

Technique

Theta

Alpha

Beta

Gamma

Frequency

analysis

multimodal techniques

Structural

fiber tracking

fmri

constrained

fMRI MEG

unconstrained

constrained

Pre-surgical Mapping

Usefulness for surgical planning

Brain Mapping allows avoidance of critical areas

craniotomy

Usefulness for surgical planning

Brain Mapping allows avoidance of critical areas

Gamma Knife before after

Fetal MEG

30 weeks 36 weeks newborn

Latency of the peak

gets SHORTER

QUESTIONS?

Documents

MNTP Magnetoencephalography (MEG) · What does MEG measure? Post-Synaptic Intracellular Current Summed over 10000 or more neurons Organized Tangentially to the Scalp . Action potentials: