From Neuronal activity to EEG/MEG signals

From Neuronal activity to EEG/MEG signals

Jérémie MattoutU821 INSERMBrain Dynamics and CognitionLyon, FranceSPM Course – May 2010 – London

A short tale about the origins of Electroencephalographyand Magnetoencephalography

A brief history

The EEG & MEG instrumentation

What do we measure with EEG & MEG ?

Of the importance of modelling forward

Outline

Carl Friedrich Gauss1777 - 1855

Lionel Messi

A brief history

A brief history

From the electrical nature of brain signals …

… to the first EEG recordings

Richard Caton1842 - 1926

Hans Berger1873 - 1941

1875: R.C. measured currents inbetween the cortical surface and the skull, in dogs and monkeys

1924: H.B. first EEG in humans, description of alpha and beta waves

Alpha actiity ~ 200 μV

A brief history

About 50 years later …

DavidCohen

1962: Josephson effect

1968: first (noisy) measure of a magnetic brain signal [Cohen, Science 68]

1970: James Zimmerman invents the ‘Superconducting quantum interference device’ (SQUID)

1972: first (1 sensor) MEG recording based on SQUID [Cohen, Science 1972]

1973: Josephson wins the Nobel Prize in Physics

Brian-DavidJosephson

A brief history

About 40 years later… today!

Bob - 2010


EEG

- The EEG cap sticks to the subject’s head- EEG measures are not much sensitive to environmental noise (except for 50Hz)

- EEG data depend upon a choice of reference- EEG data might be corrupted by artefacts (blinks, saccades, heart beat, sweat,muscle activity, breathing, swallowing, yawning, sweat, 50Hz, )

Claire & JB (french scientists)


Sensors(Pick up coil)

SQUIDs

MEG - 269 °C

There are different types of sensors

Magnetometers: measure the magnetic flux through a single coil

Gradiometers: measure the difference in magnetic flux between two points in space (axial/planar ; order 1, 2 or 3)


MEG essentially measures… noise!


Heart beat

Eye movements

Brain activity

Evoked brain activity

Biomagnetic fields

Earth magnetic field

Environmental noise

Urban noise

Car (50m)

Screw driver (5m)

Electronic circuit(2m)

1 femto-Tesla (fT) = 10-15 TAlpha waves ~ 103 fT

What do we measure with EEG & MEG ?from a single neuron to a neuronal assembly

From a single neuron to a neuronal assembly/column

- A single active neuron is not sufficient. ~100.000 simultaneously active neurons are needed to generate scalp measures.

- Pyramidal cells are the main direct neuronal sources of EEG & MEG signals.

- Synaptic currents but not action potentials generate EEG/MEG signals


The dipolar model

- A current source in the brain corresponds to a neuronal column and is modelled by a current dipole

- A current dipole is fully defined by 6 parameters: 3 for its position & 3 for its moment (includes orientation and amplitude)

- A dipolar moment Q = I x d ~ 10 to 100 nAm


source

sink

What do we measure with EEG & MEG ?from a neuronal assembly to sensors

From a single source to the sensor: the quasi-static assumption


James Clerk Maxwell(1831 - 1879)

E: electric fieldB: magnetic field

From a single source to the sensor: EEG


primary/sourcecurrents

secondary/conductioncurrents

Electric field lines

JcJs



Georg Simon Ohm1789 - 1841

Ohm’s law

Jc = E = - grad(V) tissue conductivities

Margaret Thatcher

QueenElisabeth II

Conservation law

.Js + . Jc = 0 => . Js = .[grad(V)]



- EEG is sensitive to both radial and tangential sources- EEG is sensitive to conductivities which explains the low resolution scalp topographies- To model EEG data, it matters to account for real tissue conductivity and geometry

Simulatedexample

Early auditoryevoked repsonse

>

From a single source to the sensor: MEG


Right hand rule

Barak Obama

Tangential dipoleRadial dipole



source locationsensor location

source orientation & sizesource amplitude

- The magnetic field amplitude decreases with the square of the distance between the source and the sensor => MEG is less sensitive to deep sources

- Pure radial sources will remain silent

Félix Savart (1791-1841) Jean-Baptiste Biot (1791-1841)


From a single source to the sensor: MEG Biot & Savart’s law

MEG

EEG



Summary

spati

al re

solu

tion

(mm

)

invasivity

weak strong

5

10

15

20

temporal resolution (ms)1 10 102 103 104 105

sEEG

MEG

EEG

fMRI

MRI(a,d)

PET

SPECT

OIECoG



« Will it ever happen that mathematicians will know enough about the physiology of the brain, and neurophysiologists enough of mathematical discovery, for efficient cooperation to be possible ? »

Jacques Hadamard (1865-1963)


inference

MEG

EEG

From EEG/MEG data to neuronal sources ?

Forward model

Generative models

MEG

EEGDipolar sources

Head tissues(conductivity & geometry)


Gain vectors & Lead-field matrix

Y = g() Simulating data

sourceparameters

forwardmodel

scalpdata

-1 layer vs. 3 layers- spheres vs. realistic surfaces or volumes- analytical vs. numerical solutions

1 source 1 gain vector

All sources 1 gain operator orlead-field matrix


Inverse problem

Y = g(1) + g(2) + Modelling empirical data

Unknownsource

Parameters ?

forwardModel

(lead-fields)

scalpdata


Jean Daunizeau

Karl Friston

James Kilner

Stefan Kiebel

Guillaume Flandin

Vladimir Litvak

Christophe Phillips

Rik Henson

Marta GarridoWill Penny

Rosalyn Moran

Gareth Barnes JM Schoffelen

From Neuronal activity to EEG/MEG signals

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