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From Neuronal activity to EEG/MEG signals Jérémie Mattout U821 INSERM Brain Dynamics and Cognition Lyon, France SPM Course – May 2010 – London A short.

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Presentation on theme: "From Neuronal activity to EEG/MEG signals Jérémie Mattout U821 INSERM Brain Dynamics and Cognition Lyon, France SPM Course – May 2010 – London A short."— Presentation transcript:

1 From Neuronal activity to EEG/MEG signals Jérémie Mattout U821 INSERM Brain Dynamics and Cognition Lyon, France SPM Course – May 2010 – London A short tale about the origins of Electroencephalography and Magnetoencephalography

2 A brief history The EEG & MEG instrumentation What do we measure with EEG & MEG ? Of the importance of modelling forward Outline

3

4 Carl Friedrich Gauss Lionel Messi

5 A brief history

6 From the electrical nature of brain signals … … to the first EEG recordings Richard Caton Hans Berger : 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

7 A brief history About 50 years later … David Cohen 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-David Josephson

8 A brief history About 40 years later… today! Bob

9 The EEG & MEG instrumentation

10 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)

11 The EEG & MEG instrumentation Sensors (Pick up coil) SQUIDs MEG °C

12 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) The EEG & MEG instrumentation

13 MEG essentially measures… noise! The EEG & MEG instrumentation 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) = T Alpha waves ~ 10 3 fT

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

15 From a single neuron to a neuronal assembly/column - A single active neuron is not sufficient. ~ 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 What do we measure with EEG & MEG ?

16 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 What do we measure with EEG & MEG ? source sink

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

18 From a single source to the sensor: the quasi-static assumption What do we measure with EEG & MEG ? James Clerk Maxwell ( ) E: electric field B: magnetic field

19 From a single source to the sensor: EEG What do we measure with EEG & MEG ? primary/source currents secondary/conduction currents Electric field lines JcJs

20 From a single source to the sensor: EEG What do we measure with EEG & MEG ? Georg Simon Ohm Ohm’s law Jc =  E = -  grad(V)  tissue conductivities Margaret Thatcher Queen Elisabeth II Conservation law .Js + . Jc = 0 => . Js = .[  grad(V)]

21 From a single source to the sensor: EEG What do we measure with EEG & MEG ? - 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 Simulated example Early auditory evoked repsonse

22 > From a single source to the sensor: MEG What do we measure with EEG & MEG ? Right hand rule Barak Obama

23 Tangential dipole Radial dipole What do we measure with EEG & MEG ? From a single source to the sensor: MEG

24 source location sensor location source orientation & size source 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 ( ) Jean-Baptiste Biot ( ) What do we measure with EEG & MEG ? From a single source to the sensor: MEG Biot & Savart’s law

25 MEG EEG What do we measure with EEG & MEG ? From a single source to the sensor: MEG

26 Summary spatial resolution (mm) invasivity weakstrong temporal resolution (ms) sEEG MEG EEG fMRI MRI(a,d) PET SPECT OI ECoG What do we measure with EEG & MEG ?

27 Of the importance of modelling forward « 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 ( )

28 Of the importance of modelling forward inference MEG EEG From EEG/MEG data to neuronal sources ?

29 Forward model Generative models MEG EEG Dipolar sources Head tissues (conductivity & geometry) Of the importance of modelling forward

30 Gain vectors & Lead-field matrix Y = g(  ) Simulating data source parameters forward model scalp data -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 or lead-field matrix Of the importance of modelling forward

31 Inverse problem Y = g(  1 ) + g(  2 ) +  Modelling empirical data Unknown source Parameters ? forward Model (lead-fields) scalp data Of the importance of modelling forward

32 Jean Daunizeau Karl Friston James Kilner Stefan Kiebel Guillaume Flandin Vladimir Litvak Christophe Phillips Rik Henson Marta Garrido Will Penny Rosalyn Moran Gareth Barnes JM Schoffelen


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