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From Neuronal activity to EEG/MEG signals

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

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From Neuronal activity to EEG/MEG signals

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

  2. Outline A briefhistory The EEG & MEG instrumentation What do wemeasurewith EEG & MEG ? Of the importance of modellingforward

  3. Carl Friedrich Gauss 1777 - 1855 Lionel Messi

  4. A briefhistory

  5. A brief history From the electrical nature of brain signals … 1875: R.C. measured currents inbetween the cortical surface and the skull, in dogs and monkeys Richard Caton 1842 - 1926 … to the first EEG recordings 1924: H.B. first EEG in humans, description of alpha and beta waves Hans Berger 1873 - 1941 Alpha actiity ~ 200 μV

  6. A brief history About 50 years later … Brian-David Josephson 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 David Cohen

  7. A brief history About 40 years later… today! Bob - 2010

  8. The EEG & MEG instrumentation

  9. The EEG & MEG instrumentation EEG Claire & JB (french scientists) • 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, )

  10. The EEG & MEG instrumentation MEG - 269 °C SQUIDs Sensors (Pick up coil)

  11. The EEG & MEG instrumentation 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)

  12. The EEG & MEG instrumentation MEG essentially measures… noise! 1 femto-Tesla (fT) = 10-15 T Alpha waves ~ 103 fT Earth magnetic field Urban noise Car (50m) Screw driver (5m) Heart beat Eye movements Electronic circuit (2m) Brain activity Evoked brain activity Environmental noise Biomagnetic fields

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

  14. What do we measure with EEG & MEG ? 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

  15. What do we measure with EEG & MEG ? The dipolar model source sink • 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

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

  17. What do we measure with EEG & MEG ? From a single source to the sensor: the quasi-static assumption E: electric field B: magnetic field James Clerk Maxwell (1831 - 1879)

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

  19. What do we measure with EEG & MEG ? From a single source to the sensor: EEG Ohm’s law Jc = sE = - s grad(V) s : tissue conductivities Georg Simon Ohm 1789 - 1841 Conservation law .Js + . Jc = 0 => . Js = .[s grad(V)] Queen Elisabeth II Margaret Thatcher

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

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

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

  23. What do we measure with EEG & MEG ? Biot & Savart’s law From a single source to the sensor: MEG source orientation & size source amplitude Jean-Baptiste Biot (1791-1841) Félix Savart (1791-1841) source location sensor location • 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

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

  25. What do we measure with EEG & MEG ? invasivity weak strong EEG 20 spatial resolution (mm) MEG SPECT 15 OI PET 10 fMRI sEEG 5 MRI(a,d) 1 10 102 103 104 105 temporal resolution (ms) Summary ECoG

  26. 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 (1865-1963)

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

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

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

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

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

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