EEG/MEG source EEG/MEG source reconstruction reconstruction in SPM5 in SPM5 Jérémie Mattout / Christophe Phillips / Jérémie Mattout / Christophe Phillips / Karl Friston Karl Friston With thanks to With thanks to John Ashburner, Guillaume Flandin, Rik Henson, John Ashburner, Guillaume Flandin, Rik Henson, Stefan Kiebel Stefan Kiebel
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EEG/MEG source reconstruction in SPM5 Jérémie Mattout / Christophe Phillips / Karl Friston With thanks to John Ashburner, Guillaume Flandin, Rik Henson,
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Jérémie Mattout / Christophe Phillips / Karl FristonJérémie Mattout / Christophe Phillips / Karl Friston
With thanks toWith thanks to
John Ashburner, Guillaume Flandin, Rik Henson, Stefan KiebelJohn Ashburner, Guillaume Flandin, Rik Henson, Stefan Kiebel
Outline
Introduction- EEG/MEG inverse problem- 3D reconstruction in SPM5
I - Source model
II - Data registration
III - Head model and forward computation
IV - Inverse estimation
Demo
Introduction - EEG/MEG inverse problem
Introduction - EEG/MEG inverse problem
Jacques Hadamard (1865-1963)
1. Existence2. Unicity3. Stability
“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?”
Introduction - EEG/MEG inverse problem
Data Y Current density J
Inverse problem (ill-posed)Inverse problem (ill-posed)
Forward problem (well-posed)Y = K(J) + E
Forward problem (well-posed)Y = K(J) + E
• incorporate multiple constraints/prior information• estimate the optimal contribution of those priors• evaluate the relevance of the priors/model
- inverted transformation applied to the template mesh2
- inner-skull and scalp binary masks
- cortical mesh- inner-skull mesh- scalp mesh
functions output
1Unified segmentation, J. Ashburner and K.J. Friston, NeuroImage, 2005.2Canonical source reconstruction for EEG & MEG, J. Mattout and K.J. Friston, in preparation.
Bayesian Model ComparisonBayesian Model Comparison 21 FF ?
MAP estimate
ReML estimate
),,(],,[ NQYYREMLFJ T
IV - Parametric Empirical Bayes (Inverse)
J
Log(Bayes factor) = F1-F21
4Comparing dynamic causal models, W.D. Penny, K.E. Stephan, A. Mechelli, K. Friston, NeuroImage, 2004.
Evoked and induced activityEvoked and induced activity
Synchronized oscillations in time,but not in phase with the stimulation
Events
Average
FT
- =
t
Evoked resp. Induced resp.
t
s
IV - Parametric Empirical Bayes (Inverse)
Multiple trialsMultiple trialsdata & constraints
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evoked energy induced energy
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IV - Parametric Empirical Bayes (Inverse)
ExampleExample Energy changes (Faces - Scrambled, p<0.01)
0.1 0.2 0.4 0.6 0.8
time (s)
10
20
30
40
35
45
15
25
0.70.50.30-0.1
0
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2
3
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-1frequ
ency
(Hz)
100 200 300 400
time (ms)
Right temporal evoked signal
facesscrambled
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Time-frequency subspace
0 200time (ms)
400
MEG experimentof Face perception4
4Electrophysiology and haemodynamic correlates of face perception, recognition and priming, R.N. Henson, Y. Goshen-Gottstein, T. Ganel, L.J. Otten, A. Quayle, M.D. Rugg, Cereb. Cortex, 2003.
1An empirical Bayesian solution to the source reconstruction problem in EEG, C. Phillips, J. Mattout, M.D. Rugg, P. Maquet and K.J. Friston, NeuroImage, 2005.2MEG source localization under multiple constraints: an extended Bayesian framework, J. Mattout, C. Phillips, M.D. Rugg and K.J. Friston, NeuroImage (in press).3Bayesian estimation of evoked and induced responses, K.J. Friston, R.N. Henson, C. Phillips and J. Mattout, Hum. Brain Mapp. (in press).4Variational free energy and the Laplace approximation, K.J. Friston, J. Mattout, N. Trujillo-Barreto, J. Ashburner and W. Penny (in preparation).