Presentation is loading. Please wait.

Presentation is loading. Please wait.

EEG/MEG source reconstruction in SPM5 Jérémie Mattout / Christophe Phillips / Karl Friston With thanks to John Ashburner, Guillaume Flandin, Rik Henson,

Similar presentations


Presentation on theme: "EEG/MEG source reconstruction in SPM5 Jérémie Mattout / Christophe Phillips / Karl Friston With thanks to John Ashburner, Guillaume Flandin, Rik Henson,"— Presentation transcript:

1 EEG/MEG source reconstruction in SPM5 Jérémie Mattout / Christophe Phillips / Karl Friston With thanks to John Ashburner, Guillaume Flandin, Rik Henson, Stefan Kiebel

2 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

3 Introduction - EEG/MEG inverse problem

4 Jacques Hadamard ( ) 1.Existence 2.Unicity 3.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?

5 Introduction - EEG/MEG inverse problem Data Y Current density J 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 Bayesian framework Parametric empirical Bayes Bayesian model comparison

6 Preprocessing Projection SPM5-engine EEG/MEG Raw data Single Trials - epoching - artefacts - filtering - averagin Single Trials - epoching - artefacts - filtering - averagin 2D - scalp SPM{t} SPM{F} SPM{t} SPM{F} Mass univariate analysis Mass univariate analysis 3D - brain DCM spm_eeg_inv_*.m Introduction - 3D Reconstruction in SPM5

7 Sources Imaging Equivalent Current Dipoles (ECD) 3D Projection Introduction - 3D Reconstruction in SPM5 MEG data EEG data

8 (1) Source model (3) Forward model (4) Inverse method (2) Registration ECD Imaging Data Anatomy Introduction - 3D Reconstruction in SPM5

9 D = data: [151x2188x5 spm_file_array] channels: [1x1 struct] scale: [1x1 struct] filter: [1x1 struct] events: [1x1 struct] reref: [] descrip: [] datatype: 'int16' fname: 'fmbe_emer01_TCS.mat' fnamedat: 'fmbe_emer01.dat' Nchannels: 151 Nevents: 5 Nsamples: 2188 Radc: 625 path: [1x76 char] inv: {1x7 cell} modality: 'MEG' D.inv{1} = method: 'Imaging' mesh: [1x1 struct] datareg: [1x1 struct] forward: [1x1 struct] inverse: [1x1 struct] comment: {'MN + Smoothness'} date: [2x11 char] Introduction - 3D Reconstruction in SPM5 Data structure D = spm_eeg_ldata;

10 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

11 Compute transformation T Apply inverse transformation T -1 - Individual MRI - Template mesh input - spatial normalization into MNI template 1 - inverted transformation applied to the template mesh 2 - inner-skull and scalp binary masks - cortical mesh - inner-skull mesh - scalp mesh functions output 1 Unified segmentation, J. Ashburner and K.J. Friston, NeuroImage, Canonical source reconstruction for EEG & MEG, J. Mattout and K.J. Friston, in preparation. - wmeshTemplate_3004d.mat - wmeshTemplate_4004d.mat - wmeshTemplate_5004d.mat - wmeshTemplate_7004d.mat Individual MRI Individual mesh Templates I - Source Model (Meshes)

12 D.inv{1} = method: 'Imaging' mesh: [1x1 struct] datareg: [1x1 struct] forward: [1x1 struct] inverse: [1x1 struct] comment: {'MN + Smoothness'} date: [2x11 char] D.inv{1}.mesh = sMRI: [1x87 char] nobias: [1x86 char] def: [1x94 char] invdef: [1x98 char] msk_iskull: [1x92 char] msk_scalp: [1x91 char] msk_flags: '' tess_ctx: [1x95 char] Ctx_Nv: 4004 Ctx_Nf: 8000 tess_iskull: [1x108 char] Iskull_Nv: 2002 Iskull_Nf: 4000 tess_scalp: [1x106 char] Scalp_Nv: 2002 Scalp_Nf: 4000 CtxGeoDist: [1x101 char] I - Source Model (Meshes)

13 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

14 Rigid transformation (R,t) fiducials - sensor locations - fiducial locations (in sensor & MRI space) - structural MRI - (scalp mesh) input - registration of the EEG/MEG data into MRI space 3 - registered data - transformation matrix functions output EEG/MEG sensor space MRI space 3 A method for registration of 3d-shapes, P.J. Besl and N.D. McKay, IEEE Trans. Pat. Anal. And Mach. Intel., Landmarks (MEG/EEG) - ICP Surface matching (EEG) II - Data Registration

15 D.inv{1} = method: 'Imaging' mesh: [1x1 struct] datareg: [1x1 struct] forward: [1x1 struct] inverse: [1x1 struct] comment: {'MN + Smoothness'} date: [2x11 char] D.inv{1}.datareg = sens: [1x98 char] fid: [1x94 char] fidmri: [1x94 char] hsp: '' scalpvert: '' sens_coreg: [1x104 char] fid_coreg: [1x100 char] hsp_coreg: '' eeg2mri: [1x87 char] II - Data Registration

16 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

17 Compute for each dipole Compute for each dipole + p n - sensor locations - cortical mesh - scalp mesh input - single sphere - three spheres - overlapping spheres - realistic spheres - forward operator functions output BrainSTorm MRI space Forward operator Head model III - Head model & Forward computation

18 D.inv{1} = method: 'Imaging' mesh: [1x1 struct] datareg: [1x1 struct] forward: [1x1 struct] inverse: [1x1 struct] comment: {'MN + Smoothness'} date: [2x11 char] D.inv{1}.forward = bst_options: [1x1 struct] bst_channel: [1x100 char] bst_tess: [1x97 char] gainmat: [1x103 char] pcagain: [1x107 char] III - Head model & Forward computation

19 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

20 2-level hierarchical model Linear parameterization of the variances Gaussian variables with unknown variance Gaussian variables with unknown variance Single trial Sensors Sources Q: variance components : hyperparameters IV - Parametric Empirical Bayes (Inverse)

21 Bayesian inference on model parameters Model M ++ E-step: maximizing F wrt J M-step: maximizing of F wrt Maximizing the log-evidence data fitpriors Expectation-Maximization (EM) Inference Bayesian Model Comparison ? MAP estimate ReML estimate IV - Parametric Empirical Bayes (Inverse) Log(Bayes factor) = F1-F2 1 4 Comparing dynamic causal models, W.D. Penny, K.E. Stephan, A. Mechelli, K. Friston, NeuroImage, 2004.

22 Evoked 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)

23 Multiple trials data & constraints evoked energyinduced energy IV - Parametric Empirical Bayes (Inverse)

24 Example Energy changes (Faces - Scrambled, p<0.01) time (s) frequency (Hz) time (ms) Right temporal evoked signal faces scrambled M170 Time-frequency subspace 0200 time (ms) 400 MEG experiment of Face perception 4 4 Electrophysiology 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, IV - Parametric Empirical Bayes (Inverse)

25 Example IV - Parametric Empirical Bayes (Inverse)

26 Example IV - Parametric Empirical Bayes (Inverse)

27 - preprocessed data - forward operator - mesh - constraints input - compute the MAP estimate of J 1 - compute the ReML estimate of 1 - model evidence 2,4 - source dynamic 1,2 - power 3 functions output 1 An empirical Bayesian solution to the source reconstruction problem in EEG, C. Phillips, J. Mattout, M.D. Rugg, P. Maquet and K.J. Friston, NeuroImage, MEG source localization under multiple constraints: an extended Bayesian framework, J. Mattout, C. Phillips, M.D. Rugg and K.J. Friston, NeuroImage (in press). 3 Bayesian estimation of evoked and induced responses, K.J. Friston, R.N. Henson, C. Phillips and J. Mattout, Hum. Brain Mapp. (in press). 4 Variational free energy and the Laplace approximation, K.J. Friston, J. Mattout, N. Trujillo-Barreto, J. Ashburner and W. Penny (in preparation). IV - Parametric Empirical Bayes (Inverse)

28 D.inv{1} = method: 'Imaging' mesh: [1x1 struct] datareg: [1x1 struct] forward: [1x1 struct] inverse: [1x1 struct] comment: {'MN + Smoothness'} date: [2x11 char] D.inv{1}.inverse = activity: 'evoked' contrast: [ ] woi: [ ] priors: [1x1 struct] dim: 4004 resfile: 'fmbe_emer01_TCS_remlmat_150_190ms_evoked_11H3.mat' LogEv: e+003 IV - Parametric Empirical Bayes (Inverse)


Download ppt "EEG/MEG source reconstruction in SPM5 Jérémie Mattout / Christophe Phillips / Karl Friston With thanks to John Ashburner, Guillaume Flandin, Rik Henson,"

Similar presentations


Ads by Google