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EEG/MEG source reconstruction in SPM5

EEG/MEG source reconstruction in SPM5. Jérémie Mattout / Christophe Phillips / Karl Friston. With thanks to John Ashburner, Guillaume Flandin, Rik Henson, Stefan Kiebel. Outline. Introduction - EEG/MEG inverse problem - 3D reconstruction in SPM5 I - Source model II - Data registration

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EEG/MEG source reconstruction in SPM5

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  1. EEG/MEG source reconstructionin 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. Introduction - EEG/MEG inverse problem • Jacques Hadamard (1865-1963) • Existence • Unicity • 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 Forward problem (well-posed) Y = K(J) + E Inverse problem (ill-posed) DataY Current densityJ Bayesian framework • incorporate multiple constraints/prior information • estimate the optimal contribution of those priors • evaluate the relevance of the priors/model Parametric empirical Bayes Bayesian model comparison

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

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

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

  9. Introduction - 3D Reconstruction in SPM5 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 = spm_eeg_ldata; Data structure D.inv{1} = method: 'Imaging' mesh: [1x1 struct] datareg: [1x1 struct] forward: [1x1 struct] inverse: [1x1 struct] comment: {'MN + Smoothness'} date: [2x11 char]

  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. I - Source Model (Meshes) Compute transformation T Individual MRI • wmeshTemplate_3004d.mat • - wmeshTemplate_4004d.mat • - wmeshTemplate_5004d.mat • - wmeshTemplate_7004d.mat Templates Apply inverse transformation T-1 Individual mesh input functions output • Individual MRI • Template mesh • spatial normalization into MNI template1 • inverted transformation applied to the template mesh2 • inner-skull and scalp binary masks • cortical mesh • inner-skull mesh • scalp mesh 1Unified segmentation, J. Ashburner and K.J. Friston, NeuroImage, 2005. 2Canonical source reconstruction for EEG & MEG, J. Mattout and K.J. Friston, in preparation.

  12. I - Source Model (Meshes) 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]

  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. fiducials Rigid transformation (R,t) II - Data Registration fiducials • Landmarks (MEG/EEG) • ICP Surface matching (EEG) EEG/MEG sensor space MRI space input output • sensor locations • fiducial locations • (in sensor & MRI space) • structural MRI • (scalp mesh) functions • registered data • transformation matrix • registration of the EEG/MEG data into MRI space3 3A method for registration of 3d-shapes, P.J. Besl and N.D. McKay, IEEE Trans. Pat. Anal. And Mach. Intel., 1992.

  15. II - Data Registration 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]

  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. III - Head model & Forward computation p Compute for each dipole + n Forward operator MRI space Head model functions input output • single sphere • three spheres • overlapping spheres • realistic spheres • sensor locations • cortical mesh • scalp mesh • forward operator BrainSTorm http://neuroimage.usc.edi/brainstorm

  18. III - Head model & Forward computation 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]

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

  21. IV - Parametric Empirical Bayes (Inverse) Bayesian inference on model parameters + + Model M Maximizing the log-evidence data fit priors Expectation-Maximization (EM) E-step: maximizing F wrt J MAP estimate M-step: maximizing of F wrt ReML estimate ? Log(Bayes factor) = F1-F21 Bayesian Model Comparison Inference 4Comparing dynamic causal models, W.D. Penny, K.E. Stephan, A. Mechelli, K. Friston, NeuroImage, 2004.

  22. IV - Parametric Empirical Bayes (Inverse) Evoked and induced activity Events s  t t Synchronized oscillations in time, but not in phase with the stimulation FT Average Evoked resp. Induced resp. - =

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

  24. Energy changes (Faces - Scrambled, p<0.01) Right temporal evoked signal 45 faces scrambled 40 3 35 2 30 frequency (Hz) 25 1 20 0 15 -1 10 400 100 200 300 -2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 time (ms) time (s) M170 -3 Time-frequency subspace 0 200 400 time (ms) IV - Parametric Empirical Bayes (Inverse) Example MEG experiment of 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.

  25. IV - Parametric Empirical Bayes (Inverse) Example

  26. IV - Parametric Empirical Bayes (Inverse) Example

  27. IV - Parametric Empirical Bayes (Inverse) input functions output • preprocessed data • - forward operator • mesh • constraints • - compute the MAP estimate of J1 • compute the ReML estimate of 1 • model evidence2,4 • source dynamic1,2 • power3 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).

  28. IV - Parametric Empirical Bayes (Inverse) 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: [0.5000 0.5000 1 0 0] woi: [150 190] priors: [1x1 struct] dim: 4004 resfile: 'fmbe_emer01_TCS_remlmat_150_190ms_evoked_11H3.mat' LogEv: 9.8269e+003

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