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EEG/MEG Source Localisation

EEG/MEG Source Localisation. SPM Short Course – Wellcome Trust Centre for Neuroimaging – May 2008. ?. Jérémie Mattout, Christophe Phillips. Jean Daunizeau Guillaume Flandin Karl Friston Rik Henson Stefan Kiebel Vladimir Litvak. EEG/MEG Source localisation. Outline. Introduction

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EEG/MEG Source Localisation

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  1. EEG/MEG Source Localisation SPM Short Course – Wellcome Trust Centre for Neuroimaging – May 2008 ? Jérémie Mattout, Christophe Phillips Jean Daunizeau Guillaume Flandin Karl Friston Rik Henson Stefan Kiebel Vladimir Litvak

  2. EEG/MEG Source localisation Outline Introduction Forward model Inverse problem Bayesian inference applied to the EEG/MEG inverse problem Conclusion

  3. EEG/MEG Source localisation Outline Introduction Forward model Inverse problem Bayesian inference applied to the EEG/MEG inverse problem Conclusion

  4. MRI MEG EEG invasivity weak strong OI 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) EEG/MEG Source localisation Introduction: EEG/MEG as Neuroimaging techniques

  5. Data importation/convertion • Import most common MEG/EEG data formats into one single data format • Include “associated data”, e.g. electrode location and sensor setup EEG/MEG Source localisation MEEG functionalities in SPM8 Data Preperation • New MEEG data • format based on • “object-oriented” • coding • More stable interfacing and user-friendly  and a bit harder for developers 

  6. EEG/MEG Source localisation MEEG functionalities in SPM8 Data Preperation “Usual“ preprocessing • Filtering • Re-referencing • Epoching • Artefact and bad channel rejection • Averaging • Displaying • …

  7. Source reconstruction EEG/MEG Source localisation MEEG functionalities in SPM8 Data Preprocessing Scalp Data Analysis Statistical Parametric Mapping Dynamic Causal Modelling

  8. 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 EEG/MEG Source localisation MEEG “usual” results 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.

  9. EEG/MEG Source localisation Change speaker…

  10. Forward model Inverse problem EEG/MEG source reconstruction process EEG/MEG Source localisation Introduction: overview

  11. EEG/MEG Source localisation Outline Introduction Forward model Inverse problem Bayesian inference applied to the EEG/MEG inverse problem Conclusion

  12. EEG/MEG Source localisation Forward model: source space source biophysical model: current dipole Imaging or Distributed Equivalent Current Dipoles (ECD) EEG/MEG source models • many dipoles with • fixed location and orientation • few dipoles with • free location and orientation

  13. Forward model EEG/MEG Source localisation Forward model: formulation data forward operator dipole parameters noise

  14. EEG/MEG Source localisation Forward model: imaging/distributed model data gain matrix dipole amplitudes noise

  15. EEG/MEG Source localisation Outline Introduction Forward model Inverse problem Bayesian inference applied to the EEG/MEG inverse problem Conclusion

  16. Inverse problem EEG/MEG Source localisation Inverse problem: an ill-posed problem « 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) • Existence • Unicity • Stability

  17. Inverse problem EEG/MEG Source localisation Inverse problem: an ill-posed problem « 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) • Existence • Unicity • Stability

  18. Inverse problem EEG/MEG Source localisation Inverse problem: an ill-posed problem « 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) • Existence • Unicity • Stability Introduction of prior knowledge (regularization) is needed

  19. Spatial and temporal priors Adequacy with other modalities Data fit EEG/MEG Source localisation Inverse problem: regularization data fit prior (regularization term) W = I : minimum norm W =Δ : maximum smoothness (LORETA)

  20. EEG/MEG Source localisation Outline Introduction Forward model Inverse problem Bayesian inference applied to the EEG/MEG inverse problem Conclusion

  21. Forward model Inverse problem posterior EEG/MEG Source localisation Bayesian inference: probabilistic formulation likelihood posterior likelihood prior evidence

  22. source (2nd) level sensor (1st) level EEG/MEG Source localisation Bayesian inference: hierarchical linear model prior likelihood Q : (known) variance components (λ,μ) : (unknown) hyperparameters

  23. EEG/MEG Source localisation Bayesian inference: variance components # dipoles … # dipoles Minimum Norm (IID) Maximum Smoothness (LORETA) Multiple Sparse Priors (MSP)

  24. λ1 λk J μ1 Y μq EEG/MEG Source localisation Bayesian inference: graphical representation prior likelihood

  25. EEG/MEG Source localisation Bayesian inference: iterative estimation scheme Expectation-Maximization (EM) algorithm E-step M-step

  26. Fi model Mi 3 1 2 EEG/MEG Source localisation Bayesian inference: model comparison At convergence

  27. EEG/MEG Source localisation Outline Introduction Forward model Inverse problem Bayesian inference applied to the EEG/MEG inverse problem Conclusion

  28. Individual reconstructions in MRI template space L R Group results p < 0.01 uncorrected R L EEG/MEG Source localisation Conclusion: At the end of the day...

  29. Forward model Inverse problem EEG/MEG Source localisation Conclusion: Summary • EEG/MEG source reconstruction: 1. forward model 2. inverse problem (ill-posed) • Prior information is mandatory • Bayesian inference is used to: 1. incorpoate such prior information… 2. … and estimating their weight w.r.t the data 3. provide a quantitative feedback on model adequacy

  30. EEG/MEG Source localisation Change speaker… Again !

  31. EEG/MEG Source localisation Equivalent Current Dipole (ECD) solution source biophysical model: current dipole few dipoles with free location and orientation many dipoles with fixed location and orientation Imaging or Distributed Equivalent Current Dipoles (ECD) EEG/MEG source models

  32. Forward model EEG/MEG Source localisation ECD approach: principle data forward operator dipole parameters noise buta priori fixed number of sources considered  iterative fitting of the 6 parameters of each dipole

  33. Dipole locations s and dipole moments w generated data using EEG/MEG Source localisation ECD solution: variational Bayes (VB) approach εis white observation noise with precision γy. The locations s and moments w are drawn from normal distributions with precisions γs and γw. These are drawn from a prior gamma distribution.

  34. EEG/MEG Source localisation ECD solution: “classical” vs. VB approaches

  35. EEG/MEG Source localisation ECD solution: when and how to apply VB-ECD? • can be applied to single time-slice data or average over time (MEG and EEG) • useful for comparing several few-dipole solutions for selected time points (N100, N170, etc.) • although not dynamic, can be used for building up intuition about underlying generators, or using as a motivation for DCM source models • implemented in Matlab and (very soon) available in SPM8

  36. EEG/MEG Source localisation

  37. EEG/MEG Source localisation Bayesian inference: multiple sparse priors • Log-normal hyperpriors • Enforces the non-negativity of the hyperparameters • Enables Automatic Relevance Determination (ARD)

  38. EEG/MEG Source localisation Forward model: canonical mesh MNI Space Canonical mesh Subjects MRI [Un]-normalising spatial transformation Anatomical warping Cortical mesh

  39. EEG/MEG Source localisation Forward model: coregistration From Sensor to MRI space EEG HeadShape + Surface Matching Rigid Transformation Full setup HeadShape MRI derived meshes MEG

  40. EEG/MEG Source localisation Main references Friston et al. (2008) Multiple sparse priors for the M/EEG inverse problem Kiebel et al. (2008) Variational Bayesian inversion of the equivalent current dipole model in EEG/MEG Mattout et al. (2007) Canonical Source Reconstruction for MEG Daunizeau and Friston (2007) A mesostate-space model for EEG and MEG Henson et al. (2007) Population-level inferences for distributed MEG source localization under multiple constraints: application to face-evoked fields Friston et al. (2007) Variational free energy and the Laplace approximation Mattout et al. (2006) MEG source localization under multiple constraints Friston et al. (2006) Bayesian estimation of evoked and induced responses Phillips et al. (2005) An empirical Bayesian solution to the source reconstruction problem in EEG

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