Practical Aspects of Imaging Source Reconstruction in the Bayesian Framework

1. Practical aspects of… Imaging Source Reconstruction in the Bayesian Framework Jason Taylor MRC Cognition and Brain Sciences Unit (CBU) Cambridge Centre for Ageing and Neuroscience (CamCAN) 19 January 2011 | Brussels | Thanks to Rik Henson & the CBU MEG community

2. Selecting data (evoked/induced) & time-window (epoch/window) Meshes and Forward Models (template? resolution?) Inversion schemes (IID / MSP / other) Group Inversion, Sensor Fusion, fMRI priors Selecting time-window(s) for contrasts Statistcs Choices, choices

3. Neuromag Mags/Grads (No EEG) N=18 480 trials: 50% words, 50% pseudowords Respond: Word/Non-word with button press, hand counterbalanced Test Case: MEG Lexical Decision Data Taylor & Henson, submitted

4. Mags Grads (RMS)

5. The Entire Analysis Pipeline (Taylor & Henson, submitted)

6. SPM allows you to invert either trials (epochs) or averages ->Decide whether interested in evoked or induced (total) ->for induced: covariance is accumulated over trials ->allows selection of frequency window of interest -- note: trial inversion can by memory-intensive if many trials Selecting data to invert

7. (see Christoph’s talk this morning) ->For the present data, used inverse-normalised template cortical mesh (~7000 points) + individually defined inner-skull & scalp mesh BEM Meshes and Forward Models MSP IID Can(ctx) +Ind(skull) Individual (all) Henson et al, 2009, NImage

8. Model comparison approach: Don’t need to decide a priori ->But consider: - Expect focal or distributed sources? (MSP better captures focal; IID more appropriate for distributed?) - Individual accuracy vs. group consistency? (Maximising individual accuracy may come at the expense of consistency over subjects – if localisation/anatomy is variable) - Distributional assumptions of stats? (Sparse solutions tend not to be Gaussian - recall stats talk) Inversion scheme (MSP, IID, …)

9. Model comparison approach also works for Individual vs. Group, and for the addition of priors from e.g. fMRI To compare Separate Sensor vs Fusion inversions, however, the data have changed, so model comparison does not apply Group Inversion, Sensor Fusion, etc. Group: Litvak & Friston, 2008, Nimage Fusion: Henson et al, 2009, NImage

10. Step 1: Individual subject/sensor inversions Taylor & Henson, submitted

11. Step 2: Fusion of Sensor Types Taylor & Henson, submitted

12. fMRI priors Taylor & Henson, submitted

13. MSP >> IID (note difference in scales between two plots) • Group inversion doesn’t affect IID (nothing to optimise); • Group inversion decreases (though n.s.) MSP model evidence (at individual level); trade-off of individual accuracy and group consistency • (3) fMRI priors improve IID but not MSP (presumably fMRI blobs already covered by patches in MSP) Taylor & Henson, submitted

14. Our approach: Use sensor stats to constrain/inform source analysis ->Identify time-windows of interest ->divide into sub-windows based on hierarchical cluster analysis Selecting time-windows for contrasts

15. Taylor & Henson, submitted

16. Taylor & Henson, submitted

17. Taylor & Henson, submitted

18. Discussed yesterday: Sparse source images tend not to be Gaussian (e.g., MSP) SPMs, PPMs, SnPMs Trade-off?: individual accuracy vs. group consistency Statistics

19. fMRI priors When several fMRI (or other) priors are entered separately, each may be up- or down-weighted Different priors may be endorsed for different subjects Group optimisation reduces these inter-subject differences Taylor & Henson, submitted

20. Discussed yesterday: Sparse source images tend not to be Gaussian (e.g., MSP) Also yesterday: SPMs, PPMs, SnPMs Trade-off?: individual accuracy vs. group consistency Factorise time: allows inferences about emergence/disappearance of effects Statistics

21. Condition X Time-Window Interactions Taylor & Henson, submitted

22. - The End - • Thanks!