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Statistical Analysis of M/EEG Sensor- and Source-Level Data Jason Taylor MRC Cognition and Brain Sciences Unit (CBU)

Practical aspects of…. Statistical Analysis of M/EEG Sensor- and Source-Level Data Jason Taylor MRC Cognition and Brain Sciences Unit (CBU) Cambridge Centre for Ageing and Neuroscience (CamCAN) 19 January 2011 | Brussels | Almost entirely stolen from Rik Henson. Overview.

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Statistical Analysis of M/EEG Sensor- and Source-Level Data Jason Taylor MRC Cognition and Brain Sciences Unit (CBU)

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  1. Practical aspects of… Statistical Analysis of M/EEG Sensor- and Source-Level Data Jason Taylor MRC Cognition and Brain Sciences Unit (CBU) Cambridge Centre for Ageing and Neuroscience (CamCAN) 19 January 2011 | Brussels | Almost entirely stolen from Rik Henson

  2. Overview A mass-univariate statistical approach to localising effects in space/time/frequency (using replications across trials/subjects)… • Sensor Space: 2D Time-Frequency (within-subject) 3D Topography-by-Time (within-subject) 3D Topography-by-Time (between-subjects) • Source Space: 3D Time-Frequency contrast images SPM vs SnPM vs PPM vs FDR Other issues & Future directions

  3. Random Field Theory (RFT) • RFT is a method for correcting for multiple statistical comparisons with N-dimensional spaces (for parametric statistics, e.g., Z-, T-, F- statistics)… -> When is there an effect in time, eg GFP (1D)? -> Where is there an effect in time-frequency space (2D)? -> Where is there an effect in time-sensor space (3D)? -> Where is there an effect in time-source space (4D)? Worsley Et Al (1996). Human Brain Mapping, 4:58-73

  4. “Multimodal Dataset” in SPM8 Manual (Rik Henson) Single subject: 128 EEG 275 MEG 3T fMRI (with nulls) 1mm3 sMRI 2 sessions ~160 face trials and ~160 scrambled trials per session Group: N=12 subjects, as in Henson et al, 2009 a,b,c) Chapter 33 SPM8 Manual

  5. “CBU Neuromag Dataset” (Dan Wakeman & Rik Henson) Single subject: 70 EEG 102 Magnetometers 204 Planar Gradiometers H&VEOG ECG 1mm3 sMRI 6 sessions faces & scrambled-face images Group: N=18 Biomag 2010 Award-Winning Dataset!

  6. Where is an effect in time-frequency space? • Single MEG channel • Mean over trials of Morlet wavelet projection (i.e., induced + evoked) • Write t x f image per trial • Compute SPM, RFT correct Faces > Scrambled Faces Scrambled Kilner et al., 2005, Nsci Letters Multimodal Dataset

  7. Where is an effect in time-frequency space? Single channel -Effect over subjects EEG MEG (Mag) 100-220ms 8-18Hz Faces > Scrambled 6 90 6 90 Freq (Hz) Freq (Hz) -500 +1000 -500 +1000 Time (ms) Time (ms) CBU Neuromag Dataset

  8. Where is an effect in sensor-time space? Stats (F): Faces vs Scrambled (single subject, over trials) Topography-x-Time Image (EEG) Multimodal Dataset

  9. Where is an effect in sensor-time space? Each trial-type (6) Confounds (4) Each trial W/in Subject (1st-level) model beta_00* images reflect mean (adjusted) 3D topo-time volume for each condition Henson et al., 2008, NImage

  10. Where is an effect in sensor-time space? Analysis over subjects (2nd Level) NOTE for MEG: Complicated by variability in head-position SOLUTION: Virtual transformation to same position in sessions, subjects Without transformation to Device Space With transformation to Device Space • Stats over 18 subjects, RMS of planar gradiometers • Improved (i.e., more blobs, • larger T values) w/ transform Taylor & Henson (2008) Biomag

  11. Where is an effect in source space (3D)? Source Analysis over N=12 subjects, MEG: 102 mags, MSP, evoked, RMS of source energy, smoothed 12-mm kernel STEPS: Estimate evoked/induced energy (RMS) at each dipole for a certain time-frequency window of interest (e.g., 100-220ms, 8-18 Hz) For each condition (Faces, Scrambled images) Smooth along 2D surface Write data to 3D image (in MNI space) Smooth by 3D gaussian Analysis Mask Note sparseness of MSP inversions…. Henson et al., 2007, NImage

  12. Where is an effect in source space (3D)? MEG E+MEG EEG A CBU Neuromag Dataset

  13. Where is an effect in source space (3D)? • Classical SPM approach • Caveats: • Inverse operator induces long-range error correlations (e.g., similar gain vectors from non-adjacent dipoles with similar orientation), making RFT conservative • Need a cortical mask, else activity ‘smoothed’ outside • Distributions over subjects may not be Gaussian (e.g., if sparse, as in MSP) • (Taylor & Henson, submitted; Biomag 2010) Sources MSP IID Over Participants Over Voxels

  14. Where is an effect in source space (3D)? • Classical SPM approach • Caveats: • Inverse operator induces long-range error correlations (e.g., similar gain vectors from non-adjacent dipoles with similar orientation), making RFT conservative • Need a cortical mask, else activity ‘smoothed’ outside • Distributions over subjects may not be Gaussian (e.g., if sparse, as in MSP) • (Taylor & Henson, submitted; Biomag 2010) Sources Mags Grads (RMS) Over Participants Over Voxels

  15. Where is an effect in source space (3D)? Non-Parametric Approach (SnPM) Robust to non-Gaussian distributions Less conservative than RFT when dfs<20 Caveats: No idea of effect size (e.g., for power, future expts) Exchangeability difficult for more complex designs (Taylor & Henson, Biomag 2010) p<.05 FWE SnPM Toolbox by Holmes & Nichols: http://go.warwick.ac.uk/tenichols/software/snpm/ Multimodal Dataset

  16. Where is an effect in source space (3D)? Posterior Probability Maps (PPMs) Bayesian Inference No need for RFT (no MCP) Threshold on posterior probabilty of an effect greater than some size Can show effect size after thresholding Caveats: Assume Gaussian distribution (e.g., of mean over voxels), sometimes not met (ok for IID…?) (Taylor & Henson, submitted; Biomag 2010) p>.95 (γ>1SD) Multimodal Dataset

  17. Where is an effect in source space (3D)? p<.001 unc SPM Topological FDR correction Choose an uncorrected threshold (e.g., p<.001) to define topologial features (e.g., peak and cluster size) Topological FDR actually produces higher corrected p-values (i.e., fewer suprathreshol voxels) than FEW in the data used here Caveat: If sources are constrained to a gray matter cortical surface, are topological features as meaningful? (Taylor & Henson, Biomag 2010) Multimodal Dataset

  18. Future Directions • Extend RFT to 2D cortical surfaces (+1D time)? (e.g., Pantazis et al., 2005, Nimage) • Go multivariate? To localise linear combinations of spatial (sensor or source) effects in time (e.g., Carbonnell, 2004, NImage; Barnes & Litvak, Biomag 2010) To detect spatiotemporal patterns in 3D images (e.g., Duzel, 2004, NImage; Kherif, 2003, NImage)

  19. - The End - • Thanks!

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