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Statistical Analysis of M/EEG Sensor- and Source-Level Data Jason Taylor

Learn about practical aspects of statistical analysis for M/EEG sensor and source-level data, including SPM approach, Random Field Theory correction, multiple comparison problem, and future directions. Get insights on where and when to detect effects in time, frequency, sensor, and source spaces.

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Statistical Analysis of M/EEG Sensor- and Source-Level Data Jason Taylor

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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) Cambridge, UK jason.taylor@mrc-cbu.cam.ac.uk | http://imaging.mrc-cbu.cam.ac.uk (wiki) 09 May 2011 | London | Thanks to Rik Henson and Dan Wakeman @ CBU

  2. Overview The SPM approach to M/EEG Statistics: A mass-univariate statistical approach to inference regarding effects in space/time/frequency (using replications across trials or subjects). • In Sensor Space: 2D Time-Frequency 3D Topography-by-Time • In Source Space: 3D Time-Frequency contrast images SPM vs SnPM vs PPM vs FDR Other issues & Future directions

  3. The Multiple Comparison Problem Random Field Theory (RFT) is a method for correcting for multiple statistical comparisons with N-dimensional spaces (for parametric statistics, e.g., Z-, T-, F- statistics). Note these comparisons are often made implicitly, e.g., by viewing (“eye-balling”) data before selecting a time-window or set of channels for statistical analysis! RFT correction depends on the search volume. -> When is there an effect in time, e.g., GFP (1D)? -> When/What frequency is there an effect in time and frequency (2D)? -> When/Where is there an effect in sensor topography space and time (3D)? -> When/Where is there an effect in source space and time (4-ish D)? Worsley Et Al (1996). Human Brain Mapping, 4:58-73

  4. CTF Multimodal Faces Dataset | SPM8 Manual (Rik Henson) CTF MEG & EEG Data: 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. Neuromag Faces Dataset | Dan Wakeman & Rik Henson @ CBU Neuromag MEG & EEG Data: 70 EEG 102 Magnetometers 204 Planar Gradiometers H&VEOG ECG 1mm3 sMRI 6 sessions faces & scrambled-face images (480 trials) Group: N=18 Biomag 2010 Award-Winning Dataset!

  6. Neuromag Lexical Decision Dataset | Taylor & Henson @ CBU Neuromag MEG Data: 102 Magnetometers 204 Planar Gradiometers H&VEOG ECG 1mm3 sMRI 4 sessions words & pseudowords presented visually (480 trials) Group: N=18 Taylor & Henson, submitted

  7. 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 CTF Multimodal Faces

  8. 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) Neuromag Faces

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

  10. GLM: Removing variance due to confounds 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

  11. 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 Neuromag Lexical Decision

  12. 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) 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

  13. Where is an effect in source space (3D)? MEG E+MEG EEG A Neuromag Faces

  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) Sensors Mags Grads Over Participants Over Voxels Neuromag Lexical Decision

  15. 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 (fused sensors) MSP IID Over Participants Over Voxels Neuromag Lexical Decision

  16. 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/ CTF Multimodal Faces

  17. 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) CTF Multimodal Faces

  18. 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 FWE 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) CTF Multimodal Faces

  19. Where and When so effects emerge/disappear in source space (3D)? Condition x Time-window Interactions Factorising time allows you to infer (rather than simply describe) when effects emerge or disappear. We've used a data-driven method (hierarchical cluster analysis) in sensor space to define time-windows of interest for source-space analyses. * high-dimensional distance between topography vectors * cut the cluster tree where the solution is stable over longest range of distances Taylor & Henson, submitted Neuromag Lexical Decision

  20. Where and When so effects emerge/disappear in source space (3D)? Condition x Time-window Interactions Factorising time allows you to infer (rather than simply describe) when effects emerge or disappear. We've used a data-driven method (hierarchical cluster analysis) in sensor space to define time-windows of interest for source-space analyses. * estimate sources in each sub-time-window * submit to GLM with conditions & time-windows as factors (here: Cond effects per TWin) Taylor & Henson, submitted Neuromag Lexical Decision

  21. Where and When so effects emerge/disappear in source space (3D)? Condition x Time-window Interactions Factorising time allows you to infer (rather than simply describe) when effects emerge or disappear. We've used a data-driven method (hierarchical cluster analysis) in sensor space to define time-windows of interest for source-space analyses. * estimate sources in each sub-time-window * submit to GLM with conditions & time-windows as factors (here: Cond x TW interactions) Taylor & Henson, submitted Neuromag Lexical Decision

  22. Future Directions • Extend RFT to 2D cortical surfaces (+1D time)? (e.g., Pantazis et al., 2005, NImage) • Novel approaches to the multiple comparison problem (e.g., 'unique extrema' in sensors: Barnes et al., 2011, NImage) • Go multivariate? To identify linear combinations of spatial (sensor or source) effects in time and space (e.g., Carbonnell, 2004, NImage) To detect spatiotemporal patterns in 3D images (e.g., Duzel, 2004, NImage; Kherif, 2003, NImage)

  23. - The End - Thanks!

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