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Practical Aspects of Statistical Analysis of M/EEG Sensor- and Source-Level Data

This article provides an overview of the practical aspects of statistical analysis of M/EEG sensor- and source-level data, using the mass-univariate statistical approach implemented in SPM. It covers topics such as the multiple comparison problem, familywise error rate, random field theory, and GLM analysis.

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Practical Aspects of Statistical Analysis of M/EEG Sensor- and Source-Level Data

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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/meg (wiki) 14 May 2012 | 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 4-ish-D space by (factorised) time Alternative approaches: SnPM, PPM, etc.

  3. Example Datasets

  4. CTF Multimodal Face-Evoked Dataset | SPM8 Manual (Rik Henson) CTF MEG/ActiveTwo EEG: 275 MEG 128 EEG 3T fMRI (with nulls) 1mm3sMRI 2 sessions Stimuli: ~160 face trials/session ~160 scrambled trials/session Group: N=12 subjects (Henson et al, 2009 a,b,c) Chapter 37 SPM8 Manual

  5. Neuromag Faces Dataset | Dan Wakeman & Rik Henson @ CBU Neuromag MEG & EEG Data: 102 Magnetometers 204 Planar Gradiometers 70 EEG HEOG, VEOG, ECG 1mm3 sMRI 6 sessions Stimuli: faces & scrambled-face images (480 trials total) Group: N=18 (Henson et al, 2011) 400- 600ms 800- 1000ms 1700ms Biomag 2010 Award-Winning Dataset!

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

  7. The Multiple Comparison Problem

  8. Where is the effect?: The curse of too much data

  9. The Multiple Comparison Problem The more comparisons we conduct, the more Type I errors (false positives) we will make when the Null Hypothesis is true. * Must consider Familywise (vs. per-comparison) Error Rate 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)? time

  10. The Multiple Comparison Problem The more comparisons we conduct, the more Type I errors (false positives) we will make when the Null Hypothesis is true. * Must consider Familywise (vs. per-comparison) Error Rate 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/at what frequency is there an effect an effect in time/frequency (2D)? frequency time

  11. The more comparisons we conduct, the more Type I errors (false positives) we will make when the Null Hypothesis is true. * Must consider Familywise (vs. per-comparison) Error Rate 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/where is there an effect in sensor-topography space/time (3D)? The Multiple Comparison Problem x y time

  12. The more comparisons we conduct, the more Type I errors (false positives) we will make when the Null Hypothesis is true. * Must consider Familywise (vs. per-comparison) Error Rate 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/where is there an effect in source space/time (4-ish-D)? The Multiple Comparison Problem x z y time

  13. The Multiple Comparison Problem • The more comparisons we conduct, the more Type I errors (false positives) we will make when the Null Hypothesis is true. • * Must consider Familywise (vs. per-comparison) Error Rate • 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. • 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). • * Takes smoothness of images into account Worsley Et Al (1996). Human Brain Mapping, 4:58-73

  14. GLM: Condition Effects after removing variance due to confounds Each trial-type (6) Confounds (4) Each trial W/in Subject (1st-level) model -> one image per trial -> one covariate value per trial Also: Group Analysis (2nd-level) -> one image per subject per condition -> one covariate value per subject per condition beta_00* image volumes reflect (adjusted) condition effects Henson et al., 2008, NImage

  15. Sensor-space analyses

  16. Faces Scrambled Where is an effect in time-frequency space? 1 subject (1st-level analysis) 1 MEG channel Morlet wavelet projection 1 t-x-f image per trial Faces > Scrambled Kilner et al., 2005, Nsci Letters CTF Multimodal Faces

  17. EEG 100-220ms 8-18Hz Faces > Scrambled 6 90 Freq (Hz) -500 +1000 Time (ms) Where is an effect in time-frequency space? 18 subjects 1 channel (2nd-level analysis) MEG (Mag) 6 90 Freq (Hz) -500 +1000 Time (ms) Neuromag Faces

  18. 3D Topography-x-Time Image volume (EEG) time y x Where is an effect in sensor-time space? 1 subject ALL EEG channels Each trial, Topographic projection (2D) for each time point (1D) = topo-time image volume (3D) Faces vs Scrambled CTF Multimodal Faces

  19. 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 (sessions, subjects) - using Signal Space Separation (SSS; Taulu et al, 2005; Neuromag systems) 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/ head-position transform Other evidence: ICA ‘splitting’ with movement Taylor & Henson (2008) Biomag Neuromag Lexical Decision

  20. Where is an effect in sensor-time space? Analysis over subjects (2nd Level): Magnetometers: Pseudowords vs Words PPM (P>.95 effect>0) Effect Size 18 fT -18 Taylor & Henson (submitted) Neuromag Lexical Decision

  21. Source-space analyses

  22. Where is an effect in source space (3D)? 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) - For each sensor type OR fused modalities Write data to 3D image - in MNI space - smooth along 2D surface Smooth by 3D Gaussian Submit to GLM Henson et al., 2007, NImage

  23. sensor fusion E+MEG MEG EEG Where is an effect in source space (3D)? RESULTS: Faces > Scrambled fMRI Henson et al, 2011 Neuromag Faces

  24. with group optimised fMRI priors EEG+MEG+fMRI with fMRI priors EEG+MEG+fMRI mean stats Where is an effect in source space (3D)? RESULTS: Faces > Scrambled fMRI Henson et al, 2011 Neuromag Faces

  25. Where and When do effects emerge/disappear in source space (4-ish-D: time factorised)? 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

  26. Where and When do effects emerge/disappear in source space (4-ish-D)? 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 t-win) source timecourses Taylor & Henson, submitted Neuromag Lexical Decision

  27. Where and When do effects emerge/disappear in source space (4-ish-D)? 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: Cond x TW interactions Taylor & Henson, submitted Neuromag Lexical Decision

  28. Alternative Approaches

  29. 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 correction conservative Distributions over subjects/voxels may not be Gaussian (e.g., if sparse, as in MSP) Alternative Approaches (Taylor & Henson, submitted; Biomag 2010)

  30. Alternative Approaches 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

  31. Alternative Approaches 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) p>.95 (γ>1SD) CTF Multimodal Faces

  32. Future Directions • Extend RFT to 2D cortical surfaces (+1D time)? (e.g., Pantazis et al., 2005, NImage) • Novel approaches to controlling for multiple comparisons (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; but see Barnes et al., 2011) To detect spatiotemporal patterns in 3D images (e.g., Duzel, 2004, NImage; Kherif, 2003, NImage)

  33. -- The end -- Thanks for listening Acknowledgements: Rik Henson (MRC CBU) Dan Wakeman (MRC CBU / now at MGH) Vladimir, Karl, and the FIL Methods Group More info: http://imaging.mrc-cbu.cam.ac.uk/meg (wiki) jason.taylor@mrc-cbu.cam.ac.uk

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