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Group analyses

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  1. Group analyses Will Penny Wellcome Dept. of Imaging Neuroscience University College London

  2. Data fMRI, single subject EEG/MEG, single subject Time fMRI, multi-subject ERP/ERF, multi-subject Hierarchical model for all imaging data!

  3. Reminder: voxel by voxel model specification parameter estimation Time hypothesis statistic Time Intensity single voxel time series SPM

  4. General Linear Model = + Error Covariance • Model is specified by • Design matrix X • Assumptions about e N: number of scans p: number of regressors

  5. L g l Estimation 1. ReML-algorithm 2. Weighted Least Squares Friston et al. 2002, Neuroimage

  6. Hierarchical model Multiple variance components at each level Hierarchical model At each level, distribution of parameters is given by level above. What we don’t know: distribution of parameters and variance parameters.

  7. Example: Two level model = + + = Second level First level

  8. Estimation Hierarchical model Single-level model

  9. Group analysis in practice Many 2-level models are just too big to compute. And even if, it takes a long time! Is there a fast approximation?

  10. Summary Statistics approach First level Second level DataDesign MatrixContrast Images SPM(t) One-sample t-test @ 2nd level

  11. Validity of approach The summary stats approach is exact if for each session/subject: Within-session covariance the same First-level design the same All other cases: Summary stats approach seems to be robust against typical violations.

  12. Auditory Data Summary statistics Hierarchical Model Friston et al. (2004) Mixed effects and fMRI studies, Neuroimage

  13. Multiple contrasts per subject Stimuli: Auditory Presentation (SOA = 4 secs) of words Subjects: (i) 12 control subjects fMRI, 250 scans per subject, block design Scanning: What regions are affected by the semantic content of the words? Question: U. Noppeney et al.

  14. ANOVA 1st level: 1.Motion 2.Sound 3.Visual 4.Action ? = ? = ? = 2nd level:

  15. ANOVA 1st level: Motion Sound Visual Action ? = ? = ? = 2nd level:

  16. Summary Linear hierarchical models are general enough for typical multi-subject imaging data (PET, fMRI, EEG/MEG). Summary statistics are robust approximation for group analysis. Also accomodates multiple contrasts per subject.