Haskins fMRI Workshop Part III: Across Subjects Analysis - Univariate, Multivariate, Connectivity

1. Haskins fMRI WorkshopPart III:Across Subjects Analysis - Univariate, Multivariate, Connectivity

2. Across-subjects “Composite” Maps Recall: two-stage analysis • Stage 1: extract subject maps for effects of interest (B weights) • Stage 2: at each voxel, test the values across-subjects versus zero • t-test, ANOVA with planned comparisons or contrasts • each new composite map shows p-values for one subject-level effect [ single-subject maps ] [ composite map ] + + + = S1 S2 S3 S4 average

3. data matrix layout for across-subjects analysis

4. CRM study: “new” words

5. CRM study: “old” words

6. CRM study: contrast of old-new words

7. Thresholds What is the appropriate threshold? • 902,629 voxels in standard MNI space image; 258,370 actually in-brain • Type I and Type II error Approaches: • assume real activations are large (reduce number of actual tests) control Family-Wise Error Rate “chance of any false positives” • alternatively: control False Discovery Rate “proportion of false positives among rejected tests” • employ a priori regions-of-interest (ROIs) • multivariate analysis

8. Correlational Analysis • across subjects: at each voxel, correlate the activation level to some external subject variable like age:

9. Correlational Analysis • across subjects: at each voxel, correlate the activation level to some external subject variable like age... or behavioral skill:

10. Multivariate Analysis • PCA/SVD/Eigenimage analysis/ICA • within subject: identify set of (1) spatial patterns with (2) associated timecourse • across subject: identify spatial patterns with associated subject loadings • data driven, work only on the input image data (not classified by condition, subject group, etc.) • PLS (Partial Least Squares) • across subject: identify spatial patterns that change from task to task • also data driven, but optimized to identify task-related changes • identifies the strongest possible contrasts among conditions

11. Multivariate Analysis Calhoun et al., Human Brain Mapping, 2001

12. Connectivity Functional Connectivity: • df: correlations between spatially remote neurophysiological events • does not imply causality, but identifies covariation • subject to “third variable” explanations Effective Connectivity: • df: the influence one neuronal system exerts on another • implies causality; requires something beyond correlations and correlational analysis such as • tests of temporal relations (e.g. lagged autocorrelation analysis) • SEM - model testing

13. Within- vs. Between-Subjects Connectivity Within-subject Connectivity: • df: correlations over time-course of a single study activations by time point

14. Within- vs. Between-Subjects Connectivity Within-subject Connectivity: • difficulties... • low signal-to-noise • primarily reported in low frequencies <20sec/cycle • HRF response dissimilar across regions Hampson et al., Human Brain Mapping, 2002

15. Within- vs. Between-Subjects Connectivity Between-subject Connectivity: • df: correlations over subjects within a single task • cf Horwitz et al., 1984 (!) activations by subject number

16. Pugh et al., 2000; also Horwitz et al., 1992

17. Functional Connectivity activations in Shaywitz et al. 2002 older good readers 74 good readers 7-18 yrs 70 dyslexic readers 7-18 yrs

18. Functional Connectivity seed voxel correlations older good readers

19. Functional Connectivity selected univariate correlations

20. Functional Connectivity univariate correlations Older Non-Impaired

21. Functional Connectivity Younger Non-Impaired Older Non-Impaired univariate correlations Older Dyslexics Younger Dyslexics

22. Functional Connectivity First Component Second Component

23. Connecticut Longitudinal Study: Connectivity Shaywitz et al., 2003

24. Worsely et al., 2005

25. Dynamic System…