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

2. 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. 3 data matrix layout for across-subjects analysis

4. 4 CRM study: “new” words

5. 5 CRM study: “old” words

6. 6 CRM study: contrast of old-new words

7. 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. 8 Correlational Analysis across subjects: at each voxel, correlate the activation level to some external subject variable like age:

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

10. 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. 11 Multivariate Analysis Calhoun et al., Human Brain Mapping, 2001

12. 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. 13 Within- vs. Between-Subjects Connectivity Within-subject Connectivity: df: correlations over time-course of a single study activations by time point

14. 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. 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. 16 Pugh et al., 2000; also Horwitz et al., 1992

17. 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. 18 Functional Connectivity seed voxel correlations older good readers

19. 19 Functional Connectivity selected univariate correlations

20. 20 Functional Connectivity Older Non-Impaired univariate correlations

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

22. 22 Functional Connectivity First Component Second Component

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

24. 24 Worsely et al., 2005

25. 25 Dynamic System…