Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA)
Fig 7
Validation of fc-MVPA voxel-level inferences.
Analysis of Receiver Operating Characteristic curves evaluating between-group differences in functional connectivity under the null hypothesis (when there are no true differences in the population). Top left: surfaces, and highlighted thick black lines, show, for a chosen combination of false positive threshold (false positive rate x-axis) and number of eigenpatterns (k y-axis), the resulting proportion of false positive results (positive rate z-axis), where the fc-MVPA procedure would falsely conclude there is a significant difference in connectivity between the groups. The red line marks the observed rate of false positives when fixing the prescribed false positive rate threshold at a fixed 5% level (graphically, the intersection of each ROC surface and a vertical plane with constant false positive rate = 0.05), matching the expected 5% level. Top Right: Observed false positive rates (y-axis) when using fc-MVPA statistical analyses controlled at a p < .05 level across the reference simulations (‘reference’) and simulations evaluating different conditions (FWHM = 0, FHWM-25, N = 10, N = 100, Nt = 10, Nt = 100). The average (black dots) and histogram (gray surfaces) of the observed false positive rates across these simulations all indicate an appropriate match to the expected/prescribed false positive level (5%). Bottom: evaluating validity under different conditions: (A) low spatial autocorrelation (FWHM = 0); (B) large spatial autocorrelation (FWHM = 25 voxels); (C) low number of subjects (N = 10); (D) high number of subjects (N = 100); (E) short scanning session (Nt = 10); (F) long scanning session (Nt = 100).