Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA)
Fig 8
Sensitivity of fc-MVPA voxel-level inferences.
Analysis of Receiver Operating Characteristic curves evaluating between-group differences in functional connectivity. 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 true positive results (positive rate z-axis), where the fc-MVPA procedure would correctly conclude there is a significant difference in connectivity between the groups in our reference simulations. Top Right: Observed true 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 true positive rates, or proportion of significant results, across these simulations indicate that sensitivity is typically higher when using low or intermediate numbers of eigenpatterns, with poorer sensitivity when the number of timepoints for functional connectivity estimation is low (Nt = 10), or when the number of subjects included in the analysis is low (N = 10). Bottom: evaluating sensitivity under different conditions: (A) no 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).