Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA)
Fig 3
Selecting different number of fc-MVPA eigenpatterns.
Difference in fc-MVPA statistic parametric maps evaluating gender differences in connectivity, when varying k, the number of fc-MVPA eigenpatterns used in the analysis, from k = 1 (left) to k = 100 (right). For reference, the original results shown in Fig 2 used k = 10 (highlighted here inside black box). Top: Statistic parametric maps with color coding showing voxel-level -log10(p) values for four different choices of k (from 5 to 20). The results show consistent statistic parametric maps across different k values. Bottom: Distribution of fc-MVPA statistics across all gray matter voxels with k ranging from 1 to 100, compared to null hypothesis distribution (shown in leftmost ‘null’ histogram). The results indicate high sensitivity across the entire range of evaluated k values, with sensitivity peaking at around k = 50 (close to a 4:1 ratio in subjects to eigenpatterns) for detecting widespread gender effects in this dataset.