Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA)
Fig 6
Comparison between PCA and MVPA components.
Top: Median (dots) and 25%-75% percentile range (vertical lines) of the total covariance in functional connectivity patterns at each voxel explained cumulatively by the first k components from a functional connectivity Principal Component Analysis (black dots and lines), and by the first k fc-MVPA eigenpatterns (light gray dots and lines), from the analysis of the same sample dataset (Cambridge, n = 198 dataset). Bottom: First five principal components from PCA (first row) and from fc-MVPA (second and third row, first five eigenvariates shown only at two sample locations: posterior cingulate and anterior insula). Each row shows individual components sorted from first/left to fifth/right, projected to a left hemisphere lateral view (top image) and medial view (bottom image), on a relative color scale ranging from blue (highest negative values for each component) to yellow (highest positive values). Larger explanatory power of fc-MVPA components compared to PCA (shown on top figure) stems largely from the ability of fc-MVPA components to adapt to the specificity of the functional connectivity patterns at each individual location (as exemplified in the bottom figures by the differences and commonalities between the components describing posterior cingulate vs. anterior insula connectivity patterns).