  Current functional Magnetic Resonance Imaging technology is able to resolve
billions of individual functional connections characterizing the human
connectome. Classical statistical inferential procedures attempting to make
valid inferences across this many measures from a reduced set of observations
and from a limited number of subjects can be severely underpowered for any but
the largest effect sizes. This manuscript discusses fc-MVPA (functional
connectivity Multivariate Pattern Analysis), a novel application of
multivariate pattern analysis techniques in the context of brain-wide
connectome inferences. The theory behind fc-MVPA is presented, and several of
its key concepts are illustrated through examples from a publicly available
resting state dataset, including an example analysis evaluating gender
differences across the entire functional connectome. Last, Monte Carlo
simulations are used to demonstrated this method's validity and sensitivity. In
addition to offering powerful whole-brain inferences, fc-MVPA also provides a
meaningful characterization of the heterogeneity in functional connectivity
across subjects.
