Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA)

PLoS Comput Biol. 2022 Nov 15;18(11):e1010634. doi: 10.1371/journal.pcbi.1010634. eCollection 2022 Nov.

Abstract

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 method using 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 analysis of gender differences across the entire functional connectome. Finally, Monte Carlo simulations are used to demonstrate the validity and sensitivity of this method. In addition to offering powerful whole-brain inferences, fc-MVPA also provides a meaningful characterization of the heterogeneity in functional connectivity across subjects.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Brain / diagnostic imaging
  • Connectome* / methods
  • Diffusion Magnetic Resonance Imaging
  • Humans
  • Magnetic Resonance Imaging / methods
  • Multivariate Analysis
  • Nerve Net / diagnostic imaging