Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA)
Name
journal.pcbi.1010634.pdf
Size
3.66 MB
Format
Adobe PDF
Checksum (MD5)
c952f121b6230b3a386362eb97057065
Author(s)
Nieto-Castanon, Alfonso
Date Issued
November 15, 2022
Publisher
Public Library of Science (PLoS)
Citation
Nieto-Castanon, Alfonso. 2022. "Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA)." 18 (11).
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.
Subjects
Computational Theory and Mathematics
Cellular and Molecular Neuroscience
Genetics
Molecular Biology
Ecology
Modeling and Simulation
Ecology, Evolution, Behavior and Systematics
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1010634
