Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis

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Hirayama , J , Hyvarinen , A , Kiviniemi , V , Kawanabe , M & Yamashita , O 2016 , ' Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis ' , PLoS One , vol. 11 , no. 12 , 0168180 . https://doi.org/10.1371/journal.pone.0168180

Title: Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis
Author: Hirayama, Jun-ichiro; Hyvarinen, Aapo; Kiviniemi, Vesa; Kawanabe, Motoaki; Yamashita, Okito
Contributor organization: Department of Computer Science
Neuroinformatics research group / Aapo Hyvärinen
Helsinki Institute for Information Technology
Finnish Centre of Excellence in Algorithmic Data Analysis Research (Algodan)
Date: 2016-12-21
Language: eng
Number of pages: 24
Belongs to series: PLoS One
ISSN: 1932-6203
DOI: https://doi.org/10.1371/journal.pone.0168180
URI: http://hdl.handle.net/10138/176220
Abstract: Characterizing the variability of resting-state functional brain connectivity across subjects and/or over time has recently attracted much attention. Principal component analysis (PCA) serves as a fundamental statistical technique for such analyses. However, performing PCA on high-dimensional connectivity matrices yields complicated "eigenconnectivity" patterns, for which systematic interpretation is a challenging issue. Here, we overcome this issue with a novel constrained PCA method for connectivity matrices by extending the idea of the previously proposed orthogonal connectivity factorization method. Our new method, modular connectivity factorization (MCF), explicitly introduces the modularity of brain networks as a parametric constraint on eigenconnectivity matrices. In particular, MCF analyzes the variability in both intra-and inter-module connectivities, simultaneously finding network modules in a principled, data-driven manner. The parametric constraint provides a compact module based visualization scheme with which the result can be intuitively interpreted. We develop an optimization algorithm to solve the constrained PCA problem and validate our method in simulation studies and with a resting-state functional connectivity MRI dataset of 986 subjects. The results show that the proposed MCF method successfully reveals the underlying modular eigenconnectivity patterns in more general situations and is a promising alternative to existing methods.
Subject: FUNCTIONAL CONNECTIVITY
RESTING-STATE
NETWORKS
DYNAMICS
PATTERNS
113 Computer and information sciences
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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