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Front. Neurosci. | doi: 10.3389/fnins.2018.00114

Variation in Resting State Network and Functional Network Connectivity Associated with Schizophrenia Genetic Risk: A Pilot Study

  • 1Mind Research Network, United States

Imaging genetics posits a valuable strategy for elucidating genetic influences on brain abnormalities in psychiatric disorders. However, association analysis between 2D genetic data (subject × genetic variable) and 3D first-level functional magnetic resonance imaging (fMRI) data (subject × voxel × time) has been challenging given the asymmetry in data dimension. A summary feature needs to be derived for the imaging modality to compute inter-modality association at subject level. In this work, we propose to use variability in resting state networks (RSNs) and functional network connectivity (FNC) as potential features for purpose of association analysis. We conducted a pilot study to investigate the proposed features in a dataset of 171 healthy controls and 134 patients with schizophrenia (SZ). We computed variability in RSN and FNC in a group independent component analysis framework and tested three types of variability metrics, namely Euclidean distance, Pearson correlation and Kullback-Leibler divergence. Euclidean distance and Pearson correlation metrics more effectively discriminated controls from patients than Kullback-Leibler divergence. The group differences observed with variability in RSN and FNC were highly consistent, indicating patients presenting increased deviation from the cohort-common pattern of RSN and FNC than controls. The variability in RSN and FNC showed significant associations with network global efficiency, the more the deviation, the lower the efficiency. Furthermore, the RSN and FNC variability were found to associate with individual SZ risk SNPs as well as cumulative polygenic risk score for SZ. Collectively the current findings provide preliminary evidence for variability in RSN and FNC being promising imaging features that may find applications as biomarkers and in imaging genetic association analysis.

Keywords: variability, resting state network, functional network connectivity, Schizophrenia, PGC, parallel ICA

Received: 24 Aug 2017; Accepted: 13 Feb 2018.

Edited by:

Russell A. Poldrack, Stanford University, United States

Reviewed by:

Jean-Baptiste Poline, University of California, Berkeley, United States
Kaiming Li, Sichuan University, China
Gennady Knyazev, Institute of Physiology and Basic Medicine, Russia  

Copyright: © 2018 Chen, Rashid, Yu, Liu, Lin and Calhoun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Jiayu Chen, Mind Research Network, Albuquerque, United States,