Abstract
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 (KL) divergence. Euclidean distance and Pearson correlation metrics more effectively discriminated controls from patients than KL 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.
Highlights
Most psychiatric disorders have been characterized to present moderate to high heritability in family and twin studies (Kendler and Eaves, 2005)
Our results suggest that variability in resting state networks (RSNs) and functional network connectivity (FNC) consistently discriminate controls from patients with SZ and show preliminary single nucleotide polymorphisms (SNPs) associations, lending support for it being a promising feature in imaging genetic association analysis
We demonstrate that variabilities in RSN and FNC may serve as meaningful brain-based phenotypes in imaging genetic association analysis
Summary
Most psychiatric disorders have been characterized to present moderate to high heritability in family and twin studies (Kendler and Eaves, 2005). The most well-characterized is schizophrenia, for which 108 genome-wide significant risk loci have been identified in a large GWAS of 36,989 cases and 113,075 controls (Ripke et al, 2014). Patients with psychiatric disorders present brain structural and functional abnormalities, which may underlie the clinical manifestations. Aberrant resting state functions have been noted in people at high risk of developing psychiatric disorders (Meda et al, 2016)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
