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Neuroimage. 2019 Jan 1;184:843-854. doi: 10.1016/j.neuroimage.2018.10.004. Epub 2018 Oct 6.

A framework for linking resting-state chronnectome/genome features in schizophrenia: A pilot study.

Author information

1
Harvard Medical School, Boston, MA, USA; The Mind Research Network & LBERI, Albuquerque, NM, USA. Electronic address: barnaly_rashid@hms.harvard.edu.
2
The Mind Research Network & LBERI, Albuquerque, NM, USA.
3
Department of Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM, USA.
4
The Mind Research Network & LBERI, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA.
5
Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA.
6
Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA.
7
The Mind Research Network & LBERI, Albuquerque, NM, USA; Department of Psychology and Neuroscience, Georgia State University, Atlanta, GA, USA.
8
Department of Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; Veterans Affairs San Francisco Healthcare System, San Francisco, CA, USA.
9
Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA.
10
Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
11
Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA.
12
Department of Psychiatry, University of California Irvine, Irvine, CA, USA.
13
Department of Psychiatry & Neuroscience, University of New Mexico, Albuquerque, NM, USA.
14
Olin Neuropsychiatry Research Center - Institute of Living, Hartford, CT, USA; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Department of Neurobiology, Yale University School of Medicine, New Haven, CT, USA.
15
The Mind Research Network & LBERI, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA. Electronic address: vcalhoun@mrn.org.

Abstract

Multimodal, imaging-genomics techniques offer a platform for understanding genetic influences on brain abnormalities in psychiatric disorders. Such approaches utilize the information available from both imaging and genomics data and identify their association. Particularly for complex disorders such as schizophrenia, the relationship between imaging and genomic features may be better understood by incorporating additional information provided by advanced multimodal modeling. In this study, we propose a novel framework to combine features corresponding to functional magnetic resonance imaging (functional) and single nucleotide polymorphism (SNP) data from 61 schizophrenia (SZ) patients and 87 healthy controls (HC). In particular, the features for the functional and genetic modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) features and the SNP data, respectively. The dFNC features are estimated from component time-courses, obtained using group independent component analysis (ICA), by computing sliding-window functional network connectivity, and then estimating subject specific states from this dFNC data using a k-means clustering approach. For each subject, both the functional (dFNC states) and SNP data are selected as features for a parallel ICA (pICA) based imaging-genomic framework. This analysis identified a significant association between a SNP component (defined by large clusters of functionally related SNPs statistically correlated with phenotype components) and time-varying or dFNC component (defined by clusters of related connectivity links among distant brain regions distributed across discrete dynamic states, and statistically correlated with genomic components) in schizophrenia. Importantly, the polygenetic risk score (PRS) for SZ (computed as a linearly weighted sum of the genotype profiles with weights derived from the odds ratios of the psychiatric genomics consortium (PGC)) was negatively correlated with the significant dFNC component, which were mostly present within a state that exhibited a lower occupancy rate in individuals with SZ compared with HC, hence identifying a potential dFNC imaging biomarker for schizophrenia. Taken together, the current findings provide preliminary evidence for a link between dFNC measures and genetic risk, suggesting the application of dFNC patterns as biomarkers in imaging genetic association study.

KEYWORDS:

Dynamic functional connectivity; Multimodal analysis; Parallel ICA; Resting-state fMRI; Schizophrenia; Single nucleotide polymorphism

PMID:
30300752
PMCID:
PMC6230505
[Available on 2020-01-01]
DOI:
10.1016/j.neuroimage.2018.10.004
[Indexed for MEDLINE]

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