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

Barnaly Rashid, Jiayu Chen, Ishtiaque Rashid, Eswar Damaraju, Jingyu Liu, Robyn Miller, Oktay Agcaoglu, Theo G.M. van Erp, Kelvin O. Lim, Jessica A. Turner, Daniel H. Mathalon, Judith M. Ford, James Voyvodic, Bryon A. Mueller, Aysenil Belger, Sarah McEwen, Steven G. Potkin, Adrian Preda, Juan R. Bustillo, Godfrey D. Pearlson & 1 others Vince D. Calhoun

Research output: Contribution to journalArticle

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.

LanguageEnglish (US)
Pages843-854
Number of pages12
JournalNeuroImage
Volume184
DOIs
StatePublished - Jan 1 2019

Fingerprint

Schizophrenia
Single Nucleotide Polymorphism
Genome
Genomics
Psychiatry
Biomarkers
Multimodal Imaging
Brain
Genetic Association Studies
Cluster Analysis
Odds Ratio
Genotype
Magnetic Resonance Imaging
Phenotype
Weights and Measures

Keywords

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

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

Cite this

Rashid, B., Chen, J., Rashid, I., Damaraju, E., Liu, J., Miller, R., ... Calhoun, V. D. (2019). A framework for linking resting-state chronnectome/genome features in schizophrenia: A pilot study. NeuroImage, 184, 843-854. https://doi.org/10.1016/j.neuroimage.2018.10.004

A framework for linking resting-state chronnectome/genome features in schizophrenia : A pilot study. / Rashid, Barnaly; Chen, Jiayu; Rashid, Ishtiaque; Damaraju, Eswar; Liu, Jingyu; Miller, Robyn; Agcaoglu, Oktay; van Erp, Theo G.M.; Lim, Kelvin O.; Turner, Jessica A.; Mathalon, Daniel H.; Ford, Judith M.; Voyvodic, James; Mueller, Bryon A.; Belger, Aysenil; McEwen, Sarah; Potkin, Steven G.; Preda, Adrian; Bustillo, Juan R.; Pearlson, Godfrey D.; Calhoun, Vince D.

In: NeuroImage, Vol. 184, 01.01.2019, p. 843-854.

Research output: Contribution to journalArticle

Rashid, B, Chen, J, Rashid, I, Damaraju, E, Liu, J, Miller, R, Agcaoglu, O, van Erp, TGM, Lim, KO, Turner, JA, Mathalon, DH, Ford, JM, Voyvodic, J, Mueller, BA, Belger, A, McEwen, S, Potkin, SG, Preda, A, Bustillo, JR, Pearlson, GD & Calhoun, VD 2019, 'A framework for linking resting-state chronnectome/genome features in schizophrenia: A pilot study' NeuroImage, vol. 184, pp. 843-854. https://doi.org/10.1016/j.neuroimage.2018.10.004
Rashid, Barnaly ; Chen, Jiayu ; Rashid, Ishtiaque ; Damaraju, Eswar ; Liu, Jingyu ; Miller, Robyn ; Agcaoglu, Oktay ; van Erp, Theo G.M. ; Lim, Kelvin O. ; Turner, Jessica A. ; Mathalon, Daniel H. ; Ford, Judith M. ; Voyvodic, James ; Mueller, Bryon A. ; Belger, Aysenil ; McEwen, Sarah ; Potkin, Steven G. ; Preda, Adrian ; Bustillo, Juan R. ; Pearlson, Godfrey D. ; Calhoun, Vince D. / A framework for linking resting-state chronnectome/genome features in schizophrenia : A pilot study. In: NeuroImage. 2019 ; Vol. 184. pp. 843-854.
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