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

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.

Multiframe Evolving Dynamic Functional Connectivity (EVOdFNC): A Method for Constructing and Investigating Functional Brain Motifs

“…The evolving motifs fluidly move through transient states of connectivity that resemble familiar formations obtained from the basic time-blind clustering into single transiently realized connectivity patterns ( Figure 2 ). Consistent with published results [35, 49, 53, 54, 57] on occupancy rates of time-blind SNAPdFNC states, we find here that: strongly modularized and hyperconnected patterns feature more prominently in EVOdFNCs with greater representational importance in controls (1, 5, 7 and 8) and in EVOdFNCs whose representational importance in controls is not statistically distinguishable from that in patients (4, 6, 9); weak connectivity and modularized negative DMN-to-other (DMNneg) patterns (2, 3) feature more prominently in EVOdFNCs with significantly higher representational importance in patients. A novel modularized pattern of functional organization, not seen in time-blind SNAPdFNC states, appears in EVOdFNC 10, which features a persistent stretch of strong modularized negative SM-VIS/CC/DMN connectivity.…”

Characterizing Rapid Fluctuations of Resting State Functional Connectivity in Demyelinating, Neurodegenerative, and Psychiatric Conditions: From Static to Time-Varying Analysis

“…Patients with bipolar disorder and major depression expressed TVC abnormalities mainly in executive (Rashid et al, 2014; Du et al, 2017), amygdala/salience (Qiu et al, 2018; Zhi et al, 2018), and salience/executive regions (Mokhtari et al, 2018a,b). Schizophrenia patients showed a complex pattern of decreased and increased TVC mainly in the DMN (Sakoglu et al, 2010; Abrol et al, 2017), and in frontal, parietal, auditory (Damaraju et al, 2014; Rashid et al, 2014; Du et al, 2017, 2018; Sun et al, 2018), visual (Fu et al, 2018; Rashid et al, 2018b; Sun et al, 2018), and thalamic areas (Damaraju et al, 2014; Rashid et al, 2014, 2018b; Du et al, 2018). Schizophrenia patients spent less time and made fewer transitions between states characterized by weak correlations between the thalami and sense-related brain regions (Damaraju et al, 2014).…”

Transcriptional Signatures of the Hierarchical Chronnectome Organization in the Human Brain

“…Our study revealed a significant coupling between the expression pattern of mitochondria-related genes and the intrinsic chronnectome pattern, with high expression dominantly located in the bi-active hubs (medial/lateral prefrontal and parietal cortices) that exhibit a high level of dynamic features. Consistently, the occupancy rate in the dynamic connectivity state linking most of these regions is associated with a single nucleotide polymorphism component with top related genes associated with metabolism 44 . These regions are essential to mediate various complex cognitive functions 45 and are phylogenetically late developing, with a disproportionate enlargement during evolution 46 .…”