Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia

Abstract

The brain's default mode network (DMN) is highly heritable and is compromised in a variety of psychiatric disorders. However, genetic control over the DMN in schizophrenia (SZ) and psychotic bipolar disorder (PBP) is largely unknown. Study subjects (n = 1,305) underwent a resting-state functional MRI scan and were analyzed by a two-stage approach. The initial analysis used independent component analysis (ICA) in 324 healthy controls, 296 SZ probands, 300 PBP probands, 179 unaffected first-degree relatives of SZ probands (SZREL), and 206 unaffected first-degree relatives of PBP probands to identify DMNs and to test their biomarker and/or endophenotype status. A subset of controls and probands (n = 549) then was subjected to a parallel ICA (para-ICA) to identify imaging-genetic relationships. ICA identified three DMNs. Hypo-connectivity was observed in both patient groups in all DMNs. Similar patterns observed in SZREL were restricted to only one network. DMN connectivity also correlated with several symptom measures. Para-ICA identified five sub-DMNs that were significantly associated with five different genetic networks. Several top-ranking SNPs across these networks belonged to previously identified, well-known psychosis/mood disorder genes. Global enrichment analyses revealed processes including NMDA-related long-term potentiation, PKA, immune response signaling, axon guidance, and synaptogenesis that significantly influenced DMN modulation in psychoses. In summary, we observed both unique and shared impairments in functional connectivity across the SZ and PBP cohorts; these impairments were selectively familial only for SZREL. Genes regulating specific neurodevelopment/transmission processes primarily mediated DMN disconnectivity. The study thus identifies biological pathways related to a widely researched quantitative trait that might suggest novel, targeted drug treatments for these diseases.

Keywords: BSNIP; architecture; genetics; molecular.

Fig. 1.

Overall workflow of the study describing the steps in processing both the imaging and genetic data leading to para-ICA analysis.

Fig. 2.

(Left) Spatial topology of ICA-derived DMNs identified in the study thresholded at Z > 2. (Center) Between-group voxelwise differences within DMNs derived from Randomize-TFCE (FWE <0.05). (Right) Bar graphs show average connectivity values across groups in clusters identified in the center panel as being significantly different among groups.

Fig. 3.

The five default mode subnetworks and their associated genetic components as identified by para-ICA. Each genetic component is represented by the top 10 genes plus genes that feature with more than three SNPs within each network. Also identified are select significantly enriched ontology term(s) (FDR < 0.05) within each genetic cluster. Arrows pointing up and down indicate whether the loading coefficient for that particular feature (fMRI or gene) was significantly higher or lower for probands than for controls.

Fig. 4.

Significant ontology terms derived from a pooled analysis representing genes identified in our study in a variety of processes/pathways/networks whose overrepresentation suggests that they mediate the risk of psychosis via default mode connectivity in probands.