Episode-state-associated and shared intrinsic functional network patterns in bipolar disorder.

Understanding functional connectivity alterations across manic, depressive, and remitted states of bipolar disorder (BD) remains an important challenge. In this cross-sectional study, we collected resting-state functional magnetic resonance imaging data from 117 BD patients, including manic BD patients (BipM: n = 38), depressive BD patients (BipD: n = 42), remitted BD patients (rBD: n = 37), and 35 healthy controls. We aimed to identify functional connectivity patterns associated with different BD episode states and shared alterations across groups, and to examine their normative reliability, heritability, and spatial correspondence with molecular and cellular brain maps. We identified shared altered connectivity patterns mainly involving ventral attention network-related regions, together with candidate state-associated patterns involving default-mode-related regions in BipM, frontoparietal/somatomotor/visual regions in BipD, and limbic-related regions in rBD. Using Human Connectome Project data, we further found that subsets of these selected connections showed normative test-retest reliability and heritability in healthy adults. Exploratory machine-learning analyses suggested that these patterns contained information related to BD episode-state classification and symptom variation, although the results require independent external validation. Spatial annotation analyses showed that the regional distribution of these connectivity patterns corresponded with normative neurotransmitter receptor, cell-type, and BD-related gene-expression maps. These findings suggest that BD episode states are associated with partially distinct and partially shared functional connectivity alterations. However, because the study was cross-sectional and single-center, and because the molecular analyses were based on normative spatial maps, the findings should be interpreted as exploratory state-associated network observations rather than causal mechanisms or validated clinical biomarkers.