Combined static and dynamic functional connectivity signatures differentiating bipolar depression from major depressive disorder.

Abstract

Bipolar disorder in the depressive phase (BDd) may be misdiagnosed as major depressive disorder (MDD), resulting in poor treatment outcomes. To identify biomarkers distinguishing BDd from MDD is of substantial clinical significance. This study aimed to characterize specific alterations in intrinsic functional connectivity (FC) patterns in BDd and MDD by combining whole-brain static and dynamic FC. A total of 40 MDD and 38 BDd patients, and 50 age-, sex-, education-, and handedness-matched healthy controls (HCs) were included in this study. Static and dynamic FC strengths (FCSs) were analyzed using complete time-series correlations and sliding window correlations, respectively. One-way analysis of variance was performed to test group effects. The combined static and dynamic FCSs were then used to distinguish BDd from MDD and to predict clinical symptom severity. Compared with HCs, BDd patients showed lower static FCS in the medial orbitofrontal cortex and greater static FCS in the caudate, while MDD patients exhibited greater static FCS in the medial orbitofrontal cortex. BDd patients also demonstrated greater static and dynamic FCSs in the thalamus compared with both MDD patients and HCs, while MDD patients exhibited greater dynamic FCS in the precentral gyrus compared with both BDd patients and HCs. Combined static and dynamic FCSs yielded higher accuracy than either static or dynamic FCS analysis alone, and also predicted anhedonia severity in BDd patients and negative mood severity in MDD patients. Altered FC within frontal-striatal-thalamic circuits of BDd patients and within the default mode network/sensorimotor network of MDD patients accurately distinguishes between these disorders. These unique FC patterns may serve as biomarkers for differential diagnosis and provide clues to the pathogenesis of mood disorders.