Background: The interplay between state- and trait-related disruptions in structural networks remains unclear in bipolar disorder (BD), but graph theory can offer insights into global and local network changes. We sought to use diffusion-tensor imaging (DTI) and graph theory approaches to analyze structural topological properties across distinct mood states and identify high-risk individuals by examining state- and trait-related impairments in BD.
Methods: We studied changes in white matter network among patients with BD and healthy controls, exploring relationships with clinical variables. Secondary analysis involved comparing patients with BD with unaffected people at high genetic risk for BD.
Results: We included 152 patients with BD, including 52 with depressive BD (DBD), 64 with euthymic BD (EBD) and 36 with manic BD (MBD); we also included 75 healthy controls. Secondary analyses involved 27 unaffected people at high genetic risk for BD. Patients with DBD and MBD exhibited significantly lower global efficiencies than those with EBD and healthy controls, with patients with DBD showing the lowest global efficiencies. In addition, patients with DBD displayed impaired local efficiency and normalized clustering coefficient (γ). At a global level, γ correlated negatively with depression and anxiety. Compared with healthy controls, and across mood states, patients with BD showed abnormal shortest path lengths in the frontolimbic circuit, a trend mirrored among those at high genetic risk for BD.
Limitations: Considerations include medication effects, absence of recorded BD episode counts and the cross-sectional nature of the study.
Conclusion: Mood-specific whole-brain network metrics could serve as potential biomarkers in BD for transitions between mood states. Moreover, these findings contribute to evidence of trait-related frontolimbic circuit irregularities, shedding light on underlying pathophysiological mechanisms in BD.
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