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In Frontiers in psychiatry ; h5-index 0.0

Background: Aberrant functional and structural connectivity across multiple brain networks have been reported in bipolar disorder (BD). However, most previous studies consider the functional and structural alterations in isolation regardless of their possible integrative relationship. The present study aimed to identify the brain connectivity alterations in BD by capturing the latent nexus in multimodal neuroimaging data. Methods: Structural and resting-state images were acquired from 83 patients with BD and 94 healthy controls (HCs). Combined with univariate methods conducted to detect the dysconnectivity in BD, we also employed a semi-multimodal fusion framework fully utilizing the interrelationship between the two modalities to distinguish patients from HCs. Moreover, one-way analysis of variance was adopted to explore whether the detected dysconnectivity has differences across stages of patients with BD. Results: The semi-multimodal fusion framework distinguished patients from HCs with 81.47% accuracy, 85.42% specificity, and 74.75% sensitivity. The connection between the anterior cingulate cortex (ACC) and superior medial prefrontal cortex (sMPFC) contributed the most to BD diagnosis. Consistently, the univariate method also identified that this ACC-sMPFC functional connection significantly decreased in BD patients compared to HCs, and the significant order of the dysconnectivity is: depressive episode < HCs and remission episode < HCs. Conclusions: Our findings, by adopting univariate and multivariate analysis methods, shed light on the decoupling within the anterior midline brain in the pathophysiology of BD, and this decoupling may serve as a trait marker for this disease.

Yang Jie, Pu Weidan, Ouyang Xuan, Tao Haojuan, Chen Xudong, Huang Xiaojun, Liu Zhening


bipolar disorder, functional connectivity, machine learning, multimodal fusion, structural connectivity