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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Psychiatry | doi: 10.3389/fpsyt.2019.00788

Abnormal Connectivity within Anterior Cortical Midline Structures in Bipolar Disorder: Evidence from Integrated MRI and Functional MRI

 Jie Yang1*,  Weidan Pu1, Xuan Ouyang1, Xudong Chen1, Xiaojun Huang1 and Zhening Liu1
  • 1Second Xiangya Hospital, Central South University, China

Abstract
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 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 were 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 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 anterior midline brain in the pathophysiology of BD, and this decoupling may serve as a trait-marker for this disease.

Keywords: Bipolar Disorder, functional connectivity, structural connectivity, machine learning, multimodal fusion

Received: 29 Jun 2019; Accepted: 03 Oct 2019.

Copyright: © 2019 Yang, Pu, Ouyang, Chen, Huang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Jie Yang, Second Xiangya Hospital, Central South University, Changsha, China, yang0826@csu.edu.cn