The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P R China.
School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China.
J Affect Disord. 2021 Jun 15;289:151-159. doi: 10.1016/j.jad.2021.04.029. Epub 2021 Apr 24.
Bipolar disorder is a common psychiatric disorder characterized by insufficient or ineffective connections associated with white-matter (WM) abnormalities. Previous studies have detected the structural attributes of WM using magnetic resonance imaging (MRI) or diffusion tensor imaging, however, they failed to disentangle the dysfunctional organization within the WM.
This study aimed to uncover the WM functional connectivity (FC) in 45 bipolar disorder patients during depressive episodes (BDD) and 45 healthy controls based on resting-state functional MRI. Eight WM functional networks were identified by using a clustering analysis of voxel-based correlation profiles, which were further classified into superficial, middle and deep layers of networks.
Group comparisons on the FCs among 8 WM networks showed that the superficial tempofrontal network (TFN) in BDD patients had increased FC with the superficial cerebellar network (CN) and with the superficial pre/post-central network (PCN). Further, support vector regression prediction analysis results revealed that the increased FCs of CN-TFN and PCN-TFN could be served as features to predict the numbers of depressive episode in BDD patients.
The current study extended our knowledge about the impaired WM functional connections associated with emotional and sensory-motor perception processing in BDD, which may facilitate the interpretation of the pathophysiology mechanisms underlying BDD.
双相情感障碍是一种常见的精神疾病，其特征是与白质（WM）异常相关的连接不足或无效。先前的研究已经使用磁共振成像（MRI）或弥散张量成像检测到 WM 的结构属性，但未能区分 WM 内的功能失调组织。
本研究旨在基于静息态功能磁共振成像，揭示 45 名处于抑郁发作期（BDD）的双相情感障碍患者和 45 名健康对照者的 WM 功能连接（FC）。通过体素相关系数图谱的聚类分析，确定了 8 个 WM 功能网络，并进一步将其分为网络的浅层、中层和深层。
在 8 个 WM 网络之间的 FC 上进行的组间比较表明，BDD 患者的浅层颞额网络（TFN）与浅层小脑网络（CN）和浅层额/后中央网络（PCN）之间的 FC 增加。此外，支持向量回归预测分析结果表明，CN-TFN 和 PCN-TFN 的 FC 增加可以作为特征，用于预测 BDD 患者的抑郁发作次数。
本研究扩展了我们对白质 WM 功能连接与 BDD 中情绪和感觉运动感知处理相关的受损的认识，这可能有助于解释 BDD 潜在的病理生理学机制。
