Fengmei Lu1, Qian Cui2, Zongling He1, Qin Tang1, Yuyan Chen1, Wei Sheng1, Yang Yang1, Wei Luo1, Yue Yu1, Jiajia Chen1, Di Li1, Jiaxin Deng3, Shan Hu3, Huafu Chen4. 1. 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. 2. School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China. Electronic address: qiancui26@gmail.com. 3. School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China. 4. 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; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, P R China. Electronic address: chenhf@uestc.edu.cn.
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: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. METHODS: This study aimed to uncover the WM functional connectivity (FC) in 45 bipolar disorderpatients 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. RESULTS: Group comparisons on the FCs among 8 WM networks showed that the superficial tempofrontal network (TFN) in BDDpatients 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 BDDpatients. CONCLUSIONS: 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.