Transition and Dynamic Reconfiguration of Whole-Brain Network in Major Depressive Disorder
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Transition and Dynamic Reconfiguration of Whole-Brain Network in Major Depressive Disorder Shengpei Wang 1,2 & Hongwei Wen 3,4 & Xiaopeng Hu 5 & Peng Xie 6,7,8 & Shuang Qiu 1 & Yinfeng Qian 5 & Jiang Qiu 3,4 & Huiguang He 1,2,9 Received: 1 February 2020 / Accepted: 22 June 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Major depressive disorder (MDD) has been characterized by abnormal brain activity and interactions across the whole-brain functional networks. However, the underlying alteration of brain dynamics remains unclear. Here, we aim to investigate in detail the temporal dynamics of brain activity for MDD, and to characterize the spatiotemporal specificity of whole-brain networks and transitions across them. We developed a hidden Markov model (HMM) analysis for resting-state functional magnetic resonance imaging (fMRI) from two independent cohorts with MDD. In particular, one cohort included 127 MDD patients and 117 genderand age-matched healthy controls, and the other included 44 MDD patients and 33 controls. We identified brain states characterized by the engagement of distinct functional networks that recurred over time and assessed the dynamical configuration of whole-brain networks and the patterns of activation of states that characterized the MDD groups. Furthermore, we analyzed the community structure of transitions across states to investigate the specificity and abnormality of transitions for MDD. Based on our identification of 12 HMM states, we found that the temporal reconfiguration of states in MDD was associated with the highorder cognition network (DMN), subcortical network (SUB), and sensory and motor networks (SMN). Further, we found that the specific module of transitions was closely related to MDD, which were characterized by two HMM states with opposite activations in DMN, SMN, and subcortical areas. Notably, our results provide novel insights into the dynamical circuit configuration of whole-brain networks for MDD and suggest that brain dynamics should remain a prime target for further MDD research. Keywords Resting-state fMRI . Major depressive disorder (MDD) . Hidden Markov model (HMM) . Brain network dynamic . Transition probability
Shengpei Wang, Hongwei Wen and Xiaopeng Hu contributed equally to this work and should be considered as co-first authors. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12035-020-01995-2) contains supplementary material, which is available to authorized users. * Jiang Qiu [email protected] * Huiguang He [email protected] 1
Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
2
University of Chinese Academy of Sciences, Beijing, China
3
Key Laboratory of Cognition and Personality (Ministry of Education), Chongqing, China
4
School of Psychology, Southwest University, Chongqing, China
5
Department of Radiology, the First Affiliated Hospital of Anhui Me
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