本文的第一作者为博士生汪胜佩，题目为“Transition and Dynamic Reconfiguration of Whole-brain Network in Major Depressive Disorder”，被Molecular Neurobiology录用。
以下为文章摘要：
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 gender- and 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 high-order 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.
中文摘要参考：
重度抑郁症（Major depressive disorder, MDD）可由大脑异常活动以及大脑功能网络之间的交互所表征，但是重度抑郁症患者大脑动态特性的变化仍存在许多未知。在本研究中，我们旨在探究重度抑郁症患者的大脑活动在时间上的动态特性，同时表征全脑网络的时空特异性以及大脑网络之间的过度特性。首先，我们在两个独立的静息态功能磁共振成像（Resting-state Functional Magnetic Resonance Imaging，fMRI）数据集上进行了隐马尔科夫模型（Hidden Markov Model, HMM）分析。其中一个数据集包括127名重度抑郁症患者和117名年龄性别相匹配的正常对照，另外一个数据集包括44名重度抑郁症患者和33名年龄性别相匹配的正常对照。其次，我们确定了以不同功能网络为表征的随时间变化而反复出现的大脑微状态，同时评估了重度抑郁症患者的全脑网络的动态组织模式及状态激活模式。最后，我们分析大脑状态转移之间的社区（Community）结构来探究重度抑郁症患者状态转移的特异性及异常性。研究结果表明，基于HMM识别的12个大脑微状态，重度抑郁症患者的以高阶认知网络（Default Mode Network，DMN）、皮层下网络（Subcortical Network，SUB)以及感知运动网络（Sensory and Motor Network，SMN）为表征的大脑微状态存在时间上的重配置。同时，我们发现大脑微状态转移图谱中存在特异的状态转移模块与重度抑郁症患者密切相关，该状态转移模块以DMN、SMN和SUB网络的相反激活的两个大脑微状态所表征。本研究为重度抑郁症的全脑网络的动态配置提供了新的见解，同时表明大脑动力学仍是未来研究重度抑郁症神经的机制相关研究的主要目标。
文章链接：https://link.springer.com/article/10.1007%2Fs12035-020-01995-2