Large-scale dynamic causal modeling of major depressive disorder based on resting-state functional magnetic resonance imaging
Large-scale dynamic causal modeling of major depressive disorder based on resting-state functional magnetic resonance imaging
复制标题
基于静息状态功能磁共振成像的重度抑郁症大尺度动态因果建模
DOI：
10.1002/hbm.24845
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发表时间：
2020-03-01
影响因子：
4.8
通讯作者：
Shen, Dinggang
中科院分区：
文献类型：
--
作者：
Li, Guoshi;Liu, Yujie;Shen, Dinggang
严重抑郁障碍(MDD)是一种严重的精神疾病，其特征是分布的大脑区域之间的连接功能障碍。以往基于功能磁共振成像(FMRI)的连接组研究主要集中在非定向功能连接，而现有的定向有效连接(EC)研究主要涉及基于任务的fMRI，并且只涉及少数脑区。为了克服这些限制并了解MDD是由网络内还是网络间的连通性介导的，我们应用谱动态因果模型来估计一个大规模网络的EC，该网络包括四个分布式功能脑网络(默认模式、执行控制、显著和边缘网络)中的27个感兴趣区，基于包括100名健康受试者和100名首发药物初发MDD患者的大样本静息态fMRI。我们应用新开发的参数经验贝叶斯(PEB)框架来检验特定的假设。我们发现MDD改变了高阶功能网络内和高阶功能网络之间的EC。具体地说，MDD主要与默认模式网络(DMN)内以及默认模式和显著网络之间的兴奋性连接减少有关。此外，DMN内的网络平均抑制性EC在MDD中被发现显著升高。DMN内兴奋性减少而抑制性增加的因果联系的共存可能是MDD自我认知和情绪控制中断的基础。总体而言，这项研究强调，MDD可能与高阶大脑功能网络之间的因果相互作用改变有关。
Major depressive disorder (MDD) is a serious mental illness characterized by dysfunctional connectivity among distributed brain regions. Previous connectome studies based on functional magnetic resonance imaging (fMRI) have focused primarily on undirected functional connectivity and existing directed effective connectivity (EC) studies concerned mostly task-based fMRI and incorporated only a few brain regions. To overcome these limitations and understand whether MDD is mediated by within-network or between-network connectivities, we applied spectral dynamic causal modeling to estimate EC of a large-scale network with 27 regions of interests from four distributed functional brain networks (default mode, executive control, salience, and limbic networks), based on large sample-size resting-state fMRI consisting of 100 healthy subjects and 100 individuals with first-episode drug-naive MDD. We applied a newly developed parametric empirical Bayes (PEB) framework to test specific hypotheses. We showed that MDD altered EC both within and between high-order functional networks. Specifically, MDD is associated with reduced excitatory connectivity mainly within the default mode network (DMN), and between the default mode and salience networks. In addition, the network-averaged inhibitory EC within the DMN was found to be significantly elevated in the MDD. The coexistence of the reduced excitatory but increased inhibitory causal connections within the DMNs may underlie disrupted self-recognition and emotional control in MDD. Overall, this study emphasizes that MDD could be associated with altered causal interactions among high-order brain functional networks.