Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.
Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States.
Front Neural Circuits. 2021 Mar 18;15:649417. doi: 10.3389/fncir.2021.649417. eCollection 2021.
Schizophrenia affects around 1% of the global population. Functional connectivity extracted from resting-state functional magnetic resonance imaging (rs-fMRI) has previously been used to study schizophrenia and has great potential to provide novel insights into the disorder. Some studies have shown abnormal functional connectivity in the default mode network (DMN) of individuals with schizophrenia, and more recent studies have shown abnormal dynamic functional connectivity (dFC) in individuals with schizophrenia. However, DMN dFC and the link between abnormal DMN dFC and symptom severity have not been well-characterized. Resting-state fMRI data from subjects with schizophrenia (SZ) and healthy controls (HC) across two datasets were analyzed independently. We captured seven maximally independent subnodes in the DMN by applying group independent component analysis and estimated dFC between subnode time courses using a sliding window approach. A clustering method separated the dFCs into five reoccurring brain states. A feature selection method modeled the difference between SZs and HCs using the state-specific FC features. Finally, we used the transition probability of a hidden Markov model to characterize the link between symptom severity and dFC in SZ subjects. We found decreases in the connectivity of the anterior cingulate cortex (ACC) and increases in the connectivity between the precuneus (PCu) and the posterior cingulate cortex (PCC) (i.e., PCu/PCC) of SZ subjects. In SZ, the transition probability from a state with weaker PCu/PCC and stronger ACC connectivity to a state with stronger PCu/PCC and weaker ACC connectivity increased with symptom severity. To our knowledge, this was the first study to investigate DMN dFC and its link to schizophrenia symptom severity. We identified reproducible neural states in a data-driven manner and demonstrated that the strength of connectivity within those states differed between SZs and HCs. Additionally, we identified a relationship between SZ symptom severity and the dynamics of DMN functional connectivity. We validated our results across two datasets. These results support the potential of dFC for use as a biomarker of schizophrenia and shed new light upon the relationship between schizophrenia and DMN dynamics.
精神分裂症影响全球约 1%的人口。先前使用静息态功能磁共振成像（rs-fMRI）提取的功能连接来研究精神分裂症，并且具有提供对该疾病的新见解的巨大潜力。一些研究表明精神分裂症个体的默认模式网络（DMN）中存在功能连接异常，而最近的研究表明精神分裂症个体中存在动态功能连接（dFC）异常。然而，DMN dFC 以及异常 DMN dFC 与症状严重程度之间的联系尚未得到很好的描述。 对来自两个数据集的精神分裂症（SZ）和健康对照（HC）受试者的静息态 fMRI 数据进行了独立分析。我们通过应用组独立成分分析捕获了 DMN 中的七个最大独立子节点，并使用滑动窗口方法估计了子节点时间序列之间的 dFC。聚类方法将 dFC 分为五个重复出现的脑状态。特征选择方法使用状态特异性 FC 特征来模拟 SZ 和 HC 之间的差异。最后，我们使用隐马尔可夫模型的转移概率来描述 SZ 受试者中症状严重程度与 dFC 之间的联系。 我们发现 SZ 受试者的前扣带皮层（ACC）连接减少，而楔前叶（PCu）与后扣带皮层（PCC）之间的连接增加（即 PCu/PCC）。在 SZ 中，从一个具有较弱 PCu/PCC 和较强 ACC 连接的状态向一个具有较强 PCu/PCC 和较弱 ACC 连接的状态的转移概率随着症状严重程度的增加而增加。 据我们所知，这是第一项研究 DMN dFC 及其与精神分裂症症状严重程度的关系。我们以数据驱动的方式识别出可重复的神经状态，并证明了这些状态内的连接强度在 SZ 和 HC 之间存在差异。此外，我们确定了 SZ 症状严重程度与 DMN 功能连接动力学之间的关系。我们在两个数据集上验证了我们的结果。这些结果支持 dFC 作为精神分裂症生物标志物的潜力，并为精神分裂症与 DMN 动力学之间的关系提供了新的认识。
