Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Schizophr Res. 2018 Nov;201:208-216. doi: 10.1016/j.schres.2018.04.029. Epub 2018 Apr 27.
The functional architecture of resting-state networks (RSNs) is defined by their connectivity and metastability. Disrupted RSN connectivity has been amply demonstrated in schizophrenia while the role of metastability remains poorly defined. Here, we undertake a comprehensive characterisation of RSN organization in schizophrenia and test its contribution to the clinical profile of this disorder.
We extracted RSNs representing the default mode (DMN), central executive (CEN), salience (SAL), language (LAN), sensorimotor (SMN), auditory (AN) and visual (VN) networks from resting-state functional magnetic resonance imaging data obtained from patients with schizophrenia (n = 85) and healthy individuals (n = 48). For each network, we computed its functional cohesiveness and integration and used the Kuramoto order parameter to compute metastability. We used stepwise multiple regression analyses to test these RSN features as predictors of symptom severity in patients.
RSN features respectively explained 14%, 17%, 12% and 5% of the variance in positive, negative, anxious/depressive and agitation/disorganization symptoms. Lower functional integration between the DMN, CEN and SMN primarily contributed to positive symptoms. The functional properties of the SAL network were key predictors of all other symptom dimensions; specifically, lower cohesiveness of the SAL, lower integration of this network with the LAN and higher integration with the CEN respectively contributed to negative, anxious/depressive and disorganization symptoms. Increased SAL metastability was associated with negative symptoms.
These results confirm the primacy of the SAL network for schizophrenia and demonstrate that abnormalities in RSN connectivity and metastability are significant predictors of schizophrenia-related psychopathology.
静息态网络（RSN）的功能结构由其连接性和亚稳性定义。精神分裂症中已充分证明了 RSN 连接的中断，而亚稳性的作用仍未得到明确界定。在这里，我们全面描述了精神分裂症中的 RSN 组织，并测试了其对该疾病临床特征的贡献。
我们从精神分裂症患者（n=85）和健康个体（n=48）的静息态功能磁共振成像数据中提取了代表默认模式（DMN）、中央执行（CEN）、突显（SAL）、语言（LAN）、感觉运动（SMN）、听觉（AN）和视觉（VN）网络的 RSN。对于每个网络，我们计算了其功能内聚性和集成性，并使用了 Kuramoto 序参数来计算亚稳性。我们使用逐步多元回归分析来测试这些 RSN 特征作为患者症状严重程度的预测因子。
RSN 特征分别解释了阳性、阴性、焦虑/抑郁和激越/混乱症状的 14%、17%、12%和 5%的方差。DMN、CEN 和 SMN 之间的功能集成降低主要导致了阳性症状。SAL 网络的功能特性是所有其他症状维度的关键预测因子；具体来说，SAL 网络的内聚性降低、与 LAN 的集成降低以及与 CEN 的集成增加分别对应于阴性、焦虑/抑郁和混乱症状。SAL 亚稳性的增加与阴性症状有关。
这些结果证实了 SAL 网络在精神分裂症中的首要地位，并表明 RSN 连接和亚稳性异常是与精神分裂症相关的精神病理学的重要预测因子。
