Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California.
Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California.
Biol Psychiatry. 2019 Jan 1;85(1):60-69. doi: 10.1016/j.biopsych.2018.07.020. Epub 2018 Aug 1.
Schizophrenia is a highly disabling psychiatric disorder characterized by a range of positive "psychosis" symptoms. However, the neurobiology of psychosis and associated systems-level disruptions in the brain remain poorly understood. Here, we test an aberrant saliency model of psychosis, which posits that dysregulated dynamic cross-network interactions among the salience network (SN), central executive network, and default mode network contribute to positive symptoms in patients with schizophrenia.
Using task-free functional magnetic resonance imaging data from two independent cohorts, we examined 1) dynamic time-varying cross-network interactions among the SN, central executive network, and default mode network in 130 patients with schizophrenia versus well-matched control subjects; 2) accuracy of a saliency model-based classifier for distinguishing dynamic brain network interactions in patients versus control subjects; and 3) the relation between SN-centered network dynamics and clinical symptoms.
In both cohorts, we found that dynamic SN-centered cross-network interactions were significantly reduced, less persistent, and more variable in patients with schizophrenia compared with control subjects. Multivariate classification analysis identified dynamic SN-centered cross-network interaction patterns as factors that distinguish patients from control subjects, with accuracies of 78% and 80% in the two cohorts, respectively. Crucially, in both cohorts, dynamic time-varying measures of SN-centered cross-network interactions were correlated with positive, but not negative, symptoms.
Aberrations in time-varying engagement of the SN with the central executive network and default mode network is a clinically relevant neurobiological signature of psychosis in schizophrenia. Our findings provide strong evidence for dysregulated brain dynamics in a triple-network saliency model of schizophrenia and inform theoretically motivated systems neuroscience approaches for characterizing aberrant brain dynamics associated with psychosis.
精神分裂症是一种高度致残的精神障碍，其特征是一系列阳性“精神病”症状。然而，精神病的神经生物学和相关的大脑系统水平紊乱仍知之甚少。在这里，我们测试了一种异常突显模型的精神病，该模型假设，突显网络（SN）、中央执行网络和默认模式网络之间的失调动态跨网络相互作用导致精神分裂症患者的阳性症状。
使用来自两个独立队列的任务型功能磁共振成像数据，我们检查了 1）130 名精神分裂症患者与匹配的对照组之间 SN、中央执行网络和默认模式网络之间的动态时变跨网络相互作用；2）基于突显模型的分类器区分患者与对照者动态脑网络相互作用的准确性；3）SN 为中心的网络动态与临床症状的关系。
在两个队列中，我们发现与对照组相比，精神分裂症患者的 SN 为中心的跨网络相互作用明显减少、持续时间更短、变异性更大。多元分类分析确定了以 SN 为中心的网络动态跨网络相互作用模式作为区分患者与对照者的因素，两个队列的准确率分别为 78%和 80%。至关重要的是，在两个队列中，SN 为中心的跨网络动态时间变化测量值与阳性症状但与阴性症状相关。
SN 与中央执行网络和默认模式网络之间时变参与的异常是精神分裂症精神病的一种具有临床意义的神经生物学特征。我们的发现为精神分裂症三重网络突显模型中大脑动力学的失调提供了强有力的证据，并为描述与精神病相关的异常大脑动力学提供了理论驱动的系统神经科学方法。
