■静息状态网络(RSN)功能连接分析深刻影响了我们对精神病的病理生理学及其临床高风险(CHR)状态的理解。然而,传统的RSN分析解决了大规模大脑网络的静态性质。相比之下,新的方法论方法旨在评估大脑网络相互作用的动量状态和时间动态。
■50名CHR个体和33名健康对照(HC)完成了静息状态功能MRI扫描。我们进行了能源景观分析,使用成对最大熵模型的数据驱动方法,为了描述大规模的大脑网络动力学,如持续时间和频率,和之间的过渡,不同的大脑状态我们比较了CHR和HC的这些指标,并研究了CHR中神经心理学测量与神经动力学之间的关联。
■我们的主要发现是持续时间显着增加,频率,和更高的过渡到罕见的大脑状态与显著性的共同激活,边缘,与HC相比,CHR中的默认模式和躯体运动RSN。大脑动力学从这种大脑状态的转变与CHR的处理速度显着相关。
■在CHR中,颞叶大脑动力学被一种罕见的大脑状态所吸引,反映默认模式的异常交互更频繁和更长时间的发生,显著性,和边缘网络。同时,更频繁和更长时间发生的大脑状态与核心认知功能障碍有关,未来全面精神病发作的预测因子。
UNASSIGNED: Resting-state network (RSN) functional connectivity analyses have profoundly influenced our understanding of the pathophysiology of psychoses and their clinical high risk (CHR) states. However, conventional RSN analyses address the static nature of large-scale brain networks. In contrast, novel methodological approaches aim to assess the momentum state and temporal dynamics of brain network interactions.
UNASSIGNED: Fifty CHR individuals and 33 healthy controls (HC) completed a resting-state functional MRI scan. We performed an Energy Landscape analysis, a data-driven method using the pairwise maximum entropy model, to describe large-scale brain network dynamics such as duration and frequency of, and transition between, different brain states. We compared those measures between CHR and HC, and examined the association between neuropsychological measures and neural dynamics in CHR.
UNASSIGNED: Our main finding is a significantly increased duration, frequency, and higher transition rates to an infrequent brain state with coactivation of the salience, limbic, default mode and somatomotor RSNs in CHR as compared to HC. Transition of brain dynamics from this brain state was significantly correlated with processing speed in CHR.
UNASSIGNED: In CHR, temporal brain dynamics are attracted to an infrequent brain state, reflecting more frequent and longer occurrence of aberrant interactions of default mode, salience, and limbic networks. Concurrently, more frequent and longer occurrence of the brain state is associated with core cognitive dysfunctions, predictors of future onset of full-blown psychosis.