Time-Varying Analyses of Imaging Data: Capturing the Role of Network Dynamics in Psychopathology.
Time-Varying Analyses of Imaging Data: Capturing the Role of Network Dynamics in Psychopathology.
复制标题
成像数据的时变分析：捕捉网络动力学在精神病理学中的作用。
DOI：
10.1016/j.bpsc.2018.01.006
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Posner,Jonathan
中科院分区：
文献类型：
--
作者：
Bernanke,Joel;Wang,Yun;Posner,Jonathan
The triple-network model of cognitive control suggests that abnormal interactions among the default mode network (DMN), salience network (SN), and central executive network (CEN) play a key role in pathophysiology of inattention. Functional magnetic resonance imaging (fMRI) studies have shown that the DMN is active during introspection and mind wandering, while task-positive networks, including the SN and CEN, are active during periods of focused, external attention. Among healthy control subjects, there is evidence of strong anticorrelations between the DMN and task-positive networks (1). However, among individuals with attention-deficit/hyperactivity disorder (ADHD) and other disorders with attentional impairments, weaker anticorrelations are observed (2–4). From these findings, a conceptual model of ADHD has emerged: failure to suppress the DMN when task-positive networks are engaged gives rise to inattention as the introspective function of the DMN encroaches on the externally focused attention of the task-positive networks (1).An important article by Cai et al. uses two independent case-control studies of youth with and without ADHD to examine how time-averaged and dynamic interactions between the DMN, SN, and CEN contribute to symptoms of inattention (5). Building on the concept that the SN mediates interactions between the CEN and DMN, the authors hypothesized that abnormal cross-network interactions would be associated with inattention. And indeed, they found relatively weak cross-network interactions among youth with ADHD relative to healthy control subjects.
