失眠患者静息态脑网络的改变：网络内与网络间的功能连接异常

失眠已成为现代人群中的一种高发健康问题。静息态功能磁共振以其数据采集便利性和无创性, 成为失眠研究的主要成像手段之一。基于近年来静息态功能磁共振的发现, 失眠患者存在前额叶、颞叶、前扣带回、脑岛等认知-情绪神经环路的异常。大尺度脑网络是涵盖多个脑区、功能相对单一的大脑结构。失眠患者存在默认网络、突显网络、认知控制网络和负性情绪网络内部活动与连接异常, 而且呈现出以默认网络为核心, 包含认知控制网络、突显网络、负性情绪网络的网络间连接异常模式。此外, 结合症状、治疗和大尺度脑网络的视角, 可为失眠的“精准治疗”提供神经理论依据。未来研究可结合大数据和多模态分析技术, 验证静息态功能磁共振已有发现。而失眠的纵向追踪和队列研究会有利于进一步阐释失眠的神经机制。

Altered resting-state brain networks in insomnia: Functional connectivities within and between networks

Insomnia has high incidence in modern society. The resting-state functional magnetic resonance (rs-fMRI) becomes one of the main imaging methods for the neuroimaging studies of insomnia, with its convenience and non-intrusive during data recording. Recent rs-fMRI studies showed that patients with insomnia had abnormalities in the prefrontal lobe, the temporal lobe, anterior cingulate gyrus and insula. Large-scale brain network is a brain structure that contains multiple brain regions and has relatively unique cognitive function. Based on the perspective of large-scale brain networks, patients with insomnia had abnormal activities and connectivities within the default network, the salience network, the cognitive control network and the negative affect network. More important, growing evidence presented an altered connectivities pattern among these four large-scale brain networks. Based on the symptoms, therapy, and the patterns of the large-scale brain networks, we proposed a "precision treatment" approach for insomnia. Future researches could integrate the big data with multimodal neuroimaging technology to verify the findings of rs-fMRI. Moreover, longitudinal and sequential design of insomnia can further benefit for the understanding of the neural mechanisms of insomnia.