Age attenuates noise and increases symmetry of head movements during sleep resting-state fMRI in healthy neonates,infants, and toddlers

Newborns produce spontaneous movements during sleep that are functionally important for their future development. This nuance has been previously studied using animal models and more recently using movement data from sleep resting-state fMRI (rs-fMRI) scans. Age-related trajectory of statistical features of spontaneous movements of the head is under-examined. This study quantitatively mapped a developmental trajectory of spontaneous head movements during an rs-fMRI scan acquired during natural sleep in 91 datasets from healthy children from ∼birth to 3 years old, using the Open Science Infancy Research upcycling protocol. The youngest participants studied, 2–3 week-old neonates, showed increased noise-to-signal levels as well as lower symmetry features of their movements; noise-to-signal levels were attenuated and symmetry was increased in the older infants and toddlers (all Spearman's rank-order correlations, P < 0.05). Thus, statistical features of spontaneous head movements become more symmetrical and less noisy from birth to ∼3 years in children. Because spontaneous movements during sleep in early life may trigger new neuronal activity in the cortex, the key outstanding question for in vivo, non-invasive neuroimaging studies in young children is not “How can we correct head movement better?” but rather: How can we represent all important sources of neuronal activity that shape functional connections in the still-developing human central nervous system?     

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。