PDF(Pubmed)
Abstract：
Brain function is vulnerable to the consequences of inadequate sleep, an adverse trend that is increasingly prevalent. The REM sleep phase has been implicated in coordinating various brain structures and is hypothesized to have potential links to brain variability. However, traditional imaging research have encountered challenges in attributing specific brain region activity to REM sleep, remained understudied at the whole-brain connectivity level. Through the spilt-night paradigm, distinct patterns of REM sleep phases were observed among the full-night sleep group (n = 36), the early-night deprivation group (n = 41), and the late-night deprivation group (n = 36). We employed connectome-based predictive modeling (CPM) to delineate the effects of REM sleep deprivation on the functional connectivity of the brain (REM connectome) during its resting state. The REM sleep-brain connectome was characterized by stronger connectivity within the default mode network (DMN) and between the DMN and visual networks, while fewer predictive edges were observed. Notably, connections such as those between the cingulo-opercular network (CON) and the auditory network, as well as between the subcortex and visual networks, also made significant contributions. These findings elucidate the neural signatures of REM sleep loss and reveal common connectivity patterns across individuals, validated at the group level.
摘要：
大脑功能容易受到睡眠不足的影响，越来越普遍的不利趋势。REM睡眠阶段涉及协调各种大脑结构，并被认为与大脑变异性有潜在联系。然而,传统的成像研究在将特定的大脑区域活动归因于REM睡眠方面遇到了挑战，在全脑连接水平上仍未得到充分研究。通过溢出的夜晚范例，在全夜睡眠组中观察到不同的REM睡眠阶段模式(n=36)，早晚剥夺组(n=41)，和深夜剥夺组(n=36)。我们采用基于连接体的预测模型（CPM）来描述REM睡眠剥夺对静息状态下大脑（REM连接体）功能连接的影响。REM睡眠-大脑连接体的特点是在默认模式网络（DMN）内以及DMN和视觉网络之间具有更强的连接性，而观察到的预测边缘较少。值得注意的是,连接，例如扣带操作网络(CON)和听觉网络之间的连接，以及在皮层下和视觉网络之间，也做出了重大贡献。这些发现阐明了REM睡眠不足的神经特征，并揭示了个体之间常见的连接模式。在组级别验证。
