Sang-Cheol Seok, Elizabeth McDevitt, Sara C Mednick, Paola Malerba
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引用次数: 0
Abstract
Sleep slow oscillations (SOs, 0.5-1.5 Hz) are thought to organize activity across cortical and subcortical structures, leading to selective synaptic changes that mediate consolidation of recent memories. Currently, the specific mechanism that allows for this selectively coherent activation across brain regions is not understood. Our previous research has shown that SOs can be classified on the scalp as Global, Local or Frontal, where Global SOs are found in most electrodes within a short time delay and gate long-range information flow during NREM sleep. The functional significance of space-time profiles of SOs hinges on testing if these differential SOs scalp profiles are mirrored by differential depth structure of SOs in the brain. In this study, we built an analytical framework to allow for the characterization of SO depth profiles in space-time across cortical and sub-cortical regions. To test if the two SO types could be differentiated in their cortical-subcortical activity, we trained 30 machine learning classification algorithms to distinguish Global and non-Global SOs within each individual, and repeated this analysis for light (Stage 2, S2) and deep (slow wave sleep, SWS) NREM stages separately. Multiple algorithms reached high performance across all participants, in particular algorithms based on k-nearest neighbors classification principles. Univariate feature ranking and selection showed that the most differentiating features for Global vs. non-Global SOs appeared around the trough of the SO, and in regions including cortex, thalamus, caudate nucleus, and brainstem. Results also indicated that differentiation during S2 required an extended network of current from cortical-subcortical regions, including all regions found in SWS and other basal ganglia regions, and amygdala and hippocampus, suggesting a potential functional differentiation in the role of Global SOs in S2 vs. SWS. We interpret our results as supporting the potential functional difference of Global and non-Global SOs in sleep dynamics.
睡眠慢振荡(SOs,0.5-1.5 Hz)被认为能组织大脑皮层和皮层下结构的活动,导致选择性突触变化,从而介导近期记忆的巩固。目前,人们还不清楚这种跨脑区选择性连贯激活的具体机制。我们之前的研究表明,SOs 在头皮上可分为全局、局部或额叶,其中全局 SOs 在短时间延迟内出现在大多数电极上,并在 NREM 睡眠期间把关长程信息流。SOs时空剖面的功能意义在于测试这些不同的头皮SOs剖面是否反映了大脑中不同深度结构的SOs。在这项研究中,我们建立了一个分析框架,用于描述跨皮层和皮层下区域的SO深度时空剖面。为了测试两种SO类型是否能在皮层-皮层下活动中区分开来,我们训练了30种机器学习分类算法来区分每个人体内的全局和非全局SO,并分别针对轻度(第二阶段,S2)和深度(慢波睡眠,SWS)NREM阶段重复了这一分析。在所有参与者中,多种算法都达到了较高的性能,尤其是基于 k 近邻分类原则的算法。单变量特征排序和选择表明,区分全局性睡眠与非全局性睡眠的最显著特征出现在全局性睡眠的波谷附近,并出现在皮层、丘脑、尾状核和脑干等区域。结果还表明,S2期间的分化需要来自皮层-皮层下区域的扩展电流网络,包括在SWS中发现的所有区域和其他基底节区域,以及杏仁核和海马,这表明全局性SO在S2与SWS中的作用存在潜在的功能差异。我们认为,我们的研究结果支持了全局性和非全局性SO在睡眠动力学中的潜在功能差异。