Interest in time-resolved connectivity in fMRI has grown rapidly in recent years. The most widely used technique for studying connectivity changes over time utilizes a sliding windows approach. There has been some debate about the utility of shorter versus longer windows, the use of fixed versus adaptive windows, as well as whether observed resting state dynamics during wakefulness may be predominantly due to changes in sleep state and subject head motion. In this work we use an independent component analysis (ICA)-based pipeline applied to concurrent EEG/fMRI data collected during wakefulness and various sleep stages and show: 1) connectivity states obtained from clustering sliding windowed correlations of resting state functional network time courses well classify the sleep states obtained from EEG data, 2) using shorter sliding windows instead of longer non-overlapping windows improves the ability to capture transition dynamics even at windows as short as 30 seconds, 3) motion appears to be mostly associated with one of the states rather than spread across all of them 4) a fixed tapered sliding window approach outperforms an adaptive dynamic conditional correlation approach, and 5) consistent with prior EEG/fMRI work, we identify evidence of multiple states within the wakeful condition which are able to be classified with high accuracy. Classification of wakeful only states suggest the presence of time-varying changes in connectivity in fMRI data beyond sleep state or motion. Results also inform about advantageous technical choices, and the identification of different clusters within wakefulness that are separable suggest further studies in this direction.

中文翻译：

---

从清醒到睡眠的连接动态

近年来，人们对 fMRI 中时间分辨连通性的兴趣迅速增长。研究连接性随时间变化的最广泛使用的技术是使用滑动窗口方法。关于较短窗口与较长窗口的效用、固定窗口与自适应窗口的使用，以及在清醒期间观察到的静息状态动态是否可能主要是由于睡眠状态和受试者头部运动的变化，存在一些争论。在这项工作中，我们使用基于独立组件分析 (ICA) 的管道应用于在清醒和各种睡眠阶段收集的并发 EEG/fMRI 数据，并显示：1）从静息状态功能网络时间课程的滑动窗口相关性聚类中获得的连接状态对从 EEG 数据中获得的睡眠状态进行分类，2) 使用较短的滑动窗口而不是较长的非重叠窗口提高了捕捉过渡动态的能力，即使窗口短至 30 秒，3) 运动似乎主要与其中一种状态相关，而不是分散在所有状态中 4 ) 固定锥形滑动窗口方法优于自适应动态条件相关方法，并且 5) 与先前的 EEG/fMRI 工作一致，我们确定了清醒条件下多个状态的证据，这些状态能够以高精度进行分类。仅清醒状态的分类表明，在睡眠状态或运动之外的 fMRI 数据中存在随时间变化的连接性变化。结果还告知有利的技术选择，