Hart Brian, Malone Stephen, Fiecas Mark
Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, U.S.A.
Department of Psychology, University of Minnesota, Minneapolis, MN 55455, U.S.A.
Can J Stat. 2021 Mar;49(1):89-106. doi: 10.1002/cjs.11589. Epub 2021 Feb 18.
EEG microstate analysis investigates the collection of distinct temporal blocks that characterize the electrical activity of the brain. Brain activity within each microstate is stable, but activity switches rapidly between different microstates in a nonrandom way. We propose a Bayesian nonparametric model that concurrently estimates the number of microstates and their underlying behaviour. We use a Markov switching vector autoregressive (VAR) framework, where a hidden Markov model (HMM) controls the nonrandom state switching dynamics of the EEG activity and a VAR model defines the behaviour of all time points within a given state. We analyze the resting-state EEG data from twin pairs collected through the Minnesota Twin Family Study, consisting of 70 epochs per participant, where each epoch corresponds to 2 s of EEG data. We fit our model at the twin pair level, sharing information within epochs from the same participant and within epochs from the same twin pair. We capture within twin-pair similarity, using an Indian buffet process, to consider an infinite library of microstates, allowing each participant to select a finite number of states from this library. The state spaces of highly similar twins may completely overlap while dissimilar twins could select distinct state spaces. In this way, our Bayesian nonparametric model defines a sparse set of states that describe the EEG data. All epochs from a single participant use the same set of states and are assumed to adhere to the same state switching dynamics in the HMM model, enforcing within-participant similarity.
脑电图微状态分析研究表征大脑电活动的不同时间片段的集合。每个微状态内的大脑活动是稳定的，但活动以非随机的方式在不同微状态之间快速切换。我们提出了一种贝叶斯非参数模型，该模型同时估计微状态的数量及其潜在行为。我们使用马尔可夫切换向量自回归（VAR）框架，其中隐马尔可夫模型（HMM）控制脑电图活动的非随机状态切换动态，而VAR模型定义给定状态内所有时间点的行为。我们分析了通过明尼苏达双胞胎家庭研究收集的双胞胎对的静息态脑电图数据，每个参与者有70个时段，每个时段对应2秒的脑电图数据。我们在双胞胎对层面拟合我们的模型，在来自同一参与者的时段内以及来自同一双胞胎对的时段内共享信息。我们使用印度自助餐过程捕捉双胞胎对内部的相似性，以考虑一个无限的微状态库，允许每个参与者从这个库中选择有限数量的状态。高度相似的双胞胎的状态空间可能完全重叠，而不相似的双胞胎可能选择不同的状态空间。通过这种方式，我们的贝叶斯非参数模型定义了一组稀疏的状态来描述脑电图数据。来自单个参与者的所有时段使用相同的状态集，并假设在HMM模型中遵循相同的状态切换动态，从而加强参与者内部的相似性。
