Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, India.
Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany.
Hum Brain Mapp. 2020 Jun 15;41(9):2334-2346. doi: 10.1002/hbm.24949. Epub 2020 Feb 24.
Electroencephalogram (EEG) microstates that represent quasi-stable, global neuronal activity are considered as the building blocks of brain dynamics. Therefore, the analysis of microstate sequences is a promising approach to understand fast brain dynamics that underlie various mental processes. Recent studies suggest that EEG microstate sequences are non-Markovian and nonstationary, highlighting the importance of the sequential flow of information between different brain states. These findings inspired us to model these sequences using Recurrent Neural Networks (RNNs) consisting of long-short-term-memory (LSTM) units to capture the complex temporal dependencies. Using an LSTM-based auto encoder framework and different encoding schemes, we modeled the microstate sequences at multiple time scales (200-2,000 ms) aiming to capture stably recurring microstate patterns within and across subjects. We show that RNNs can learn underlying microstate patterns with high accuracy and that the microstate trajectories are subject invariant at shorter time scales (≤400 ms) and reproducible across sessions. Significant drop in the reconstruction accuracy was observed for longer sequence lengths of 2,000 ms. These findings indirectly corroborate earlier studies which indicated that EEG microstate sequences exhibit long-range dependencies with finite memory content. Furthermore, we find that the latent representations learned by the RNNs are sensitive to external stimulation such as stress while the conventional univariate microstate measures (e.g., occurrence, mean duration, etc.) fail to capture such changes in brain dynamics. While RNNs cannot be configured to identify the specific discriminating patterns, they have the potential for learning the underlying temporal dynamics and are sensitive to sequence aberrations characterized by changes in metal processes. Empowered with the macroscopic understanding of the temporal dynamics that extends beyond short-term interactions, RNNs offer a reliable alternative for exploring system level brain dynamics using EEG microstate sequences.
脑电图（EEG）微状态代表准稳定的、全局神经元活动，被认为是大脑动力学的构建块。因此，微状态序列的分析是理解各种心理过程背后快速大脑动力学的一种很有前途的方法。最近的研究表明，EEG 微状态序列是非马尔可夫和非平稳的，这突出了不同脑状态之间信息序列流的重要性。这些发现启发我们使用由长短期记忆（LSTM）单元组成的递归神经网络（RNN）来对这些序列进行建模，以捕捉复杂的时间依赖性。使用基于 LSTM 的自动编码器框架和不同的编码方案，我们在多个时间尺度（200-2000ms）上对微状态序列进行建模，旨在捕捉个体内和个体间稳定出现的微状态模式。我们表明，RNN 可以高精度地学习潜在的微状态模式，并且微状态轨迹在较短的时间尺度（≤400ms）上是不变的，并且在不同的会话中是可重复的。对于较长的序列长度 2000ms，观察到重建准确性显著下降。这些发现间接证实了早期的研究，表明 EEG 微状态序列表现出具有有限记忆内容的长程依赖性。此外，我们发现 RNN 学习的潜在表示对外部刺激（如压力）敏感，而传统的单变量微状态测量（例如，出现、平均持续时间等）无法捕捉到大脑动力学的这种变化。虽然 RNN 不能配置为识别特定的区分模式，但它们具有学习潜在时间动态的潜力，并且对以金属过程变化为特征的序列异常敏感。借助于对超出短期相互作用的时间动态的宏观理解，RNN 为使用 EEG 微状态序列探索系统级大脑动力学提供了可靠的替代方案。
