Modeling brain dynamic state changes with adaptive mixture independent component analysis.
Modeling brain dynamic state changes with adaptive mixture independent component analysis.
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DOI：
10.1016/j.neuroimage.2018.08.001
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发表时间：
2018-12
期刊：
影响因子：
5.7
通讯作者：
Jung TP
中科院分区：
文献类型：
--
作者：
Hsu SH;Pion-Tonachini L;Palmer J;Miyakoshi M;Makeig S;Jung TP
在神经科学中，人们对评估支持人类认知和行为流动性的脑网络活动的连续、内源性和非稳态动力学越来越感兴趣。这种非平稳性可能涉及不断变化的形成和溶解的活性皮质源和大脑网络。然而，无监督的方法来识别和模拟这些变化的大脑动力学的准稳定的大脑状态之间的连续过渡，使用未标记的，非侵入性的大脑活动记录一直是有限的。本研究探讨了使用自适应混合独立成分分析（AMICA）建模多通道脑电图（EEG）数据与伊卡模型，其中每个模型分解自适应学习的数据部分统计独立的来源。我们首先表明，AMICA可以分割模拟准平稳EEG数据，并准确地识别地面实况源和源模型转换。接下来，我们证明了AMICA分解，适用于6-13通道头皮记录从CAP睡眠数据库，可以表征睡眠阶段的动态，允许75%的准确性，在识别六个睡眠阶段之间的过渡，而不使用EEG功率谱。最后，应用于30通道数据从主题在驾驶模拟器，AMICA识别模型，占脑电图在更快和更慢的响应驾驶挑战，分别。我们表明，这些模型的相对概率的变化，允许有效的预测受试者的反应速度和每时每刻的表征在单次试验中的状态变化。因此，AMICA提供了一个通用的无监督的方法来识别和建模EEG动态变化。应用于连续的，未标记的多通道数据，AMICA可能被用来检测和研究认知状态的任何变化。
There is a growing interest in neuroscience in assessing the continuous, endogenous, and nonstationary dynamics of brain network activity supporting the fluidity of human cognition and behavior. This non-stationarity may involve ever-changing formation and dissolution of active cortical sources and brain networks. However, unsupervised approaches to identify and model these changes in brain dynamics as continuous transitions between quasi-stable brain states using unlabeled, noninvasive recordings of brain activity have been limited. This study explores the use of adaptive mixture independent component analysis (AMICA) to model multichannel electroencephalographic (EEG) data with a set of ICA models, each of which decomposes an adaptively learned portion of the data into statistically independent sources. We first show that AMICA can segment simulated quasi-stationary EEG data and accurately identify ground-truth sources and source model transitions. Next, we demonstrate that AMICA decomposition, applied to 6–13 channel scalp recordings from the CAP Sleep Database, can characterize sleep stage dynamics, allowing 75% accuracy in identifying transitions between six sleep stages without use of EEG power spectra. Finally, applied to 30-channel data from subjects in a driving simulator, AMICA identifies models that account for EEG during faster and slower response to driving challenges, respectively. We show changes in relative probabilities of these models allow effective prediction of subject response speed and moment-by-moment characterization of state changes within single trials. AMICA thus provides a generic unsupervised approach to identifying and modeling changes in EEG dynamics. Applied to continuous, unlabeled multichannel data, AMICA may likely be used to detect and study any changes in cognitive states.
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