Switching state-space modeling of neural signal dynamics.
Switching state-space modeling of neural signal dynamics.
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DOI：
10.1371/journal.pcbi.1011395
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发表时间：
2023-08
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
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线性参数状态空间模型是分析神经时间序列数据的普遍工具，提供了一种比非参数数据分析方法更高的统计效率来表征潜在的大脑动力学的方法。然而，神经时间序列数据经常是时变的，表现出动态的快速变化，瞬态活动通常是数据中感兴趣的关键特征。平稳方法可以适应时变的情况下，采用固定持续时间的窗口下的假设准平稳。但是，时变动态可以通过切换状态空间模型来明确建模，即，通过使用具有由概率切换过程选择的不同动态的状态空间模型的池。不幸的是，切换状态空间模型的状态推断和参数学习的精确解是棘手的。在这里，我们重新审视切换状态空间模型推理方法首先提出的Ghahramani和欣顿。我们提供了明确的推导后，应用变分近似联合后的隐藏状态和切换过程迭代求解的推理问题。我们引入了一种新的初始化过程，使用有效的留一策略来比较候选模型，与依赖于确定性退火的现有方法相比，该方法显着提高了性能。然后，我们利用这种状态推断解决方案内的广义期望最大化算法来估计模型参数的切换过程和线性状态空间模型的动态潜在的候选模型之间共享。我们在不同的设置下进行了大量的模拟，以基准性能对现有的开关推理方法，并进一步验证我们的开关推理解决方案的生成开关模型类之外的鲁棒性。最后，我们证明了实用的睡眠纺锤波检测方法在真实的记录，显示如何切换状态空间模型可以用来检测和提取瞬态纺锤波从人类睡眠脑电图在无监督的方式。大脑活动的一个固有方面是它随时间而变化，但用于分析神经科学数据的现有方法通常假设潜在活动是严格静止的，即，该活动的属性不会随时间而改变。处理时变数据的一种方法是将数据分解为假定为准静态的较小片段，但这种方法仅在信号逐渐变化时有效，并且在变化迅速或目标活动本质上是瞬时的时往往表现不佳。一类称为线性切换状态空间模型的模型可以显式地表示时变活动，但它们带来了另一组挑战：此类模型的精确解是棘手的，并且现有的近似解可能非常不准确。在这项工作中，我们提出了一种解决方案，线性切换状态空间模型，能够恢复潜在的隐藏状态和模型参数的时变动态的方式是强大的模型误指定，并优于以前提出的方法。我们证明了我们的方法的实用性，将其应用到睡眠纺锤波检测的问题，并表明切换状态空间模型可以自动检测人类睡眠脑电图的瞬时纺锤波活动。
Linear parametric state-space models are a ubiquitous tool for analyzing neural time series data, providing a way to characterize the underlying brain dynamics with much greater statistical efficiency than non-parametric data analysis approaches. However, neural time series data are frequently time-varying, exhibiting rapid changes in dynamics, with transient activity that is often the key feature of interest in the data. Stationary methods can be adapted to time-varying scenarios by employing fixed-duration windows under an assumption of quasi-stationarity. But time-varying dynamics can be explicitly modeled by switching state-space models, i.e., by using a pool of state-space models with different dynamics selected by a probabilistic switching process. Unfortunately, exact solutions for state inference and parameter learning with switching state-space models are intractable. Here we revisit a switching state-space model inference approach first proposed by Ghahramani and Hinton. We provide explicit derivations for solving the inference problem iteratively after applying a variational approximation on the joint posterior of the hidden states and the switching process. We introduce a novel initialization procedure using an efficient leave-one-out strategy to compare among candidate models, which significantly improves performance compared to the existing method that relies on deterministic annealing. We then utilize this state inference solution within a generalized expectation-maximization algorithm to estimate model parameters of the switching process and the linear state-space models with dynamics potentially shared among candidate models. We perform extensive simulations under different settings to benchmark performance against existing switching inference methods and further validate the robustness of our switching inference solution outside the generative switching model class. Finally, we demonstrate the utility of our method for sleep spindle detection in real recordings, showing how switching state-space models can be used to detect and extract transient spindles from human sleep electroencephalograms in an unsupervised manner. An inherent aspect of brain activity is that it changes over time, but existing methods for analyzing neuroscience data typically assume that the underlying activity is strictly stationary, i.e., the properties of that activity do not change over time. One way of handling time-varying data is to break the data into smaller segments that one assumes to be quasi-stationary, but this approach only works if signals vary gradually, and tends to perform poorly when changes are rapid or the target activity is transient in nature. A class of models called linear switching state-space models can explicitly represent time-varying activity, but they pose another set of challenges: exact solutions for such models are intractable, and existing approximate solutions can be highly inaccurate. In this work we present a solution for linear switching state-space models that is able to recover the underlying hidden states and model parameters for time-varying dynamics in a way that is robust to model mis-specification and that outperforms previously proposed methods. We demonstrate the utility of our method by applying it to the problem of sleep spindle detection and show that switching state-space models can automatically detect transient spindle activity from human sleep electroencephalograms.
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影响因子：
5.6
作者：
da Silva, FL;Blanes, W;Velis, DN
通讯作者：
Velis, DN
影响因子：
4.6
作者：
Arnold, M;Miltner, WHR;Braun, C
通讯作者：
Braun, C
影响因子：
4.6
作者：
Aboy, M;Márquez, OW;Goldstein, B
通讯作者：
Goldstein, B
DOI：
10.1111/j.2517-6161.1977.tb01600.x
发表时间：
1977-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL
影响因子：
--
作者：
DEMPSTER, AP;LAIRD, NM;RUBIN, DB
通讯作者：
RUBIN, DB
影响因子：
3.7
作者：
DEJONG, P
通讯作者：
DEJONG, P