Reconstructing mammalian sleep dynamics with data assimilation.
Reconstructing mammalian sleep dynamics with data assimilation.
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DOI：
10.1371/journal.pcbi.1002788
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发表时间：
2012
影响因子：
4.3
通讯作者：
Gluckman BJ
中科院分区：
文献类型：
--
作者：
Sedigh-Sarvestani M;Schiff SJ;Gluckman BJ
数据同化是一个有价值的工具，在任何复杂的系统，测量是不完整的，不确定的，或两者兼而有之的研究。它使用户能够利用所有可用的信息，包括系统的实验测量和短期模型预测。虽然数据同化已被用于研究其他生物系统，但睡眠-觉醒调节网络的研究尚未受益于此工具集。我们提出了一个数据同化框架的基础上的无迹卡尔曼滤波器（UKF）结合稀疏测量与一个相对高维的非线性计算模型，估计状态的睡眠-觉醒调节系统的模型。我们证明了与模拟研究，一些嘈杂的变量可以用来准确地重建其余的隐藏变量。我们引入了一个度量排名的计算模型的相对部分可观测性，在UKF框架内，使我们能够选择最佳的测量变量，也提供了一种方法来优化框架参数，如UKF协方差膨胀。此外，我们展示了一种参数估计方法，使我们能够跟踪非平稳模型参数，并适应不包括在UKF滤波器模型中的缓慢动态。最后，我们表明，我们甚至可以使用观察到的离散睡眠状态，这不是模型变量之一，重建模型状态和估计未知参数。睡眠与从癫痫到精神分裂症的许多神经系统疾病有关，但同时观察调节这种行为的许多大脑成分是困难的。我们预计，这种数据同化框架将使我们能够更好地了解控制睡眠和清醒行为的详细相互作用，并提供更好、更有针对性的治疗方法。开发数学模型是为了更好地理解系统组件之间的相互作用，这些组件共同控制整体行为。睡眠的数学模型有助于阐明参与促进睡眠和觉醒行为的神经元细胞群以及它们之间的过渡。然而，为了能够充分利用这些模型，必须能够准确地估计所有变量的值。数据同化是指允许用户将联合收割机对几个系统变量的噪声测量与该系统的数学模型相结合，以估计所有变量，包括那些最初无法测量的变量。使用这些技术，我们表明，我们可以重建未测量的变量和参数的睡眠-觉醒网络的数学模型。然后，这些重建的估计值可以用于更好地理解导致睡眠和唤醒活动的潜在神经元行为。由于睡眠与从癫痫到精神分裂症的各种神经系统疾病有关，我们预计这一框架将有助于更好地理解睡眠与大脑其他部分之间的联系，并提供更好，更有针对性的治疗方法。
Data assimilation is a valuable tool in the study of any complex system, where measurements are incomplete, uncertain, or both. It enables the user to take advantage of all available information including experimental measurements and short-term model forecasts of a system. Although data assimilation has been used to study other biological systems, the study of the sleep-wake regulatory network has yet to benefit from this toolset. We present a data assimilation framework based on the unscented Kalman filter (UKF) for combining sparse measurements together with a relatively high-dimensional nonlinear computational model to estimate the state of a model of the sleep-wake regulatory system. We demonstrate with simulation studies that a few noisy variables can be used to accurately reconstruct the remaining hidden variables. We introduce a metric for ranking relative partial observability of computational models, within the UKF framework, that allows us to choose the optimal variables for measurement and also provides a methodology for optimizing framework parameters such as UKF covariance inflation. In addition, we demonstrate a parameter estimation method that allows us to track non-stationary model parameters and accommodate slow dynamics not included in the UKF filter model. Finally, we show that we can even use observed discretized sleep-state, which is not one of the model variables, to reconstruct model state and estimate unknown parameters. Sleep is implicated in many neurological disorders from epilepsy to schizophrenia, but simultaneous observation of the many brain components that regulate this behavior is difficult. We anticipate that this data assimilation framework will enable better understanding of the detailed interactions governing sleep and wake behavior and provide for better, more targeted, therapies. Mathematical models are developed to better understand interactions between components of a system that together govern the overall behavior. Mathematical models of sleep have helped to elucidate the neuronal cell groups that are involved in promoting sleep and wake behavior and the transitions between them. However, to be able to take full advantage of these models one must be able to estimate the value of all included variables accurately. Data assimilation refers to methods that allow the user to combine noisy measurements of just a few system variables with the mathematical model of that system to estimate all variables, including those originally inaccessible for measurement. Using these techniques we show that we can reconstruct the unmeasured variables and parameters of a mathematical model of the sleep-wake network. These reconstructed estimates can then be used to better understand the underlying neuronal behavior that results in sleep and wake activity. Because sleep is implicated in a wide array of neurological disorders from epilepsy to schizophrenia, we anticipate that this framework will enable better understanding of the link between sleep and the rest of the brain and provide for better, more targeted, therapies.
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影响因子：
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