Classifying dynamic transitions in high dimensional neural mass models: A random forest approach.
Classifying dynamic transitions in high dimensional neural mass models: A random forest approach.
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DOI：
10.1371/journal.pcbi.1006009
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发表时间：
2018-03
影响因子：
4.3
通讯作者：
Terry JR
中科院分区：
文献类型：
--
作者：
Ferrat LA;Goodfellow M;Terry JR
神经质量模型(NMM)越来越多地被用来揭示健康和疾病中大脑节律的大规模机制。这些模型的动力学依赖于参数的选择，因此，能够理解当参数变化时动力学如何变化是至关重要的。尽管与微尺度的神经网络模型相比，NMM被认为是低维的，但在理解参数和动力学之间的关系方面，NMM仍然是高维的，对于经典的方法，如数值延拓。因此，我们需要另一种方法来表征高维参数空间中的NMM的动力学特性。在这里，我们引入了一个统计框架，该框架能够有效地探索模型参数与模拟的、新出现的NMM模型动力学的选定特征之间的关系。我们结合了树和随机森林的经典机器学习方法来研究变化的多个参数对模型动力学的影响。该方法通过使用模拟将数学模型转换成数据库来进行。然后，使用随机森林，该数据库被用于相对于感兴趣的动态特征来划分参数空间。这使我们能够快速探索高维参数空间中的动力学，捕捉动力学中定性转变的大致位置，并同时评估模型中所有参数在所有维度中的相对重要性。我们将这种方法应用于一个常用的NMM，在向癫痫动力学转变的背景下。我们发现抑制子系统对癫痫发作动力学的产生是最关键的，证实和扩展了先前关于兴奋和抑制比率的发现，并证明了以前被忽略的参数可以对模型动力学产生重大影响。我们主张在未来使用这种方法来约束高维参数空间，从而实现更有效的、特定于人的模型校准。对神经科学来说，了解健康大脑的工作原理和导致疾病的干扰仍然是一个巨大的挑战。鉴于大脑的复杂性，数学模型对于阐明这些基本机制正变得越来越重要。然而，随着我们基本理解的发展，模型也会变得更加复杂。如果模型只有一个或两个参数，形式分析是可能的，但随着模型参数的增加，理解系统行为的变化变得越来越困难。在这篇文章中，我们介绍了一种克服这一挑战的方法，并用它来更好地阐明不同机制对大脑节律出现的贡献。我们的方法使用机器学习方法对模型在不同参数下的动力学进行分类，并计算它们的变异性。这使我们能够确定哪些参数对于特定动力学的出现至关重要。将这种方法应用于一个经典的癫痫模型，我们找到了对癫痫发作产生的新解释。这种方法可以很容易地用于计算生物学的其他应用领域。
Neural mass models (NMMs) are increasingly used to uncover the large-scale mechanisms of brain rhythms in health and disease. The dynamics of these models is dependent upon the choice of parameters, and therefore it is crucial to be able to understand how dynamics change when parameters are varied. Despite being considered low dimensional in comparison to micro-scale, neuronal network models, with regards to understanding the relationship between parameters and dynamics, NMMs are still prohibitively high dimensional for classical approaches such as numerical continuation. Therefore, we need alternative methods to characterise dynamics of NMMs in high dimensional parameter spaces. Here, we introduce a statistical framework that enables the efficient exploration of the relationship between model parameters and selected features of the simulated, emergent model dynamics of NMMs. We combine the classical machine learning approaches of trees and random forests to enable studying the effect that varying multiple parameters has on the dynamics of a model. The method proceeds by using simulations to transform the mathematical model into a database. This database is then used to partition parameter space with respect to dynamic features of interest, using random forests. This allows us to rapidly explore dynamics in high dimensional parameter space, capture the approximate location of qualitative transitions in dynamics and assess the relative importance of all parameters in the model in all dimensions simultaneously. We apply this method to a commonly used NMM in the context of transitions to seizure dynamics. We find that the inhibitory sub-system is most crucial for the generation of seizure dynamics, confirm and expand previous findings regarding the ratio of excitation and inhibition, and demonstrate that previously overlooked parameters can have a significant impact on model dynamics. We advocate the use of this method in future to constrain high dimensional parameter spaces enabling more efficient, person-specific, model calibration. Understanding the workings of the healthy brain and the disruptions that lead to disease remains a grand challenge for neuroscience. Given the complexity of the brain, mathematical models are becoming increasingly important to elucidate these fundamental mechanisms. However, as our fundamental understanding evolves, so models grow in complexity. If the model has only one or two parameters, formal analysis is possible, however understanding changes in system behaviour becomes increasingly difficult as the number of model parameters increases. In this article we introduce a method to overcome this challenge and use it to better elucidate the contribution of different mechanisms to the emergence of brain rhythms. Our method uses machine learning approaches to classify the dynamics of the model under different parameters and to calculate their variability. This allows us to determine which parameters are critically important for the emergence of specific dynamics. Applying this method to a classical model of epilepsy, we find new explanations for the generation of seizures. This method can readily be used in other application areas of computational biology.
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影响因子：
4.9
作者：
Campolongo, Francesca;Cariboni, Jessica;Saltelli, Andrea
通讯作者：
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VAPNIK, V