Global nonlinear approach for mapping parameters of neural mass models.
Global nonlinear approach for mapping parameters of neural mass models.
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DOI：
10.1371/journal.pcbi.1010985
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发表时间：
2023-03
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
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神经质量模型（Neural mass models，简称NMF）对于帮助我们解释大脑动力学的观测结果非常重要。它们提供了一种手段来理解数据的机制，如兴奋性和抑制性神经元群体之间的突触相互作用。为了使用NERGY解释数据，我们需要将NERGY的输出与数据进行定量比较，从而找到模型可以产生观测动态的参数值。以这种方式将动态映射到NMM参数值有可能提高我们对健康和疾病中大脑的理解。虽然抽象，但Nyns仍然包含许多难以先验约束的参数。这使得它具有挑战性，探索的动态NATURAL和阐明区域的参数空间中，他们的动态最好的近似数据。克服这一挑战的现有方法使用线性化模型的组合，约束它们可以采用的值，并通过先验地固定许多参数的值来探索受限的子空间。因此，我们几乎不知道NATIONAL参数空间的不同区域在多大程度上可以产生近似数据的动态，模型中的非线性如何影响参数映射，或者如何最好地量化模型输出和数据之间的相似性。这些问题需要得到解决，以充分了解NATIONAL的潜力和局限性，并有助于在未来开发新的大脑动力学模型。为了开始克服这些问题，我们提出了一个全球性的非线性方法来恢复参数的数据。我们使用全局优化，同时探索所有参数的非线性Nynomial，在一个最小的约束方式。我们使用多目标优化（多目标进化算法，MOEA）来实现这一点，以便可以量化多个数据特征。特别是，我们使用加权水平可见性图（wHVG），这是一个灵活的框架，量化不同方面的时间序列，通过将它们转换成网络。我们研究了EEG α活动记录在眼睛关闭休息状态从20个健康人，并证明了MOEA表现良好相比，单目标的方法。wHVG目标的加入使我们能够更好地约束模型输出，这导致恢复的参数值被限制在参数空间的较小区域，从而提高了模型的实际可识别性。然后，我们使用MOEA研究的差异，从20个癫痫患者的脑电图记录中观察到的α节律。我们发现，少量的参数可以解释这种差异，并且与直觉相反，癫痫患者的平均兴奋性突触增益参数与对照组相比有所降低。此外，我们建议，MOEA可以用来挖掘病理性节律的存在，并证明这癫痫样棘波放电的应用。脑电是研究大范围脑活动的有效工具。已经开发了数学模型来帮助提高对在不同大脑状态期间从EEG记录的信号的生成的理解。这些模型的动态特性取决于其输入（或参数），因此探索导致模型动态特性逼近数据的参数组合非常重要。这使我们能够更好地了解数据是如何产生的。然而，由于这些模型的相对复杂性，找到解释数据的参数组合可能是一项繁琐的任务，因此许多研究简化了模型和数据的比较。在这项研究中，我们介绍的方法，不需要这些简化的假设。使用这些方法，我们证明，我们比较模型和数据的方式不同的选择可能会导致我们推断的潜在机制的差异。然而，我们发现，将不同的选择组合到同一算法中可以使我们更好地近似数据的特征，并更好地约束模型参数。我们应用我们的方法，试图了解癫痫患者和对照组之间的静息脑电图观察到的差异。我们发现，该模型解释了这些差异主要是由癫痫患者的兴奋性突触增益减少。我们还证明了这种方法的潜力，“挖掘”不同种类的动力学在高维模型。
Neural mass models (NMMs) are important for helping us interpret observations of brain dynamics. They provide a means to understand data in terms of mechanisms such as synaptic interactions between excitatory and inhibitory neuronal populations. To interpret data using NMMs we need to quantitatively compare the output of NMMs with data, and thereby find parameter values for which the model can produce the observed dynamics. Mapping dynamics to NMM parameter values in this way has the potential to improve our understanding of the brain in health and disease. Though abstract, NMMs still comprise of many parameters that are difficult to constrain a priori. This makes it challenging to explore the dynamics of NMMs and elucidate regions of parameter space in which their dynamics best approximate data. Existing approaches to overcome this challenge use a combination of linearising models, constraining the values they can take and exploring restricted subspaces by fixing the values of many parameters a priori. As such, we have little knowledge of the extent to which different regions of parameter space of NMMs can yield dynamics that approximate data, how nonlinearities in models can affect parameter mapping or how best to quantify similarities between model output and data. These issues need to be addressed in order to fully understand the potential and limitations of NMMs, and to aid the development of new models of brain dynamics in the future. To begin to overcome these issues, we present a global nonlinear approach to recovering parameters of NMMs from data. We use global optimisation to explore all parameters of nonlinear NMMs simultaneously, in a minimally constrained way. We do this using multi-objective optimisation (multi-objective evolutionary algorithm, MOEA) so that multiple data features can be quantified. In particular, we use the weighted horizontal visibility graph (wHVG), which is a flexible framework for quantifying different aspects of time series, by converting them into networks. We study EEG alpha activity recorded during the eyes closed resting state from 20 healthy individuals and demonstrate that the MOEA performs favourably compared to single objective approaches. The addition of the wHVG objective allows us to better constrain the model output, which leads to the recovered parameter values being restricted to smaller regions of parameter space, thus improving the practical identifiability of the model. We then use the MOEA to study differences in the alpha rhythm observed in EEG recorded from 20 people with epilepsy. We find that a small number of parameters can explain this difference and that, counterintuitively, the mean excitatory synaptic gain parameter is reduced in people with epilepsy compared to control. In addition, we propose that the MOEA could be used to mine for the presence of pathological rhythms, and demonstrate the application of this to epileptiform spike-wave discharges. EEG is a useful tool to study large scale brain activity. Mathematical models have been developed to help improve the understanding of the generation of signals recorded from the EEG during different brain states. The dynamics of these models are dependent on their inputs (or parameters) and hence it is important to explore the parameter combinations that result in model dynamics that approximate data. This allows us to better understand how the data were generated. However, due to the relative complexity of these models, finding the parameter combinations that explain data can be a cumbersome task and hence many studies make simplifications about how model and data are compared. In this study, we introduce methods that do not require these simplifying assumptions. Using these methods we demonstrate that different choices in the way we compare models and data can lead to differences in what we infer about the underlying mechanisms. However, we find that combining different choices into the same algorithm allows us to better approximate features of the data and better constrain model parameters. We apply our method to try to understand differences observed in the resting EEG between patients with epilepsy and controls. We find that the model explains these differences predominately by a reduced excitatory synaptic gain in patients with epilepsy. We also demonstrate the potential of this method to “mine” for different kinds of dynamics in high dimensional models.
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影响因子：
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作者：
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通讯作者：
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影响因子：
4.3
作者：
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影响因子：
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影响因子：
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作者：
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