Identification of modulated whole-brain dynamical models from nonstationary electrophysiological data.

IF 3.8
Addison Schwamb, Zongxi Yu, ShiNung Ching
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Abstract

Objective.Understanding the mechanisms underlying brain dynamics is a long-held goal in neuroscience. However, these dynamics are both individualized and nonstationary, making modeling challenging. Here, we present a data-driven approach to modeling nonstationary dynamics based on principles of neuromodulation, at the level of individual subjects.Approach.Previously, we developed the mesoscale individualized neural dynamics (MINDy) modeling approach to capture individualized brain dynamics which do not change over time. Here, we extend the MINDy approach by adding a modulatory component which is multiplied by a set of baseline, stationary connectivity weights. We validate this model on both synthetic data and publicly available electroencephalography data in the context of anesthesia, a known modulator of neural dynamics.Main results.We find that our modulated MINDy approach is accurate, individualized, and reliable. Additionally, we find that our models yield biologically interpretable inferences regarding the effects of propofol anesthesia on mesoscale cortical networks, consistent with previous literature on the neuromodulatory effects of propofol.Significance.Ultimately, our data-driven modeling approach is reliable and scalable, and provides insight into mechanisms underlying observed brain dynamics. Our modeling methodology can be used to infer insights about modulation dynamics in the brain in a number of different contexts.

从非平稳电生理数据中识别调制全脑动力学模型。
目的:了解脑动力学的机制是神经科学长期以来的目标。然而,这些动态都是个性化的和非平稳的,使得建模具有挑战性。在这里,我们提出了一种基于神经调节原理的数据驱动方法,在个体受试者的水平上对非平稳动力学进行建模。方法:先前,我们开发了中尺度个体化神经动力学(MINDy)建模方法来捕获不随时间变化的个体化脑动力学。在这里,我们通过添加一个调制组件来扩展MINDy方法,该组件乘以一组基线、固定连接权重。我们在麻醉的背景下对合成数据和公开可用的脑电图数据验证了这个模型,这是一种已知的神经动力学调节剂。主要结果:我们发现我们的调制MINDy方法是准确的,个性化的,可靠的。此外,我们发现我们的模型对异丙酚麻醉对中尺度皮层网络的影响产生了生物学上可解释的推断,这与之前关于异丙酚神经调节作用的文献一致。意义:最终,我们的数据驱动建模方法是可靠的和可扩展的,并提供了对观察到的大脑动力学机制的见解。我们的建模方法可以用来推断在许多不同的情况下大脑中的调制动力学的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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