Wilsenach James B, Warnaby Catherine E, Deane Charlotte M, Reinert Gesine D
Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK.
Department of Statistics, University of Oxford, Oxford, UK.
Appl Netw Sci. 2022;7(1):15. doi: 10.1007/s41109-022-00454-2. Epub 2022 Mar 14.
As a relatively new field, network neuroscience has tended to focus on aggregate behaviours of the brain averaged over many successive experiments or over long recordings in order to construct robust brain models. These models are limited in their ability to explain dynamic state changes in the brain which occurs spontaneously as a result of normal brain function. Hidden Markov Models (HMMs) trained on neuroimaging time series data have since arisen as a method to produce dynamical models that are easy to train but can be difficult to fully parametrise or analyse. We propose an interpretation of these neural HMMs as multiplex brain state graph models we term Hidden Markov Graph Models. This interpretation allows for dynamic brain activity to be analysed using the full repertoire of network analysis techniques. Furthermore, we propose a general method for selecting HMM hyperparameters in the absence of external data, based on the principle of maximum entropy, and use this to select the number of layers in the multiplex model. We produce a new tool for determining important communities of brain regions using a spatiotemporal random walk-based procedure that takes advantage of the underlying Markov structure of the model. Our analysis of real multi-subject fMRI data provides new results that corroborate the modular processing hypothesis of the brain at rest as well as contributing new evidence of functional overlap between and within dynamic brain state communities. Our analysis pipeline provides a way to characterise dynamic network activity of the brain under novel behaviours or conditions.
The online version contains supplementary material available at 10.1007/s41109-022-00454-2.
作为一个相对较新的领域，网络神经科学倾向于关注在许多连续实验或长时间记录上平均得到的大脑总体行为，以便构建稳健的大脑模型。这些模型在解释由于正常大脑功能而自发发生的大脑动态状态变化方面能力有限。此后，基于神经成像时间序列数据训练的隐马尔可夫模型（HMM）作为一种产生易于训练但可能难以完全参数化或分析的动态模型的方法而出现。我们提出将这些神经HMM解释为我们称为隐马尔可夫图模型的多重脑状态图模型。这种解释允许使用网络分析技术的全部方法来分析动态大脑活动。此外，我们基于最大熵原理提出了一种在没有外部数据的情况下选择HMM超参数的通用方法，并使用此方法选择多重模型中的层数。我们开发了一种新工具，使用基于时空随机游走的程序来确定大脑区域的重要群落，该程序利用了模型的潜在马尔可夫结构。我们对真实多受试者功能磁共振成像数据的分析提供了新的结果，证实了静息状态下大脑的模块化处理假设，并为动态脑状态群落之间和内部的功能重叠提供了新的证据。我们的分析管道提供了一种在新行为或条件下表征大脑动态网络活动的方法。
在线版本包含可在10.1007/s41109-022-00454-2获取的补充材料。
