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 (HMGMs). 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.
翻译:作为一个相对较新的领域,网络神经科学倾向于侧重于大脑在很多连续的实验或长长的录音中的平均整体行为,以建立稳健的大脑模型。这些模型在解释大脑正常大脑功能自发产生的动态状态变化方面能力有限。受过神经成形时间序列数据培训的隐藏的Markov模型(HMMS)自此产生,作为产生动态模型的一种方法,这种模型易于培训,但可能难以完全对映射或分析。我们提议将这些神经HMMM作为多重脑区域多重状态图形模型来解释,我们称之为隐藏的Markov图表模型(HMGMs)。这种解释使得能够利用网络特性特性分析技术的完整重新组合来分析动态大脑活动。此外,我们提议了一个在缺乏外部数据的情况下选择 HMMM超参数的一般方法,该方法以最大通则容易培训,但可能难以完全对等式模型中层数进行选择。我们提出了一种新的工具,用于确定重要的脑区域群体,使用随机随机随机程序,利用马可夫图模型(HMMGMGMs) 模型基础的大脑特性特性特性特性模型结构结构结构结构结构结构结构分析,从而提供新的动态模型的动态模型的动态模型和机能模型中的新模型的模型的动态模型,为新的模型的动态模型的动态模型的模型。我们的数据分析提供了新的模型的动态模型的动态模型的动态模型的动态模型的动态模型,提供了新的模型的动态模型的动态模型。