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.

该研究使用隐藏马尔科夫模型（HMM）来构建多层的带有时空结构的网络模型，探索动态脑活动和区域之间的交互。结果显示出支持神经熵理论的新证据，同时也支持原有的模块化处理假设。