Department of Bioengineering, University of California, Riverside, Riverside, California, USA.
Center for Advanced Neuroimaging, University of California, Riverside, Riverside, California, USA.
Brain Connect. 2023 Apr;13(3):154-163. doi: 10.1089/brain.2022.0031. Epub 2023 Feb 16.
Hidden Markov models (HMMs) are a popular choice to extract and examine recurring patterns of activity or functional connectivity in neuroimaging data, both in terms of spatial patterns and their temporal progression. Although many diverse HMMs have been applied to neuroimaging data, most have defined states based on activity levels (intensity-based [IB] states) rather than patterns of functional connectivity between brain areas (connectivity-based states), which is problematic if we want to understand connectivity dynamics: IB states are unlikely to provide comprehensive information about dynamic connectivity patterns. We addressed this problem by introducing a new HMM that defines states based on full functional connectivity (FFC) profiles among brain regions. We empirically explored the behavior of this new model in comparison to existing approaches based on IB or summed functional connectivity states using the Human Connectome Project unrelated 100 functional magnetic resonance imaging "resting-state" dataset. Our FFC model discovered connectivity states with more distinguishable (i.e., unique and separable from each other) patterns than previous approaches, and recovered simulated connectivity-based states more faithfully than the other models tested. Thus, if our goal is to extract and interpret connectivity states in neuroimaging data, our new model outperforms previous methods, which miss crucial information about the evolution of functional connectivity in the brain. Impact statement Hidden Markov models (HMMs) can be used to investigate brain states noninvasively. Previous models "recover" connectivity from intensity-based hidden states, or from connectivity "summed" across nodes. In this study, we introduce a novel connectivity-based HMM and show how it can reveal true connectivity hidden states under minimal assumptions.
隐马尔可夫模型（HMM）是一种从神经影像学数据中提取和检查活动或功能连接重复模式的常用方法，既可以从空间模式方面，也可以从时间演变方面进行研究。尽管已经有许多不同的 HMM 应用于神经影像学数据，但大多数模型都是基于活动水平（基于强度[IB]的状态）而不是大脑区域之间功能连接模式（基于连接的状态）来定义状态，如果我们想要了解连接动力学，这就存在问题：IB 状态不太可能提供关于动态连接模式的全面信息。我们通过引入一种新的 HMM 来解决这个问题，该模型基于大脑区域之间的全功能连接（FFC）分布来定义状态。我们使用人类连接组计划无关的 100 个功能磁共振成像“静息状态”数据集，对这个新模型与基于 IB 或总功能连接状态的现有方法进行了实证比较。我们的 FFC 模型发现的连接状态具有更可区分的模式（即彼此独特且可分离），比以前的方法更能真实地恢复模拟的基于连接的状态。因此，如果我们的目标是从神经影像学数据中提取和解释连接状态，那么我们的新模型优于以前的方法，因为后者会错过有关大脑功能连接演变的重要信息。
