A Novel Hidden Markov Approach to Studying Dynamic Functional Connectivity States in Human Neuroimaging

人类连接体项目 神经影像学 隐马尔可夫模型 动态功能连接 计算机科学 功能磁共振成像 连接体 功能连接 静息状态功能磁共振成像 人工智能 模式识别(心理学) 机器学习 神经科学 心理学
作者
Sana Hussain,Jason Langley,Aaron R. Seitz,Xiaoping Hu,Megan A. K. Peters
出处
期刊:Brain connectivity [Mary Ann Liebert, Inc.]
卷期号:13 (3): 154-163 被引量:1
标识
DOI:10.1089/brain.2022.0031
摘要

Introduction: 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. Methods: 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. Results: 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. Discussion: 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.
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