A Predictor-Informed Multi-Subject Bayesian Approach for Dynamic Functional Connectivity
融合预测变量的多受试者动态功能连接贝叶斯方法AI 解读 ↓
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融合预测变量的多受试者动态功能连接贝叶斯方法
- 研究问题
- 如何利用同步记录的时变生理协变量，估计多受试者脑区连接状态及其转移，并刻画受试者之间的连接动态异质性？
- 方法与思路
- 作者结合动态高斯图模型与非齐次隐马尔可夫模型，让转移概率随协变量和受试者变化，以收缩先验及贝叶斯错误发现率控制选边。
- 材料陈述的贡献
- 摘要称，该框架可估计重复连接模式并在受试者间共享网络，应用于同步采集的功能磁共振与瞳孔数据以研究状态转换差异。
- 阅读关注点
- 应核对隐状态数量与可识别性、跨受试者网络共享方式、协变量进入转移概率的形式，以及边选择和错误发现率控制的定义。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收。摘要未说明计算诊断及效果数值，协变量关联也不能直接确立生理因果机制。
原文摘要
Time Varying Functional Connectivity (TVFC) investigates how the interactions among brain regions vary over the course of an fMRI experiment. The transitions between different individual connectivity states can be modulated by changes in underlying physiological mechanisms that drive functional network dynamics, e.g., changes in attention or cognitive effort as measured by pupil dilation. In this paper, we develop a multi-subject Bayesian framework for estimating dynamic functional networks as a function of time-varying exogenous physiological covariates that are simultaneously recorded in each subject during the fMRI experiment. More specifically, we consider a dynamic Gaussian graphical model approach, where a non-homogeneous hidden Markov model is employed to classify the fMRI time series into latent neurological states, borrowing strength over the entire time course of the experiment. The state-transition probabilities are assumed to vary over time and across subjects, as a function of the underlying covariates, allowing for the estimation of recurrent connectivity patterns and the sharing of networks among the subjects. Our modeling approach further assumes sparsity in the network structures, via shrinkage priors. We achieve edge selection in the estimated graph structures, by introducing a multi-comparison procedure for shrinkage-based inferences with Bayesian false discovery rate control. We apply our modeling framework on a resting-state experiment where fMRI data have been collected concurrently with pupillometry measurements, leading us to assess the heterogeneity of the effects of changes in pupil dilation, previously linked to changes in norepinephrine-containing locus coeruleus, on the subjects' propensity to change connectivity states.