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.
翻译:功能互连性( TVFC) 调查大脑区域之间的相互作用在FMRI实验过程中是如何变化的。 不同的个体互连性国家之间的过渡可以通过驱动功能网络动态的内在生理机制的变化来调节, 例如,通过学生变相测量的注意力或认知努力的变化。 在本文中, 我们开发了一个多主题的贝叶斯框架, 用于估算动态功能网络, 作为时间变化的外生生理共变函数的函数, 在FMRI实验中每个主题同时记录。 更具体地说, 我们考虑一种动态高斯的图形模型方法, 使用非相异的隐藏的马尔科夫模型将FMRI时间序列分类到潜在的神经系统状态, 在整个实验过程中, 将注意力变化或认知努力的强度借用。 作为基础变异函数的函数, 允许对经常连连通性模式进行估计, 以及各主体之间网络的共享。 我们的建模方法进一步假设网络结构的封闭性, 通过缩缩前的轨迹隐隐隐隐性模式, 我们的深度选择了前期的测算模型, 在测算模型结构中, 我们的测算中, 我们的测算的测算中, 的测算中, 我们的测序的测算的测序的测算的测算框架 采用了了我们测算中, 的测算的测算 的测算的测算的测算的测算结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构,, 的测测算了我们用测算了我们的测算的测算中, 的测算了我们用测算了我们用测测测测算了我们的测测算了我们测测测测测算了我们测测测测测测测的测的测的测测测的测测测测测测测测测算结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构。