Markov chain Monte Carlo methods for hierarchical clustering of dynamic causal models.

ABSTRACT: In this article, we address technical difficulties that arise when applying Markov chain Monte Carlo (MCMC) to hierarchical models designed to perform clustering in the space of latent parameters of subject-wise generative models. Specifically, we focus on the case where the subject-wise generative model is a dynamic causal model (DCM) for functional magnetic resonance imaging (fMRI) and clusters are defined in terms of effective brain connectivity. While an attractive approach for detecting mechanistically interpretable subgroups in heterogeneous populations, inverting such a hierarchical model represents a particularly challenging case, since DCM is often characterized by high posterior correlations between its parameters. In this context, standard MCMC schemes exhibit poor performance a

SUBMITTER: Yao Y 

PROVIDER: S-EPMC8193526 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature