Identifying functional co‐activation patterns in neuroimaging studies via poisson graphical models

Summary Studying the interactions between different brain regions is essential to achieve a more complete understanding of brain function. In this article, we focus on identifying functional co‐activation patterns and undirected functional networks in neuroimaging studies. We build a functional brain network, using a sparse covariance matrix, with elements representing associations between region‐level peak activations. We adopt a penalized likelihood approach to impose sparsity on the covariance matrix based on an extended multivariate Poisson model. We obtain penalized maximum likelihood estimates via the expectation‐maximization (EM) algorithm and optimize an associated tuning parameter by maximizing the predictive log‐likelihood. Permutation tests on the brain co‐activation patterns provide region pair and network‐level inference. Simulations suggest that the proposed approach has minimal biases and provides a coverage rate close to 95% of covariance estimations. Conducting a meta‐analysis of 162 functional neuroimaging studies on emotions, our model identifies a functional network that consists of connected regions within the basal ganglia, limbic system, and other emotion‐related brain regions. We characterize this network through statistical inference on region‐pair connections as well as by graph measures.