title: Identifying functional co-activation patterns in neuroimaging studies via poisson graphical models.
abstract: 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.
authors.affiliation: Department of Biostatistics and Bioinformatics, Center for Biomedical Imaging Statistics, Rollins School of Public Health, Emory University, Atlanta, Georgia, U.S.A.; Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, Connecticut, U.S.A.
authors.forename: Wenqiong
authors.initials: W
authors.lastname: Xue
authors.forename: Jian
authors.initials: J
authors.lastname: Kang
authors.forename: F DuBois
authors.initials: FD
authors.lastname: Bowman
authors.forename: Tor D
authors.initials: TD
authors.lastname: Wager
authors.forename: Jian
authors.initials: J
authors.lastname: Guo
mesh_terms: D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D004644:Emotions / Q000502:physiology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D016012:Poisson Distribution; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: EM algorithm; Emotion; Functional brain networks; Functional co-activation pattern identification; Poisson Graphical Model