B0227
Title: Distributed model building and recursive integration for functional connectivity modeling
Authors: Emily Hector - North Carolina State University (United States) [presenting]
Abstract: Motivated by the important need for computationally tractable statistical methods in the neuroimaging of autism, a distributed and integrated framework is developed for the estimation and inference of functional connectivity model parameters with ultra-high-dimensional likelihoods. A paradigm shift is proposed from whole to local brain perspectives that is rooted in distributed model building and integrated estimation and inference. The framework's backbone is a computationally and statistically efficient integration procedure that simultaneously incorporates dependence within and between neural resolutions in a recursively partitioned brain. Statistical and computational properties of the distributed approach are investigated in simulations on a variable number of cores. The proposed approach is used to extract new insights on autism spectrum disorder from the autism brain imaging data exchange.
