Autism spectrum disorder (ASD) is a heterogeneous disorder characterized by repetitive and stereotyped be-havior and difficulties in communication and social interaction. It is now one of the most prevalent psychiatricdisorders in childhood, but it is also a lifelong condition, adversely affecting an individual's social relationships,independence and employment well into adult. A major barrier to creating effective treatments for autism is thelack of understanding of the specific brain mechanisms involved and how these are related to specific behavioralsymptoms. We propose to develop novel statistical methods for combining heterogeneous imaging and behavioral datato understand how properties of complex brain networks give rise to behavioral phenotypes in autism and otherneuropsychiatric disorders. The first contribution of this project is to develop novel image analysis methods toextract individualized features of complex brain networks from imaging data. This includes powerful method fordescribing the shape of gray matter in brain networks based on diffeomorphic image registration and a rigorousmethod for inferring an individual's functional connectivity based on a hierarchical Bayesian model. The nextcontribution is a novel method to capture the topology of brain networks simultaneously across all scale levelsof connection strength. Finally, we will develop Bayesian statistical methods for finding correlations in high-dimensional and heterogeneous data, and we will use this to analyze the relationship between brain networksand behavior. This project includes a strong collaborative and multi-disciplinary team with expertise in computerscience, statistical data analysis, neuroimaging, and clinical autism care. A primary goal of this project is tocreate open-source software that is used by the neuroscience community to advance research in understandingthe brain basis of behavior in neuropsychiatric disorders.
Interagency Autism Coordinating Committee (IACC)
Autism Research Database (AFD)
