This application for an Autism Centers of Excellence (ACE) Network is a collaborative effort by investigators at four clinical sites: University of North Carolina (UNC), University of Washington (UW), Washington University (WU), and Yale University;and one data coordinating center (DCC) at the Montreal Neurological Institute (MNI) to conduct a longitudinal MRI/DTI and behavioral study of infants at high risk for autism (i.e., siblings of autistic individuals) at 6, 12, and 24 months of age. Multiple lines of converging evidence (from MRI, post-mortem, and head circumference studies) document brain enlargement in autism. MRI studies have revealed generalized enlargement in cerebral cortical gray and white matter, and selected subcortical structures as early as two years of age. Longitudinal head circumference studies suggest the onset of brain overgrowth beginning in the latter part of the first year of life. Data from behavioral studies of infant siblings of autistic individuals (infant siblings who are therefore at high risk for developing an autism spectrum disorder, or ASD) suggest that some of the defining features of this condition are not present at six months of age (e.g., social deficits) and have their first appearance by 12 months. New imaging methods are available that now provide highly detailed MRI data and are well suited for rapid scanning of very young children. The investigators'MRI research team has developed image processing tools specifically designed for highly efficient, reliable, and valid processing of MRI data from birth to two years of age. New behavioral assessment tools (the AOSI and FYI), recently developed by investigators on this application, now enable the efficient screening and detection, at 12 months, of infants who are likely to meet criteria for autism at age two. In this application they propose to employ these new methods to conduct a longitudinal MRI/DTI study of six months old infant sibs of autistic individuals, with follow-up at 12 and 24 months of age. This study will provide important, new information about the trajectory of early postnatal brain overgrowth (regions, tissues, structures, and fiber tracts), as measured on MRI and DTI, and its potential relationship to clinical features. It has the potential to provide insights into developmental brain and behavioral phenotypes, as well as neurobiological mechanisms, that will inform other levels of analysis (e.g., molecular biological studies of this period), during what appears to be a critical period of development in the pathogenesis of autism.
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| Piven, J; Elison, J T; Zylka, M J (2018) Toward a conceptual framework for early brain and behavior development in autism. Mol Psychiatry 23:165 |
| Ngattai Lam, Prince D; Belhomme, Gaetan; Ferrall, Jessica et al. (2018) TRAFIC: Fiber Tract Classification Using Deep Learning. Proc SPIE Int Soc Opt Eng 10574: |
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| St John, Tanya; Dawson, Geraldine; Estes, Annette (2018) Brief Report: Executive Function as a Predictor of Academic Achievement in School-Aged Children with ASD. J Autism Dev Disord 48:276-283 |
| Marrus, Natasha; Eggebrecht, Adam T; Todorov, Alexandre et al. (2018) Walking, Gross Motor Development, and Brain Functional Connectivity in Infants and Toddlers. Cereb Cortex 28:750-763 |
| Estes, Annette; Munson, Jeffrey; John, Tanya St et al. (2018) Parent Support of Preschool Peer Relationships in Younger Siblings of Children with Autism Spectrum Disorder. J Autism Dev Disord 48:1122-1132 |
| Mostapha, Mahmoud; Kim, SunHyung; Wu, Guorong et al. (2018) NON-EUCLIDEAN, CONVOLUTIONAL LEARNING ON CORTICAL BRAIN SURFACES. Proc IEEE Int Symp Biomed Imaging 2018:527-530 |
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