Autism spectrum disorders are now among the most prevalent medical conditions of childhood. Only a small fraction of the 486,000 individuals under 20 years of age with an autism-spectrum disorder (ASD) in the U.S are young enough to benefit from intensive early intervention. Overall prognosis for the older children with autism is not good. Despite improvements in treatment and education over the past 30 years, adult outcome even for non-mentally retarded individuals with autism has not significantly improved. The majority continue to need high levels of parental and community support throughout their lives. One reason for this huge public health problem is that the brain-basis of fundamental deficits in older children and adults with autism is not well understood. We propose collaboration between a longitudinal neuroimaging, clinical, and neuropsychological study of late neurodevelopment in autism and the National Alliance for Medical Imaging Computing (NA- MIC), one of the NIH Roadmap National Centers for Biomedical Computing. The collaboration will bring state-of-the-art brain imaging analysis tools developed by NA-MIC into autism clinical research and form a new highly interactive multidisciplinary research team. Working together, the computer scientists and clinical researchers will use critical biological questions in autism to "drive" the development of NA-MIC "tools". The critical biological questions are 1) what is the microstructural basis of abnormal brain connectivity during late neurodevelopment in autism, and 2) how is brain microstructure related to deficits, developmental trajectory, and outcome. We will use Time 1 and Time 2 high-resolution MRI and diffusion tensor imaging data that have already been collected on a single 3Tesla scanner on a cohort of 100 males with high-functioning autism and 72 typically developing males. Time 3 and 4 data are being collected over the next 5 years (MH080826). We will use novel, non-tractography-based, diffusion tensor image analysis methods developed by NA-MIC to compare, at the level of both individuals and groups, microstructural features along entire white matter tracts in language, social, and repetitive behavior neural networks. We will integrate the white matter analysis with structural image analysis of cortical and subcortical gray matter. We will determine how microstructural white matter features, gray matter morphometric features, and clinical deficits are related to each other and change over time in autism.

Public Health Relevance

Autism is now one of the most prevalent medical disorders of childhood. Adult outcome for even high-functioning older children and adolescents with autism is still usually poor, with a high need for lifelong clinical and community support. Understanding the brain- basis of deficits, disease course, and outcome in these individuals is critical for development of autism-specific treatments as well as secondary and tertiary preventive interventions.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-BST-E (50))
Program Officer
Gilotty, Lisa
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Utah
Schools of Medicine
Salt Lake City
United States
Zip Code
Lange, Nicholas; Travers, Brittany G; Bigler, Erin D et al. (2015) Longitudinal volumetric brain changes in autism spectrum disorder ages 6-35 years. Autism Res 8:82-93
Travers, Brittany G; Bigler, Erin D; Tromp, Do P M et al. (2014) Longitudinal processing speed impairments in males with autism and the effects of white matter microstructure. Neuropsychologia 53:137-45
Zielinski, Brandon A; Prigge, Molly B D; Nielsen, Jared A et al. (2014) Longitudinal changes in cortical thickness in autism and typical development. Brain 137:1799-812
Hao, Xiang; Zygmunt, Kristen; Whitaker, Ross T et al. (2014) Improved segmentation of white matter tracts with adaptive Riemannian metrics. Med Image Anal 18:161-75
Liu, Wei; Awate, Suyash P; Anderson, Jeffrey S et al. (2014) A functional network estimation method of resting-state fMRI using a hierarchical Markov random field. Neuroimage 100:520-34
Adluru, Nagesh; Hanlon, Bret M; Lutz, Antoine et al. (2013) Penalized likelihood phenotyping: unifying voxelwise analyses and multi-voxel pattern analyses in neuroimaging: penalized likelihood phenotyping. Neuroinformatics 11:227-47
Duffield, Tyler C; Trontel, Haley G; Bigler, Erin D et al. (2013) Neuropsychological investigation of motor impairments in autism. J Clin Exp Neuropsychol 35:867-81
Prigge, Molly D; Bigler, Erin D; Fletcher, P Thomas et al. (2013) Longitudinal Heschl's gyrus growth during childhood and adolescence in typical development and autism. Autism Res 6:78-90
Prigge, Molly B D; Lange, Nicholas; Bigler, Erin D et al. (2013) Corpus Callosum Area in Children and Adults with Autism. Res Autism Spectr Disord 7:221-234
Adluru, Nagesh; Zhang, Hui; Tromp, Do P M et al. (2013) Effects of DTI spatial normalization on white matter tract reconstructions. Proc SPIE Int Soc Opt Eng 8669:

Showing the most recent 10 out of 12 publications