This application is a competing continuation of an Autism Center of Excellence (ACE) Network grant now entitled, `A Longitudinal Brain and Behavior Study of Autism from Infancy through School Age`. Prior funding has supported a prospective, longitudinal study that has collected high quality brain imaging and behavior assessments in children at high- and low- familial risk (HR, LR) for an autism spectrum disorder (ASD), at 2-4 time points (including 3, 6, 9, 12, 15 and 24 months) with 36 month diagnostic re-assessment for autism. This project has been successful in producing 50 manuscripts either published/in press (#35) or under review (#15); and generated 21 external funding opportunities, leveraging this network and expanding the scope of this work. The overarching goal of this ACE Network competing continuation is to continue to follow a unique cohort of 300 HR and 100 LR children into school age (7-10 years) with detailed brain and behavior assessments. School age is a time when academic and social functioning are critically important for future success and a time when HR children are prone to manifest comorbid psychiatric disorders, difficulties with peer relationships, and learning problems which can be assessed more extensively and with greater detail than at earlier ages. Work from this network has revealed that: (1) early brain imaging features are detectable by 6 months of age, well before ASD diagnosis is possible, in those who go on to have an ASD diagnosis at 24 months; (2) autism-specific brain and behavior features change substantially from 6-24 months of age, as autism unfolds; and, (3) brain features in the first year of life are associated with later ASD behaviors and accurately predict individual ASD diagnosis at 24 months. The proposed work extends this solid foundation. In this proposal we aim to: (1) characterize school- age clinical outcomes of HR children and determine early predictors of those clinical outcomes from brain imaging and behavioral features we have already identified from 3-36 months; (2) characterize brain and brain- behavior trajectories in HR-ASD from infancy through school-age and identify the timing of ASD-related brain changes; and (3) empirically derive and validate novel subgroups within the HR group based on brain and behavior trajectories from infancy through school age, incorporating data from molecular genetics and environmental exposures. The potential impact of this study includes: (1) early identification (< 3 years) of children who are more likely to develop school-age (7-10 years) clinical problems, increasing the potential for early intervention; (2) informing intervention studies by identifying age-specific brain targets, biomarkers of treatment efficacy, and targets for pre-clinical, cross-species studies to inform drug development; and,(3) identifying empirically-derived and biologically-meaningful subgroups, based on brain and behavior trajectories from infancy to school age, that could be used to support development of individualized interventions.

Public Health Relevance

This ACE Network proposal ?A Longitudinal Brain and Behavior Study of Autism from Infancy through School Age? aims to examine school-age outcomes in 300 infants at high familial risk (HR) and 100 at low risk (LR) for autism, who have been examined using detailed brain imaging and behavior assessments from 3-36 months of age. The main goals of this proposal are: (1) to identify brain and behavior predictors of school-age cognitive, behavioral and learning problems in HR children from brain and behavior features we have already measured in the first three years of life, (2) to characterize the dynamics of brain development in children with autism and related conditions, from infancy through school age; and (3) to derive new subgroups within the HR population, based on longitudinal brain and behavior features from infancy to school age. This study will facilitate the development of tools for early identification and more timely and effective interventions for autism and related conditions, accelerating research towards personalized intervention.

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Research Project (R01)
Project #
5R01HD055741-13
Application #
9761865
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Kau, Alice S
Project Start
2007-07-01
Project End
2022-07-31
Budget Start
2019-08-01
Budget End
2020-07-31
Support Year
13
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Psychiatry
Type
Schools of Medicine
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
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Mostapha, Mahmoud; Shen, Mark D; Kim, SunHyung et al. (2018) A Novel Framework for the Local Extraction of Extra-Axial Cerebrospinal Fluid from MR Brain Images. Proc SPIE Int Soc Opt Eng 10574:
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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:
Swanson, Meghan R; Shen, Mark D; Wolff, Jason J et al. (2018) Naturalistic Language Recordings Reveal ""Hypervocal"" Infants at High Familial Risk for Autism. Child Dev 89:e60-e73
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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