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Brain and Behavior Study of Autism from Infancy through School Age

项目来源

美国卫生和人类服务部基金(HHS)

项目主持人

KAU, ALICE S

项目受资助机构

UNIV OF NORTH CAROLINA CHAPEL HILL

项目编号

5R01HD055741-15

立项年度

2021

立项时间

未公开

研究期限

未知 / 未知

项目级别

国家级

受资助金额

2047746.00美元

学科

Autism; Behavioral and Social Science; Brain Disorders; Clinical Research; Intellectual and Developmental Disabilities (IDD); Mental Health; Neurosciences; Pediatric; Prevention;

学科代码

未公开

基金类别

Non-SBIR/STTR RPGs

关键词

未公开

参与者

PIVEN, JOSEPH

参与机构

EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT

项目标书摘要：Project Summary 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.

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  • 1.Multi-modal Perceptual Adversarial Learning for Longitudinal Prediction of Infant MR Images

    • 关键词：
    • Neuroimaging;Magnetic resonance imaging;Generative adversarial networks;Adversarial learning;Adversarial networks;Brain development;Generative methods;Image intensities;Longitudinal study;Prediction process;Quantitative assessments
    • Peng, Liying;Lin, Lanfen;Lin, Yusen;Zhang, Yue;Vlasova, Roza M.;Prieto, Juan;Chen, Yen-wei;Gerig, Guido;Styner, Martin
    • 《1st International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2020, and the 5th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020》
    • 2020年
    • October 4, 2020 - October 8, 2020
    • Lima, Peru
    • 会议

    Longitudinal magnetic resonance imaging (MRI) is essential in neuroimaging studies of early brain development. However, incomplete data is an inevitable problem in longitudinal studies because of participant attrition and scan failure. Data imputation is a possible way to address such missing data. Here, we propose a novel 3D multi-modal perceptual adversarial network (MPGAN) to predict a missing MR image from an existing longitudinal image of the same subject. To the best of our knowledge, this is the first application of deep generative methods for longitudinal image prediction of structural MRI in the first year of life, where brain volume and image intensities are changing dramatically. In order to produce sharper and more realistic images, we incorporate the perceptual loss into the adversarial training process. To leverage complementary information contained in the multi-modality data, MPGAN predicts T1w and T2w images jointly in the prediction process. We evaluated MPGAN versus six alternative approaches based on visual as well as quantitative assessment. The results indicate that our MPGAN predicts missing MR images in an accurate and visually realistic fashion, and shows better performance than the alternative methods. © 2020, Springer Nature Switzerland AG.

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  • 2.Hierarchical Geodesic Modeling on the Diffusion Orientation Distribution Function for Longitudinal DW-MRI Analysis

    • 关键词：
    • Geometry;Magnetic resonance imaging;Neuroimaging;Medical computing;Population statistics;Distribution functions;Probability density function;Geodesy;Fractional Anisotropy;High angular resolution diffusion imaging;Longitudinal diffusions;Nonlinear characteristics;Orientation distribution function;Population variation;Spatio-temporal models;Statistical analysis methods
    • Kim, Heejong;Hong, Sungmin;Styner, Martin;Piven, Joseph;Botteron, Kelly;Gerig, Guido
    • 《23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020》
    • 2020年
    • October 4, 2020 - October 8, 2020
    • Lima, Peru
    • 会议

    The analysis of anatomy that undergoes rapid changes, such as neuroimaging of the early developing brain, greatly benefits from spatio-temporal statistical analysis methods to represent population variations but also subject-wise characteristics over time. Methods for spatio-temporal modeling and for analysis of longitudinal shape and image data have been presented before, but, to our knowledge, not for diffusion weighted MR images (DW-MRI) fitted with higher-order diffusion models. To bridge the gap between rapidly evolving DW-MRI methods in longitudinal studies and the existing frameworks, which are often limited to the analysis of derived measures like fractional anisotropy (FA), we propose a new framework to estimate a population trajectory of longitudinal diffusion orientation distribution functions (dODFs) along with subject-specific changes by using hierarchical geodesic modeling. The dODF is an angular profile of the diffusion probability density function derived from high angular resolution diffusion imaging (HARDI) and we consider the dODF with the square-root representation to lie on the unit sphere in a Hilbert space, which is a well-known Riemannian manifold, to respect the nonlinear characteristics of dODFs. The proposed method is validated on synthetic longitudinal dODF data and tested on a longitudinal set of 60 HARDI images from 25 healthy infants to characterize dODF changes associated with early brain development. © 2020, Springer Nature Switzerland AG.

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