Volume-based Analysis of 6-month Infant Brain MRI for Autism Biomarker Identification and Early Diagnosis

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Project Summary/Abstract Title: Volume-based analysis of 6-month infant brain MRI for autism biomarker identification and early diagnosis Autism spectrum disorder (ASD) is a complex developmental disability, characterized by deficits in social interaction, language skills, repetitive stereotyped behaviors, and restricted interests. Based on a new government survey, it shows 1 in 45 children (ages 3 to 17) are diagnosed with ASD, a significant increase from Centers for Disease Control and Prevention's previously estimated prevalence of 1 in 68 from 2011-2013. Volume-based analysis of neuroimaging data is playing an increasingly critical role in adult autism studies, and has revealed widespread structural and functional abnormalities. However, existing volume-based analysis tools developed for adult brains are ill-suited for infant studies, due to great challenges in brain tissue segmentation and ROI labeling, caused by the extremely low tissue contrast. To become an independent investigator on infant neuroimaging research, the candidate proposes in this K01 application to receive training in clinical phenomenology and child developmental cognitive neuroscience of children with ASD, developmental neurobiology and neurodevelopmental disorders, and biostatistics. These training activities will greatly augment the candidate's background in ASD, infant neuroimaging mapping and establish a solid foundation for his long-term goal of being a leading researcher on developing imaging-based early biological markers for autism. In the research plan, the candidate will create a unique suite of infant-specific, volume-based neuroimaging analysis tools that enable accurate characterization of early brain development in autistic infants, as well as improved capabilities in early identification of biomarkers and early diagnosis of at-risk infants. Specifically, a new method for unified skull stripping and tissue segmentation will be developed (Aim 1). Also, a new atlas-guided multi-channel forest learning will be proposed for ROI labeling (Aim 2). With the accurate tissue segmentation and ROI labeling, ROI-based volume measurements will be performed and used to identify early indicators or biomarker of risk for autism (Aim 3). Finally, early diagnosis of infants will be performed (Aim 4). Results from this research will help identify early biomarkers of risk for autism and also design targeted preemptive intervention strategies. All created tools and atlases will be integrated and released freely to the public, such as through NITRC (www.nitrc.org).