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

> **NIH NIH K01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2020 · $142,913

## Abstract

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).

## Key facts

- **NIH application ID:** 9829113
- **Project number:** 5K01MH109773-04
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Li Wang
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $142,913
- **Award type:** 5
- **Project period:** 2017-01-15 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9829113

## Citation

> US National Institutes of Health, RePORTER application 9829113, Volume-based Analysis of 6-month Infant Brain MRI for Autism Biomarker Identification and Early Diagnosis (5K01MH109773-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9829113. Licensed CC0.

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