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Published on: April 19, 2017
Volume-Based Analysis of 6-Month-Old Infant Brain MRI for Autism Biomarker Identification and Early Diagnosis
1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Insights
Researchers developed a novel method to detect autism spectrum disorder (ASD) in infants as young as six months old using brain MRI scans. This early detection aims to improve intervention timing for at-risk infants.
Area of Science:
- Neuroimaging
- Developmental Neuroscience
- Biomedical Engineering
Background:
- Autism spectrum disorder (ASD) diagnosis currently relies on behavioral observations, often occurring after the optimal window for early intervention.
- There is a critical need for early, objective biomarkers, particularly in infants, to facilitate timely diagnosis and intervention for ASD.
- Infant brain MRI segmentation is challenging due to low tissue contrast from ongoing myelination and maturation.
Purpose of the Study:
- To propose and validate a novel volume-based analysis for early detection of ASD risk in 6-month-old infants.
- To develop an accurate brain tissue segmentation method for infant MRI scans.
- To identify potential imaging-based biomarkers for early ASD diagnosis.
Main Methods:
- Proposed an anatomy-guided, densely-connected network for accurate segmentation of white matter, gray matter, and cerebrospinal fluid in 6-month-old infant brain MRI.
- Performed brain parcellation and statistical analysis on segmented MRI data.
- Utilized the National Database for Autism Research (NDAR) for experimental validation.
Main Results:
- The proposed method achieved high accuracy in segmenting infant brain tissues, overcoming challenges of low contrast.
- The volume-based analysis successfully identified significant regional differences between infants at risk of ASD and typically developing infants.
- Demonstrated superior segmentation and diagnostic accuracy compared to existing state-of-the-art methods.
Conclusions:
- The developed anatomy-guided network enables accurate brain MRI segmentation in young infants, crucial for early ASD detection.
- Volume-based analysis of infant brain MRI at 6 months of age shows promise as an imaging-based biomarker for ASD risk.
- This approach offers a potential pathway for earlier diagnosis and intervention, improving outcomes for infants at risk of ASD.
Abstract:
Autism spectrum disorder (ASD) is mainly diagnosed by the observation of core behavioral symptoms. Due to the absence of early biomarkers to detect infants either with or at-risk of ASD during the first postnatal year of life, diagnosis must rely on behavioral observations long after birth. As a result, the window of opportunity for effective intervention may have passed when the disorder is detected. Therefore, it is clinically urgent to identify imaging-based biomarkers for early diagnosis and intervention. In this paper, for the first time, we proposed a volume-based analysis of infant subjects with risk of ASD at very early age, i.e., as early as at 6 months of age. A critical part of volume-based analysis is to accurately segment 6-month-old infant brain MRI scans into different regions of interest, e.g., white matter, gray matter, and cerebrospinal fluid. This is actually very challenging since the tissue contrast at 6-month-old is extremely low, caused by inherent ongoing myelination and maturation. To address this challenge, we propose an anatomy-guided, densely-connected network for accurate tissue segmentation. Based on tissue segmentations, we further perform brain parcellation and statistical analysis to identify those significantly different regions between autistic and normal subjects. Experimental results on National Database for Autism Research (NDAR) show the advantages of our proposed method in terms of both segmentation accuracy and diagnosis accuracy over state-of-the-art results.
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