Diagnosis of autism spectrum disorders using regional and interregional morphological features: Diagnosis of ASD Using Morphological Features
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This article describes a novel approach to identify autism spectrum disorder (ASD) utilizing regional and interregional morphological patterns extracted from structural magnetic resonance images. Two types of features are extracted to characterize the morphological patterns: (1) Regional features, which includes the cortical thickness, volumes of cortical gray matter, and cortical-associated white matter regions, and several subcortical structures extracted from different regions-of-interest (ROIs); (2) Interregional features, which convey the morphological change pattern between pairs of ROIs. We demonstrate that the integration of regional and interregional features via multi-kernel learning technique can significantly improve the classification performance of ASD, compared with using either regional or interregional features alone. Specifically, the proposed framework achieves an accuracy of 96.27% and an area of 0.9952 under the receiver operating characteristic curve, indicating excellent diagnostic power and generalizability. The best performance is achieved when both feature types are weighted approximately equal, indicating complementary between these two feature types. Regions that contributed the most to classification are in line with those reported in the previous studies, particularly the subcortical structures that are highly associated with human emotional modulation and memory formation. The autistic brains demonstrate a significant rightward asymmetry pattern particularly in the auditory language areas. These findings are in agreement with the fact that ASD is a behavioral- and language-related neurodevelopmental disorder. By concurrent consideration of both regional and interregional features, the current work presents an effective means for better characterization of neurobiological underpinnings of ASD that facilitates its identification from typically developing children.
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A more recent study of children used multi-kernal SVM combining regional and interregional features to achieve 96% classification accuracy.
“A more recent study of children used a unique approach, multi-kernal SVM, combining regional (cortical thickness, gray matter volume) and interregional (morphological change patterns between pairs of ROIs) features to achieve 96% classification accuracy.31”
From Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder (2017) · cites this paper as [31]
In that study, the neuroanatomical features contributing most to classification were subcortical structures including putamen and accumbens.
“In this study, the neuroanatomical features contributing the most to classification were subcortical structures including putamen and accumbens, unlike the studies in older individuals.”
From Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder (2017) · cites this paper as [31]
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- Authors
- Wee, C.-Y., Wang, L., Shi, F., Yap, P.-T., & Shen, D.
- Affiliations
- Image Display; Enhancement; and Analysis (IDEA) Laboratory; Biomedical Research Imaging Center (BRIC); Department of Radiology; University of North Carolina at Chapel Hill; Chapel Hill North Carolina
- University of North Carolina at Chapel Hill (US)
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2013-11-06
- Volume
- 35
- Issue
- 7
- Pages
- 3414-3430
- ISSN
- 1065-9471 (ISSN-L)
- PMC
- PMC4109659
- OpenAlex
- W2123199678
- Cited by
- 89
- References
- 134
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 6 · 2023: 4 · 2022: 5 · 2021: 8 · 2020: 11 · 2019: 16 · 2018: 9 · 2017: 8 · 2016: 10 · 2015: 7
- FWCI
- 3.10
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Human Brain Mapping (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Health (EB006733, EB008374, EB009634, MH088520, AG041721)
- National Research Foundation grant funded by the Korean government (2012-005741)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
- Keywords
- autism spectrum disorder, ASD diagnosis, brain morphometry, cortical thickness, gray matter volume, white matter, subcortical structures, multiple kernel learning, brain asymmetry, neuroimaging classification
- MeSH terms
- Adolescent, Adult, Brain, Brain Mapping, Child, Child Development Disorders, Pervasive, Child, Preschool, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Neural Pathways, Young Adult
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