Multivariate Searchlight Classification of Structural Magnetic Resonance Imaging in Children and Adolescents with Autism
Open accessAbstract
Background Autism spectrum disorders (ASD) are neurodevelopmental disorders with a prevalence of nearly 1:100. Structural imaging studies point to disruptions in multiple brain areas, yet the precise neuroanatomical nature of these disruptions remains unclear. Characterization of brain structural differences in children with ASD is critical for development of biomarkers that may eventually be used to improve diagnosis and monitor response to treatment.Methods We use voxel-based morphometry along with a novel multivariate pattern analysis approach and searchlight algorithm to classify structural magnetic resonance imaging data acquired from 24 children and adolescents with autism and 24 age-, gender-, and IQ-matched neurotypical participants.Results Despite modest voxel-based morphometry differences, multivariate pattern analysis revealed that the groups could be distinguished with accuracies of approximately 90% based on gray matter in the posterior cingulate cortex, medial prefrontal cortex, and bilateral medial temporal lobes-regions within the default mode network. Abnormalities in the posterior cingulate cortex were associated with impaired Autism Diagnostic Interview communication scores. Gray matter in additional prefrontal, lateral temporal, and subcortical structures also discriminated between groups with accuracies between 81% and 90%. White matter in the inferior fronto-occipital and superior longitudinal fasciculi, and the genu and splenium of the corpus callosum, achieved up to 85% classification accuracy.Conclusions Multiple brain regions, including those belonging to the default mode network, exhibit aberrant structural organization in children with autism. Brain-based biomarkers derived from structural magnetic resonance imaging data may contribute to identification of the neuroanatomical basis of symptom heterogeneity and to the development of targeted early interventions.
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In a study of children and adolescents with autism, gray matter within default mode network regions (posterior cingulate cortex and medial temporal lobes: 92%, medial prefrontal cortex: 88%) could be used to discriminate between clinical and control groups with high accuracy.
“Using SVM searchlight classification, this study found that gray matter within default mode network regions (posterior cingulate cortex and medial temporal lobes: 92%, medial prefrontal cortex: 88%) could be used to discriminate between clinical and control groups with high accuracy.28”
From Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder (2017) · cites this paper as [28]
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- Authors
- Uddin, L. Q., Menon, V., Young, C. B., Ryali, S., Chen, T., Khouzam, A., Minshew, N. J., & Hardan, A. Y.
- Affiliations
- University of Pittsburgh (US)
- Stanford University (US)
- Corresponding author
- Menon, Vinod
- ORCID
- Venue
- Biological Psychiatry
- Publisher
- Elsevier BV
- Published
- 2011-11
- Volume
- 70
- Issue
- 9
- Pages
- 833-841
- ISSN
- 0006-3223 (ISSN-L)
- PMC
- PMC3191298
- OpenAlex
- W2075676600
- Cited by
- 187
- References
- 71
- Citations by year
- 2025: 6 · 2024: 12 · 2023: 4 · 2022: 15 · 2021: 10 · 2020: 13 · 2019: 9 · 2018: 15 · 2017: 18 · 2016: 18 · 2015: 17 · 2014: 17 · 2013: 17 · 2012: 15
- FWCI
- 7.96
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Biological Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Singer Foundation
- Stanford Institute for Neuro-Innovation & Translational Neurosciences
- National Institute of Child Health & Human Development (HD047520, HD059205, HD 35469, HD055748)
- National Institute of Deafness & Other Communication Disorders (DC0111095)
- National Institute of Mental Health (MH084164, MH64027)
- National Science Foundation (BCS/DRL 0750340)
- Mosbacher Postdoctoral Fellowship and National Institute of Mental Health (K01MH092288)
- National Institute of Neurological Disorders and Stroke (NS33355)
- Bristol-Myers Squibb
- 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, structural MRI, multivariate pattern analysis, voxel-based morphometry, default mode network, posterior cingulate cortex, medial prefrontal cortex, medial temporal lobe, gray matter, white matter, corpus callosum, inferior fronto-occipital fasciculus, superior longitudinal fasciculus, children and adolescents, symptom heterogeneity
- MeSH terms
- Adolescent, Autistic Disorder, Child, Data Interpretation, Statistical, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Neuropsychological Tests, Psychiatric Status Rating Scales, Support Vector Machine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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