ArticleInternational journal of methods in psychiatric research2020
Autism classified by magnetic resonance imaging: A pilot study of a potential diagnostic tool.
Article in International journal of methods in psychiatric research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Preliminary testing of a prespecified liability architecture for autism: theory-guided pathogenetic triad models outperform strength-matched alternatives.Frontiers in psychiatry · 2026Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- The Role of Structure MRI in Diagnosing Autism.Diagnostics (Basel, Switzerland) · 2022Article
- Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review.Frontiers in molecular neuroscience · 2022Review
- Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine.Brain and behavior · 2021Article
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Authors and funding
5 authors.
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Abstract
objectivesIndividual anatomical biomarkers have limited power for the classification of autism. The present study introduces a multivariate classification approach using structural magnetic resonance imaging data from individuals with and without autism.
methodsThe classifier utilizes z-normalization, parameter weighting, and interindividual comparison on brain segmentation data, for estimation of an individual summed total index (TI). The TI indicates whether the gross morphological pattern of each individual's brain is in the direction of cases or controls.
resultsMorphometric analysis found significant differences within subcortical gray matter structures and limbic areas. There was no significant difference in total brain volume. A case-control pilot-study of TIs in normally intelligent individuals with autism (24) and without (21) yielded a maximal accuracy of 78.9% following cross-validation. It showed a high accuracy compared with machine learning methods when tested on the same dataset. The TI correlated well with the autism quotient (R = 0.51) across groups.
conclusionThese results are on par with studies on autism using machine learning. The main contributions are its transparency and simplicity. The possibility of including additional neuroimaging data further increases the potential of the classifier as a diagnostic aid for neuropsychiatric disorders, as well as a research tool for neuroscientific investigations.
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