Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine. Issue 8 (15th July 2021)

• Record Type: Journal Article
• Authors: Squarcina, Letizia; Nosari, Guido; Marin, Riccardo; Castellani, Umberto; Bellani, Marcella; Bonivento, Carolina; Fabbro, Franco; Molteni, Massimo; Brambilla, Paolo
• Is Part Of: Brain and behavior. Volume 11:Issue 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-15
• Subjects: autism spectrum disorder -- cortical thickness -- magnetic resonance imaging -- supervised machine learning -- support vector machine; Neurology -- Periodicals; Neurosciences -- Periodicals; Psychology -- Periodicals; Psychiatry -- Periodicals; 616.8005
• DOI: 10.1002/brb3.2238
• Languages: English
• ISSNs: 2162-3279

Abstract

Abstract: Objective: Autism spectrum disorder (ASD) is a neurodevelopmental condition with a heterogeneous phenotype. The role of biomarkers in ASD diagnosis has been highlighted; cortical thickness has proved to be involved in the etiopathogenesis of ASD core symptoms. We apply support vector machine, a supervised machine learning method, in order to identify specific cortical thickness alterations in ASD subjects. Methods: A sample of 76 subjects (9.5 ± 3.4 years old) has been selected, 40 diagnosed with ASD and 36 typically developed subjects. All children underwent a magnetic resonance imaging (MRI) examination; T1‐MPRAGE sequences were analyzed to extract features for the characterization and parcellation of regions of interests (ROI); average cortical thickness (CT) has been measured for each ROI. For the classification process, the extracted features were used as input for a classifier to identify ASD subjects through a "learning by example" procedure; the features with best performance was then selected by "greedy forward‐feature selection." Finally, this model underwent a leave‐one‐out cross‐validation approach. Results: From the training set of 68 ROIs, five ROIs reached accuracies of over 70%. After this phase, we used a recursive feature selection process in order to identify the eight features with the best accuracy (84.2%). CT resulted higher in ASD compared to controls in all the ROIs identified at the end of the process. Conclusion: We found increased CT in various brain regions in ASD subjects, confirming their role in the pathogenesis of this condition. Considering the brain development curve during ages, these changes in CT may normalize during development. Further validation on a larger sample is required. Abstract : Cortical thickness has been proven to be involved in the etiopathogenesis of autism. We use automatic classification to cortical thickness data in order to investigate its use as a biomarker of autism. Five regions of interest reached accuracies of over 70%, and we reached an accuracy of 84.2% concatenating eight regions. Cortical thickness resulted higher in autism compared to controls, suggesting a role in the pathogenesis of autism.