Autism Spectrum Disorder Classification in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder

• URL: http://arxiv.org/abs/2307.00976v1
• Date: Mon, 3 Jul 2023 12:46:19 GMT
• Title: Autism Spectrum Disorder Classification in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder
• Authors: Ruimin Ma, Ruitao Xie, Yanlin Wang, Jintao Meng, Yanjie Wei, Wenhui Xi, Yi Pan
• Abstract summary: Autism spectrum disorder (ASD) is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients. With the development of the machine learning and neuroimaging technology, extensive research has been conducted on machine classification of ASD based on structural MRI (s-MRI) Few studies conduct machine classification of ASD for participants below 5-year-old, but, with mediocre predictive accuracy.
• Score: 5.2927782596213
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Autism spectrum disorder (ASD) is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients, making early screening and intervention of ASD critical. With the development of the machine learning and neuroimaging technology, extensive research has been conducted on machine classification of ASD based on structural MRI (s-MRI). However, most studies involve with datasets where participants' age are above 5. Few studies conduct machine classification of ASD for participants below 5-year-old, but, with mediocre predictive accuracy. In this paper, we push the boundary of predictive accuracy (above 0.97) of machine classification of ASD in children (age range: 0.92-4.83 years), based on s-MRI features extracted using contrastive variational autoencoder (CVAE). 78 s-MRI, collected from Shenzhen Children's Hospital, are used for training CVAE, which consists of both ASD-specific feature channel and common shared feature channel. The ASD participants represented by ASD-specific features can be easily discriminated from TC participants represented by the common shared features, leading to high classification accuracy. In case of degraded predictive accuracy when data size is extremely small, a transfer learning strategy is proposed here as a potential solution. Finally, we conduct neuroanatomical interpretation based on the correlation between s-MRI features extracted from CVAE and surface area of different cortical regions, which discloses potential biomarkers that could help target treatments of ASD in the future.

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