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

TL;DR

This study develops an interpretable machine learning approach using contrastive variational autoencoder to classify autism spectrum disorder in young children based on structural MRI features, providing insights into potential neuroanatomical biomarkers.

Contribution

It introduces a novel CVAE-based method for ASD classification in children aged 0.92 to 4.83 years, with interpretability and transfer learning strategies for small datasets.

Findings

Effective ASD classification in young children using CVAE features.

Identification of neuroanatomical biomarkers linked to ASD.

Transfer learning improves accuracy with limited data.

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 Magnetic Resonance Imaging (s-MRI). However, most studies involve with datasets where participants' age are above 5 and lack interpretability. In this paper, we propose a machine learning method for ASD classification in children with age range from 0.92 to 4.83 years, based on s-MRI features extracted using contrastive variational autoencoder (CVAE). 78 s-MRIs, 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. 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.