🤖 AI Summary
This study addresses the challenge of early autism spectrum disorder (ASD) screening in toddlers aged 0.92–4.83 years. We propose an interpretable classification framework for structural MRI (s-MRI) data. Methodologically, we introduce a novel contrastive variational autoencoder (CVAE) to disentangle ASD-specific neuroanatomical features from general developmental patterns; integrate transfer learning to enhance robustness under limited-sample conditions; and perform cortical surface area association analysis to ensure neuroanatomically grounded interpretability. Evaluated on 78 s-MRI scans from Shenzhen Children’s Hospital, our model significantly discriminates ASD from typically developing children and identifies the frontal and temporal cortices as potential biomarkers. To our knowledge, this is the first work to apply contrastive generative modeling to low-age ASD neuroimaging classification—achieving both high discriminative performance and clinical interpretability—thereby establishing a new paradigm for targeted early intervention.
📝 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.
