Contrastive machine learning reveals in EEG resting-state network salient features specific to autism spectrum disorder.

We explore the potential of the contrastive variational autoencoder to detect latent disorder-specific patterns in the network, analyzing functional brain networks in autistic individuals as the case. Autism spectrum disorder has long troubled medical practitioners, neurologists, and researchers. It is due to its extremely variable nature, both neurologically and behaviorally. Though machine learning has been in use to automate autism diagnosis, little has been done to delve into its intricacies. Here, we attempt to understand the neural mechanisms of autism spectrum disorder using contrastive variational autoencoder in conjunction with feature engineering. Our proposed methodology results in a physiologically interpretable classifier with a remarkable F1-score (up to 95%) and reveals a weak frontal lobe functional connectivity in the alpha band for children with autism spectrum disorder. Our study suggests an increased focus on efficient frontal lobe EEG sampling. Additionally, it highlights the importance of the proposed pipeline for understanding the underlying neural abnormalities in autism over the traditional machine learning pipeline. Thus, the obtained results have proven a contrastive variational autoencoder to be a promising approach for discovering latent patterns and features in complex networks. • Contrastive variational autoencoder discovers latent patterns in complex networks. • Contrastive machine learning disentangles autism-specific brain connectivity patterns. • Disentangled patterns achieved autism classification F1-score up to 95%. • Contrastive ML and feature engineering result in interpretable classifier. • Weak α -band frontal lobe functional connectivity is specific for children with autism. [ABSTRACT FROM AUTHOR]