End-to-End Explainable AI: Derived Theory-of-Mind Fingerprints to Distinguish Between Autistic and Typically developing and Social Symptom Severity

Abstract

Theory-of-Mind (ToM) is an evolving ability that significantly impacts human learning and cognition. Early development of ToM ability allow one to comprehend other peoples aims and ambitions, as well as thinking that differs from ones own. Autism Spectrum Disorder (ASD) is the prevalent pervasive neurodevelopmental disorder in which participants brains appeared to be marked by diffuse variations throughout large-scale brain systems made up of functionally connected but physically separated brain areas that got abnormalities in willed action, self-monitoring and monitoring the intents of others, often known as ToM. Although functional neuroimaging techniques have been widely used to establish the neural correlates implicated in ToM, the specific mechanisms still need to be clarified. The availability of current Big data and Artificial Intelligence (AI) frameworks paves the way for systematically identifying Autistics from typically developing by identifying neural correlates and connectome-based features to generate accurate classifications and predictions of socio-cognitive impairment. In this work, we develop an Ex-AI model that quantifies the common sources of variability in ToM brain regions between typically developing and ASD individuals. Our results identify a feature set on which the classification model can be trained to learn characteristics differences and classify ASD and TD ToM development more distinctly. This approach can also estimate heterogeneity within ASD ToM subtypes and their association with the symptom severity scores based on socio-cognitive impairments. Based on our proposed framework, we obtain an average accuracy of more than 90 % using Explainable ML (Ex-Ml) models and an average of 96 % classification accuracy using Explainable Deep Neural Network (Ex-DNN) models. Our findings identify three important sub-groups within ASD samples based on the key differences and heterogeneity in resting state ToM regions functional connectivity patterns and predictive of mild to severe atypical social cognition and communication deficits through early developmental stages.