Bhavna, K. | Search bioRxiv

bioRxiv2023

Developmental stability and segregation of Theory of Mind and Pain networks carry distinct temporal signatures during naturalistic viewing

Temporally stable large-scale functional brain connectivity among distributed brain regions is crucial during brain development. Recently, many studies highlighted an association between temporal dynamics during development and their alterations across various time scales. However, systematic characterization of temporal stability patterns of brain networks that represent the bodies and minds of others in children remains unexplored. To address this, we apply an unsupervised approach to reduce high-dimensional dynamic functional connectivity (dFC) features via low-dimensional patterns and characterize temporal stability using quantitative metrics across neurodevelopment. This study characterizes the development of temporal stability of the Theory of Mind (ToM) and Pain networks to address the functional maturation of these networks. The dataset used for this investigation comprised 155 subjects (children (n=122, 3-12 years) and adults (n=33)) watching engaging movie clips while undergoing fMRI data acquisition. The movie clips highlighted cartoon characters and their bodily sensations (often pain) and mental states (beliefs, desires, emotions) of others, activating ToM and Pain network regions of young children. Our findings demonstrate that ToM and pain networks display distinct temporal stability patterns by age 3 years. Finally, the temporal stability and specialization of the two functional networks increase with age and predict ToM behavior.

neuroscience↗

bioRxiv2023

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

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.

neuroscience↗