Km Bhavna

The paper identifies distinct transcriptomic and hierarchical brain organization associated with hyperconnectivity and hypoconnectivity in autism spectrum disorder, showing they are separate phenomena rather than opposing aspects of a single process.

This paper introduces an explainable deep learning framework to decode brain states and predict individual performance in false-belief tasks using functional connectivity data from early childhood.

This study examines the temporal stability of Theory of Mind and Pain networks in children and adults during naturalistic viewing, finding distinct stability patterns and age-related increases in stability and specialization.

The paper develops an explainable AI model to distinguish between autistic and typically developing individuals based on ToM brain connectivity patterns, achieving high classification accuracy.