Autism is a highly heterogeneous condition, as reflected by its pronounced individual differences in behavior, cognition, and co-morbidities, as well as by its sex disparities (boys are more frequently diagnosed with autism than girls). Since these differences likely have a neurobiological basis, leveraging information from the brain may advance our understanding of autism by improving the characterization of its heterogeneity. In this talk, Javier Rasero will discuss recent advances from his research in this direction, combining multimodal brain data, novel biomarkers, and integrative data science methods. 

Javier Rasero's current research focuses on predicting individual differences in both clinical and preclinical populations, with a particular emphasis on uncovering the neurobiological basis of the observed heterogeneity in autism and on examining the relationship between the brain and physical health. To achieve this, he leverages brain data—particularly connectivity measures derived from neuroimaging—together with machine learning methods.