Autism spectrum disorder (ASD) affects approximately 1 in 70 children and is associated to a considerable burden for affected individuals, their families, and health care at large. As ASD remains not very well understood from a neuroscientific perspective, we currently lack diagnostic and intervention approaches that target potential biological mechanisms. The current project utilizes advanced computational neuroimaging techniques to investigate the connections between different brain areas and to study functional signals in individuals with ASD and typically developing controls. Our project is based on a new dataset that contains neuroimaging measures as well as behavioral and genetic data in hundreds of typically developing children and those with a psychiatric diagnosis like ASD.
We will capitalize on new techniques that can visualize whole-brain connectivity in a low dimensional space, which will give a simplified and accessible description of the spatial distribution of whole-brain network anomalies in autism. Using innovative computational models, we will furthermore predict how these changes in connectivity affect dynamic brain function and interrogate associations to mechanisms at the level of cortical microcircuits. Finally, we will relate our findings to spatial expression patterns of genes, and study how our connectivity findings are affected by genetic factors.
Our work will outline how changes in whole brain connectivity affect ongoing brain dynamics and how these relate to changes at the level of cortical microcircuits. Our study may furthermore identify genetic and gene expression factors that contribute to these whole-brain changes, which could potentially help to identify new treatments and to monitor their effects in future trials.