Network Model Replicates Personalized Brain Dynamics in Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is an extremely heterogeneous neurodevelopmental disorder on various levels (genetic, neurobiological, cognitive) and the underlying causes are still open to debate. Given this characterizing heterogeneity, the construction of a personalized model of the brain provides the optimal approach both for investigating underlying causes of ASD and for the design of personalized therapies. In this thesis, I implemented this personalized approach simulating the dynamics of cortical regions with a brain network model through the neuroinformatics platform The Virtual Brain. I coupled mesoscopic models of neural masses representing different areas of the cortex through a network of connections reconstructed from the anatomy of one pediatric ASD subject starting from MRI data. I was able to reconstruct the spectral properties of each area and the functional connectivity between them, which will allow validation of the model via EEG recordings. In parallel, a quantitative analysis of the empirical data revealed significant differences in both activity and connectivity of the ASD subject compared to one control pediatric patient. This allowed me to investigate the role that dynamical parameters andì local and global coupling play in determining ASD-related cortical activity. This work marks a major advance in brain modeling by introducing methodological novelties and paving the way to personalized approaches for neurodevelopmental diseases.