Antonio Maria Persico

The paper introduces a multiscale resource integrating genetics, brain imaging, and clinical data from autistic individuals and their relatives to explore genetic and neurobiological patterns in autism.

The paper identifies patterns of neural variability in autism using fMRI across tasks, finding greater global connectivity deviations and similar spatial patterns in autistic individuals compared to controls.

The paper identifies a multimodal brain pattern associated with autism by integrating gray and white matter morphology data and linking it to clinical symptoms.

The paper identifies atypical brain asymmetry in autism as a potential neurophenotype for stratification, showing individualized patterns linked to symptom severity and language delay.

The paper develops a phenotypic stratification model to predict autism subtypes based on SC-RRB balance and applies it to fMRI data, identifying subtype-specific neural circuitry and gene expression patterns.

This study identifies 99 genes associated with autism spectrum disorder through large-scale exome sequencing, showing differences in mutation frequencies between ASD and severe neurodevelopmental delay groups.