Grant: CIHR Fellowship

Autism is an heterogeneous spectrum which needs a deeper comprehension to achieve personalized understanding. However, research efforts in this direction so far does not investigate the specific profiles of autistic females, who are often under-detected and thus under-studied, resulting in missed diagnoses and reduced access to support. Leveraging clinical and brain functional connectivity data from large databases, we will use statistical modeling and machine learning to better understand the heterogeneity within autistic females and to investigate how diagnosed autistic females and females with subthreshold symptoms fit within the autism spectrum. The aim is to reveal potential quantitative and qualitative differences between autistic males and females and to identify clinical patterns on which clinicians should focus to better diagnose and support autistic females.