In the current project, Sophie Molholm and colleagues plan to test the potential of neuro-oscillations, measured under a range of different task conditions, to serve as a sensitive and reliable measure of alterations in brain function in autism spectrum disorder (ASD). The approach will involve collecting scalp-recordings of EEG, as well as clinical and cognitive data (from children with ASD, unaffected siblings of children with ASD and age and sex matched typically developing children). Machine learning analyses will be used to assess which combinations of neural measures provide the most robust and reliable biomarkers of clinical phenotype.