Скачать или смотреть Lay talk of my doctoral thesis focussed on individual prediction of Autism Spectrum Disorder

To enable early targeted intervention for Autism Spectrum Disorder (ASD), it is important to identify the infants who need intervention early in life. Group differences tell us little if anything about the individual infant as there can be significant overlap between groups in individual variation. This research aimed to translate group differences to prediction of ASD outcome in the first years of life at the level of the individual infant. First, we integrated data from different domains and different modalities to identify infants who later meet criteria for an ASD diagnosis. Then, we focused on the prospective investigation of its heterogeneity. Results indicate that brain data, as opposed to behavioural and developmental data, retain the highest value for early detection of ASD. Furthermore, we identified subgroups with different functional outcomes among high-risk siblings and low-risk controls, and uncovered underlying processes acting together early in development and associated to ASD outcome.