CORDIS - Resultados de investigaciones de la UE

Decomposing Heterogeneity in Autism Spectrum Disorders

Periodic Reporting for period 3 - AUTISMS (Decomposing Heterogeneity in Autism Spectrum Disorders)

Período documentado: 2020-12-01 hasta 2022-05-31

Autism spectrum disorders (ASD) affect 1-2% of the population and are a major public health issue. Heterogeneity between affected ASD individuals is substantial both at clinical and etiological levels, thus warranting the idea that we should begin characterizing the ASD population as multiple kinds of ‘autisms’. Without an advanced understanding of how heterogeneity manifests in ASD, it is likely that we will not make pronounced progress towards translational research goals that can have real impact on patient’s lives. This research program is focused on decomposing heterogeneity in ASD at multiple levels of analysis. The overall objectives are to understand how specific types of stratifiers could be meaningful in pointing to differential clinical outcomes and underlying biology. By enhancing the precision of our understanding about multiple subtypes of ASD this work will help accelerate progress towards the ideals of personalized medicine and help to reduce the burden of ASD on individuals, families, and society.
We will examine stratifiers such as sex/gender, early language outcome, early social engagement, and predictors of treatment response as ways of decomposing heterogeneity and the clinical level. We will also examine unsupervised stratification methods that learn about new distinctions in multivariate datasets that can uncover new ways to understand how heterogeneity manifests in autism. These unsupervised approaches will be employed at behavioral, neural, and genomic levels.
Very little is understood about how autism can be split up into different types of autisms. This work will go beyond the state of the art to uncover new and important distinctions behind the autisms and how different kinds of autisms are both clinically distinct and neurobiological different.