Show simple item record

dc.contributor.authorLombardo, Michael V.en
dc.contributor.authorLai, Meng-Chuanen
dc.contributor.authorBaron-Cohen, Simonen
dc.creatorLombardo, Michael V.en
dc.creatorLai, Meng-Chuanen
dc.creatorBaron-Cohen, Simonen
dc.description.abstractAutism is a diagnostic label based on behavior. While the diagnostic criteria attempt to maximize clinical consensus, it also masks a wide degree of heterogeneity between and within individuals at multiple levels of analysis. Understanding this multi-level heterogeneity is of high clinical and translational importance. Here we present organizing principles to frame research examining multi-level heterogeneity in autism. Theoretical concepts such as ‘spectrum’ or ‘autisms’ reflect non-mutually exclusive explanations regarding continuous/dimensional or categorical/qualitative variation between and within individuals. However, common practices of small sample size studies and case–control models are suboptimal for tackling heterogeneity. Big data are an important ingredient for furthering our understanding of heterogeneity in autism. In addition to being ‘feature-rich’, big data should be both ‘broad’ (i.e., large sample size) and ‘deep’ (i.e., multiple levels of data collected on the same individuals). These characteristics increase the likelihood that the study results are more generalizable and facilitate evaluation of the utility of different models of heterogeneity. A model’s utility can be measured by its ability to explain clinically or mechanistically important phenomena, and also by explaining how variability manifests across different levels of analysis. The directionality for explaining variability across levels can be bottom-up or top-down, and should include the importance of development for characterizing changes within individuals. While progress can be made with ‘supervised’ models built upon a priori or theoretically predicted distinctions or dimensions of importance, it will become increasingly important to complement such work with unsupervised data-driven discoveries that leverage unknown and multivariate distinctions within big data. A better understanding of how to model heterogeneity between autistic people will facilitate progress towards precision medicine for symptoms that cause suffering, and person-centered support.en
dc.sourceMolecular Psychiatryen
dc.titleBig data approaches to decomposing heterogeneity across the autism spectrumen
dc.description.endingpage1450Σχολή Κοινωνικών Επιστημών και Επιστημών Αγωγής / Faculty of Social Sciences and EducationΤμήμα Ψυχολογίας / Department of Psychology
dc.contributor.orcidLombardo, Michael V. [0000-0001-6780-8619]

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record