Aggressive, violent behaviour is a major burden and challenge for society. It has been linked to deficits in social understanding, but the evidence is inconsistent and the specifics of such deficits are unclear. Here, we investigated affective (empathy) and cognitive (Theory of Mind) routes to understanding other people in aggressive individuals. Twenty-nine men with a history of legally relevant aggressive behaviour (i.e. serious assault) and 32 control participants were tested using a social video task (EmpaToM) that differentiates empathy and Theory of Mind and completed questionnaires on aggression and alexithymia. Aggressive participants showed reduced empathic responses to emotional videos of others’ suffering, which correlated with aggression severity. Theory of Mind performance, in contrast, was intact. A mediation analysis revealed that reduced empathy in aggressive men was mediated by alexithymia. These findings stress the importance of distinguishing between socio-affective and socio-cognitive deficits for understanding aggressive behaviour and thereby contribute to the development of more efficient treatments.
PurposePhenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists.MethodsHere, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds.ResultsThe additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20–89% and the top 10 accuracy rate by more than 5–99% for the disease-causing gene.ConclusionImage analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis.
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