Research master thesis | Developmental Psychopathology in Education and Child Studies (research) (MSc)
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Background: Empathy deficits are evident in Autism Spectrum Disorder (ASD) and Conduct Disorder (CD), and are linked to distinct brain structural abnormalities. Criticisms over the years highlight...Show moreBackground: Empathy deficits are evident in Autism Spectrum Disorder (ASD) and Conduct Disorder (CD), and are linked to distinct brain structural abnormalities. Criticisms over the years highlight that the DSM classifications of psychiatric disorders are primarily based on observable signs and symptoms, insufficiently based on causes and (neuro)biology, and rather ignorant of heterogeneity and overlap in symptoms. The current study therefore aimed for classification of ASD and CD in adolescents, based on brain morphology (BM) and social-emotional functioning (SEF). Methods: The sample included boys with ASD (n = 23) or CD (n = 51), and typically developing boys (TD; n = 36), aged 15-19 years. Participants’ empathy, aggression, psychopathy, problem behaviours, social function/cognition, and brain morphology (using an MRI scanning) were assessed. For the prediction of ASD and CD, a predictive regression with cross-validation comparing three models was performed, followed by several LASSO regressions. For clustering participants, K-means clustering was used with three clusters and K determined by the CH-index and ARI, followed by ANOVAs, T-tests and checking nestedness with crosstabs. Results: ASD and CD DSM-5 diagnoses can most accurately be predicted with a model based on SEF data, and least accurately with a model based on BM data. When using only SEF data, similar to the DSM, classification is erroneous in approximately one fifth of the participants. For an optimal prediction of ASD and CD, a combination of background, SEF, and BM variables is necessary. None of the cluster-solutions, theory-driven (three clusters) or data-driven (SEF data: four clusters; BM data: five clusters), were congruent with the original DSM clustering (ASD, CD, and TD). Conclusion: The current study shows that data-driven classification, based on BM and/or SEF, is not sufficiently accurate or congruent with the DSM classifications. This suggests that the DSM classifications, which are mainly based on SEF data, do not capture ASD and CD well enough. In order to better capture these disorders, combining factors across multiple domains (including background and BM data), is necessary. On top of that, this study shows less explored ways of analysing data in the field of social sciences. As such, this study may represent a stepping stone for the development of more accurate classifications with less negative implications.Show less
