Structural brain morphometry as classifier and predictor of ADHD and reward-related comorbidities - van Rooij, Daan; Zhang-James, Yanli; Buitelaar, Jan; Faraone, Stephen V.; Reif, Andreas; Grimm, Oliver (Frontiers in Psychiatry, 2022)
ADHD is one of the most common neurodevelopmental disorders, and around two-thirds of affected children report persisting problems into adulthood. This negative trajectory is associated with high comorbidity with disorders like obesity, depression or substance use disorder (SUD). On their own, a decrease in cortical volume and thickness has also been reported in depression, SUD and obesity, but it is unclear whether these structural brain alterations represent unique disorder-specific profiles. A transdiagnostic exploration of ADHD and typical comorbid disorders could help to understand whether specific morphometric brain changes are due to ADHD, or, alternatively, to these comorbid disorders. In the current study, we studied brain morphometry in 136 subjects with ADHD with and without comorbid depression, SUD and obesity, to test whether there are unique or common brain alterations. We employed a machine-learning-algorithm trained to classify subjects with ADHD in the large ENIGMA-ADHD- dataset, and used it to predict the diagnostic status of the subjects with ADHD and/or comorbidities. Parcellation analysis demonstrated decreased cortical thickness in medial prefrontal areas, that was associated with the presence of any comorbidity. However, these results did not survive correction for multiple comparisons. Similarly, the machine learning analysis indicated that the predictive algorithm grouped most of our ADHD participants as belonging to the ADHD-group, but no systematic differences between comorbidity status came up. In sum, a ML-model based on the ADHD ENIGMA data set does not differentiate between ADHD with and without comorbidities according to classification accuracy based on structural brain morphometry. As the ML model is based in part on adolescent brains, this might indicate that comorbid disorders and their brain changes are not captured by the ML-model because it represents a different developmental brain trajectory.