Striatal connectopic maps link to functional domains across psychiatric disorders

Transdiagnostic approaches to psychiatry have significant potential in overcoming the limitations of conventional diagnostic paradigms. However, while frameworks such as the Research Domain Criteria have garnered significant enthusiasm among researchers and clinicians from a theoretical angle, examples of how such an approach might translate in practice to understand the biological mechanisms underlying complex patterns of behaviors in realistic and heterogeneous populations have been sparse. In a richly phenotyped clinical sample (n = 186) specifically designed to capture the complex nature of heterogeneity and comorbidity within- and between stress- and neurodevelopmental disorders, we use exploratory factor analysis on a wide range of clinical questionnaires to identify four stable functional domains that transcend diagnosis and relate to negative valence, cognition, social functioning and inhibition/arousal before replicating them in an independent dataset (n = 188). We then use connectopic mapping to map inter-individual variation in fine-grained topographical organization of functional connectivity in the striatum-a central hub in motor, cognitive, affective and reward-related brain circuits-and use multivariate machine learning (canonical correlation analysis) to show that these individualized topographic representations predict transdiagnostic functional domains out of sample (r = 0.20, p = 0.026). We propose that investigating psychiatric symptoms across disorders is a promising path to linking them to underlying biology, and can help bridge the gap between neuroscience and clinical psychiatry.

Automated summary

Transdiagnostic approaches in psychiatry have the potential to overcome the limitations of traditional diagnostic paradigms. This study identified four stable functional domains that transcend diagnosis, related to negative valence, cognition, social functioning, and inhibition/arousal. The use of connectopic mapping and machine learning also showed promise in predicting transdiagnostic functional domains based on individualized topographic representations.