Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker

Abstract

BackgroundDysfunctional reward processing is implicated in multiple mental disorders. Novelty seeking (NS) assesses preference for seeking novel experiences, which is linked to sensitivity to reward environmental cues. MethodsA subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was used to identify high-NS–associated multimodal components by supervised fusion. These features were then used to longitudinally predict five different risk scales for the same and unseen subjects (an independent dataset of subjects at 19 years of age that was not used in predictive modeling training at 14 years of age) (within IMAGEN, n ≈1100) and even for the corresponding symptom scores of five types of patient cohorts (non-IMAGEN), including drinking (n = 313), smoking (n = 104), attention-deficit/hyperactivity disorder (n = 320), major depressive disorder (n = 81), and schizophrenia (n = 147), as well as to classify different patient groups with diagnostic labels. ResultsMultimodal biomarkers, including the prefrontal cortex, striatum, amygdala, and hippocampus, associated with high NS in 14-year-old adolescents were identified. The prediction models built on these features are able to longitudinally predict five different risk scales, including alcohol drinking, smoking, hyperactivity, depression, and psychosis for the same and unseen 19-year-old adolescents and even predict the corresponding symptom scores of five types of patient cohorts. Furthermore, the identified reward-related multimodal features can classify among attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia with an accuracy of 87.2%. ConclusionsAdolescents with higher NS scores can be used to reveal brain alterations in the reward-related system, implicating potential higher risk for subsequent development of multiple disorders. The identified high-NS–associated multimodal reward-related signatures may serve as a transdiagnostic neuroimaging biomarker to predict disease risks or severity.