Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker
Dysfunctional 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. A subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was use...
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Published in | Biological psychiatry (1969) Vol. 90; no. 8; pp. 529 - 539 |
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Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Elsevier Inc
15.10.2021
Elsevier |
Subjects | |
Online Access | Get full text |
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Summary: | Dysfunctional 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.
A 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.
Multimodal 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%.
Adolescents 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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 PMCID: PMC8322149 SQ and JS designed the study. SQ performed the data analysis and wrote the paper. JS, GS, JB, JAT, and VDC revised the paper. TB, GJB, ALWB, EBQ, SD, HF, AG, HG, PG, AH, J-LM, M-LPM, EA, FN, DPO, TP, LP, SH, JHF, MNS, HW, RW, and the IMAGEN Consortium (see the full collaborator list in the Supplement) contributed the multimodal imaging data from the IMAGEN cohort; VMV contributed to the multimodal imaging data for alcohol drinking and smoking cohorts; MS contributed to the multimodal imaging data for the ADHD cohort; XM and XY contributed to the multimodal imaging data for the MDD cohort; JAT, DHM, JMF, JV, BAM, AB, SGP, and AP contributed to the multimodal imaging data for the SZ cohort. ZF, RJ, DZ, and ED helped with data preprocessing. All authors contributed to the results’ interpretation and discussion and approved the final manuscript. |
ISSN: | 0006-3223 1873-2402 1873-2402 |
DOI: | 10.1016/j.biopsych.2021.01.011 |