TY - JOUR
T1 - Reward Processing in Novelty Seekers
T2 - A Transdiagnostic Psychiatric Imaging Biomarker
AU - IMAGEN consortium
AU - Qi, Shile
AU - Schumann, Gunter
AU - Bustillo, Juan
AU - Turner, Jessica A.
AU - Jiang, Rongtao
AU - Zhi, Dongmei
AU - Fu, Zening
AU - Mayer, Andrew R.
AU - Vergara, Victor M.
AU - Silva, Rogers F.
AU - Iraji, Armin
AU - Chen, Jiayu
AU - Damaraju, Eswar
AU - Ma, Xiaohong
AU - Yang, Xiao
AU - Stevens, Michael
AU - Mathalon, Daniel H.
AU - Ford, Judith M.
AU - Voyvodic, James
AU - Mueller, Bryon A.
AU - Belger, Aysenil
AU - Potkin, Steven G.
AU - Preda, Adrian
AU - Zhuo, Chuanjun
AU - Xu, Yong
AU - Chu, Congying
AU - Banaschewski, Tobias
AU - Barker, Gareth J.
AU - Bokde, Arun L.W.
AU - Quinlan, Erin Burke
AU - Desrivières, Sylvane
AU - Flor, Herta
AU - Grigis, Antoine
AU - Garavan, Hugh
AU - Gowland, Penny
AU - Heinz, Andreas
AU - Martinot, Jean Luc
AU - Paillère Martinot, Marie Laure
AU - Artiges, Eric
AU - Nees, Frauke
AU - Orfanos, Dimitri Papadopoulos
AU - Paus, Tomáš
AU - Poustka, Luise
AU - Hohmann, Sarah
AU - Fröhner, Juliane H.
AU - Smolka, Michael N.
AU - Walter, Henrik
AU - Whelan, Robert
AU - Calhoun, Vince D.
AU - Sui, Jing
N1 - Publisher Copyright:
© 2021 Society of Biological Psychiatry
PY - 2021/10/15
Y1 - 2021/10/15
N2 - Background: 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. Methods: 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. Results: 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%. Conclusions: 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.
AB - Background: 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. Methods: 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. Results: 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%. Conclusions: 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.
KW - ADHD
KW - Attention-deficit/hyperactivity disorder
KW - MDD
KW - Major depressive disorders
KW - Novelty seeking
KW - Reward processing
KW - Schizophrenia
KW - Substance use
UR - http://www.scopus.com/inward/record.url?scp=85107030956&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107030956&partnerID=8YFLogxK
U2 - 10.1016/j.biopsych.2021.01.011
DO - 10.1016/j.biopsych.2021.01.011
M3 - Article
C2 - 33875230
AN - SCOPUS:85107030956
SN - 0006-3223
VL - 90
SP - 529
EP - 539
JO - Biological psychiatry
JF - Biological psychiatry
IS - 8
ER -