Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker

IMAGEN consortium

doi:10.1016/j.biopsych.2021.01.011

Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker

IMAGEN consortium

Psychiatry & Behavioral Sciences

Research output: Contribution to journal › Article › peer-review

44 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	529-539
Number of pages	11
Journal	Biological Psychiatry
Volume	90
Issue number	8
Early online date	Jan 30 2021
DOIs	https://doi.org/10.1016/j.biopsych.2021.01.011
State	Published - Oct 15 2021

Bibliographical note

Funding Information:
This work was supported by the China Natural Science Foundation (Grant Nos. 82022035 and 61773380 [to JS]), the National Institutes of Health (NIH Grant Nos. R01EB005846 , R01MH117107 , P20GM103472 , and P30GM122734 [to VDC]), the National Science Foundation (Grant No. 1539067 [to VDC]), Beijing Municipal Science and Technology Commission (Grant No. Z181100001518005 [to JS]), the European Union–funded FP6 Integrated Project IMAGEN (Reinforcement-related behavior in normal brain function and psychopathology) (Grant No. LSHM-CT-2007-037286 [to GS]), the Horizon 2020–funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders) (Grant No. 695313 [to GS]), ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways) (Grant No. PR-ST-0416-10004), BRIDGET (JPND: BRain Imaging, cognition Dementia and next generation GEnomics) (Grant No. MR/N027558/1), Human Brain Project (HBP SGA 2, Grant No. 785907), the FP7 project MATRICS (Grant No. 603016), the Medical Research Council Grant ‘c-VEDA’ (Consortium on Vulnerability to Externalizing Disorders and Addictions) (Grant No. MR/N000390/1), the National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, the Bundesministerium für Bildung und Forschung (Grant Nos. 01GS08152 and 01EV0711; Forschungsnetz AERIAL Grant Nos. 01EE1406A and 01EE1406B), the Deutsche Forschungsgemeinschaft (DFG Grant Nos. SM 80/7-2, SFB 940/2, and NE 1383/14-1), the Medical Research Foundation and Medical Research Council (Grant Nos. MR/R00465X/1 and MR/S020306/1), and the NIH-funded Enhancing Neuro Imaging Genetics through Meta Analysis (Grant Nos. 5U54EB020403-05 and 1R56AG058854-01). Further support was provided by grants from ANR (project AF12-NEUR0008-01 - WM2NA and ANR-12-SAMA-0004), the Fondation de France, the Fondation pour la Recherche Médicale, the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-Hôpitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012; the NIH, Science Foundation Ireland (Grant No. 16/ERCD/3797), U.S.A. (Axon, Testosterone and Mental Health during Adolescence; Grant No. RO1 MH085772-01A1), and by NIH Consortium Grant No. U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence.

Funding Information:
This work was supported by the China Natural Science Foundation (Grant Nos. 82022035 and 61773380 [to JS]), the National Institutes of Health (NIH Grant Nos. R01EB005846, R01MH117107, P20GM103472, and P30GM122734 [to VDC]), the National Science Foundation (Grant No. 1539067 [to VDC]), Beijing Municipal Science and Technology Commission (Grant No. Z181100001518005 [to JS]), the European Union?funded FP6 Integrated Project IMAGEN (Reinforcement-related behavior in normal brain function and psychopathology) (Grant No. LSHM-CT-2007-037286 [to GS]), the Horizon 2020?funded ERC Advanced Grant ?STRATIFY? (Brain network based stratification of reinforcement-related disorders) (Grant No. 695313 [to GS]), ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways) (Grant No. PR-ST-0416-10004), BRIDGET (JPND: BRain Imaging, cognition Dementia and next generation GEnomics) (Grant No. MR/N027558/1), Human Brain Project (HBP SGA 2, Grant No. 785907), the FP7 project MATRICS (Grant No. 603016), the Medical Research Council Grant ?c-VEDA? (Consortium on Vulnerability to Externalizing Disorders and Addictions) (Grant No. MR/N000390/1), the National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, the Bundesministerium f?r Bildung und Forschung (Grant Nos. 01GS08152 and 01EV0711; Forschungsnetz AERIAL Grant Nos. 01EE1406A and 01EE1406B), the Deutsche Forschungsgemeinschaft (DFG Grant Nos. SM 80/7-2, SFB 940/2, and NE 1383/14-1), the Medical Research Foundation and Medical Research Council (Grant Nos. MR/R00465X/1 and MR/S020306/1), and the NIH-funded Enhancing Neuro Imaging Genetics through Meta Analysis (Grant Nos. 5U54EB020403-05 and 1R56AG058854-01). Further support was provided by grants from ANR (project AF12-NEUR0008-01 - WM2NA and ANR-12-SAMA-0004), the Fondation de France, the Fondation pour la Recherche M?dicale, the Mission Interminist?rielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-H?pitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012; the NIH, Science Foundation Ireland (Grant No. 16/ERCD/3797), U.S.A. (Axon, Testosterone and Mental Health during Adolescence; Grant No. RO1 MH085772-01A1), and by NIH Consortium Grant No. U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence. 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. See the Supplement for the full list of IMAGEN Consortium collaborators. The supervised fusion code has been released and integrated in the Fusion ICA Toolbox (FIT, https://trendscenter.org/software/fit), which can be downloaded and used directly by users worldwide. The IMAGEN and ADHD multimodal data used in this study can be accessed upon application from IMAGEN and ADHD-200 Consortium. The SZ, MDD, drinking, and smoking data can be accessed upon request to the corresponding authors. The authors report no biomedical financial interests or potential conflicts of interest.

Publisher Copyright:
© 2021 Society of Biological Psychiatry

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Keywords

ADHD
Attention-deficit/hyperactivity disorder
MDD
Major depressive disorders
Novelty seeking
Reward processing
Schizophrenia
Substance use

Publisher link

10.1016/j.biopsych.2021.01.011

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Cite this

@article{c7df4094284848eea629769ac3df1b29,

title = "Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker",

abstract = "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.",

keywords = "ADHD, Attention-deficit/hyperactivity disorder, MDD, Major depressive disorders, Novelty seeking, Reward processing, Schizophrenia, Substance use",

author = "\{IMAGEN consortium\} and Shile Qi and Gunter Schumann and Juan Bustillo and Turner, \{Jessica A.\} and Rongtao Jiang and Dongmei Zhi and Zening Fu and Mayer, \{Andrew R.\} and Vergara, \{Victor M.\} and Silva, \{Rogers F.\} and Armin Iraji and Jiayu Chen and Eswar Damaraju and Xiaohong Ma and Xiao Yang and Michael Stevens and Mathalon, \{Daniel H.\} and Ford, \{Judith M.\} and James Voyvodic and Mueller, \{Bryon A.\} and Aysenil Belger and Potkin, \{Steven G.\} and Adrian Preda and Chuanjun Zhuo and Yong Xu and Congying Chu and Tobias Banaschewski and Barker, \{Gareth J.\} and Bokde, \{Arun L.W.\} and Quinlan, \{Erin Burke\} and Sylvane Desrivi{\`e}res and Herta Flor and Antoine Grigis and Hugh Garavan and Penny Gowland and Andreas Heinz and Martinot, \{Jean Luc\} and \{Paill{\`e}re Martinot\}, \{Marie Laure\} and Eric Artiges and Frauke Nees and Orfanos, \{Dimitri Papadopoulos\} and Tom{\'a}{\v s} Paus and Luise Poustka and Sarah Hohmann and Fr{\"o}hner, \{Juliane H.\} and Smolka, \{Michael N.\} and Henrik Walter and Robert Whelan and Calhoun, \{Vince D.\} and Jing Sui",

note = "Funding Information: This work was supported by the China Natural Science Foundation (Grant Nos. 82022035 and 61773380 [to JS]), the National Institutes of Health (NIH Grant Nos. R01EB005846 , R01MH117107 , P20GM103472 , and P30GM122734 [to VDC]), the National Science Foundation (Grant No. 1539067 [to VDC]), Beijing Municipal Science and Technology Commission (Grant No. Z181100001518005 [to JS]), the European Union–funded FP6 Integrated Project IMAGEN (Reinforcement-related behavior in normal brain function and psychopathology) (Grant No. LSHM-CT-2007-037286 [to GS]), the Horizon 2020–funded ERC Advanced Grant {\textquoteleft}STRATIFY{\textquoteright} (Brain network based stratification of reinforcement-related disorders) (Grant No. 695313 [to GS]), ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways) (Grant No. PR-ST-0416-10004), BRIDGET (JPND: BRain Imaging, cognition Dementia and next generation GEnomics) (Grant No. MR/N027558/1), Human Brain Project (HBP SGA 2, Grant No. 785907), the FP7 project MATRICS (Grant No. 603016), the Medical Research Council Grant {\textquoteleft}c-VEDA{\textquoteright} (Consortium on Vulnerability to Externalizing Disorders and Addictions) (Grant No. MR/N000390/1), the National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King{\textquoteright}s College London, the Bundesministerium f{\"u}r Bildung und Forschung (Grant Nos. 01GS08152 and 01EV0711; Forschungsnetz AERIAL Grant Nos. 01EE1406A and 01EE1406B), the Deutsche Forschungsgemeinschaft (DFG Grant Nos. SM 80/7-2, SFB 940/2, and NE 1383/14-1), the Medical Research Foundation and Medical Research Council (Grant Nos. MR/R00465X/1 and MR/S020306/1), and the NIH-funded Enhancing Neuro Imaging Genetics through Meta Analysis (Grant Nos. 5U54EB020403-05 and 1R56AG058854-01). Further support was provided by grants from ANR (project AF12-NEUR0008-01 - WM2NA and ANR-12-SAMA-0004), the Fondation de France, the Fondation pour la Recherche M{\'e}dicale, the Mission Interminist{\'e}rielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-H{\^o}pitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012; the NIH, Science Foundation Ireland (Grant No. 16/ERCD/3797), U.S.A. (Axon, Testosterone and Mental Health during Adolescence; Grant No. RO1 MH085772-01A1), and by NIH Consortium Grant No. U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence. Funding Information: This work was supported by the China Natural Science Foundation (Grant Nos. 82022035 and 61773380 [to JS]), the National Institutes of Health (NIH Grant Nos. R01EB005846, R01MH117107, P20GM103472, and P30GM122734 [to VDC]), the National Science Foundation (Grant No. 1539067 [to VDC]), Beijing Municipal Science and Technology Commission (Grant No. Z181100001518005 [to JS]), the European Union?funded FP6 Integrated Project IMAGEN (Reinforcement-related behavior in normal brain function and psychopathology) (Grant No. LSHM-CT-2007-037286 [to GS]), the Horizon 2020?funded ERC Advanced Grant ?STRATIFY? (Brain network based stratification of reinforcement-related disorders) (Grant No. 695313 [to GS]), ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways) (Grant No. PR-ST-0416-10004), BRIDGET (JPND: BRain Imaging, cognition Dementia and next generation GEnomics) (Grant No. MR/N027558/1), Human Brain Project (HBP SGA 2, Grant No. 785907), the FP7 project MATRICS (Grant No. 603016), the Medical Research Council Grant ?c-VEDA? (Consortium on Vulnerability to Externalizing Disorders and Addictions) (Grant No. MR/N000390/1), the National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, the Bundesministerium f?r Bildung und Forschung (Grant Nos. 01GS08152 and 01EV0711; Forschungsnetz AERIAL Grant Nos. 01EE1406A and 01EE1406B), the Deutsche Forschungsgemeinschaft (DFG Grant Nos. SM 80/7-2, SFB 940/2, and NE 1383/14-1), the Medical Research Foundation and Medical Research Council (Grant Nos. MR/R00465X/1 and MR/S020306/1), and the NIH-funded Enhancing Neuro Imaging Genetics through Meta Analysis (Grant Nos. 5U54EB020403-05 and 1R56AG058854-01). Further support was provided by grants from ANR (project AF12-NEUR0008-01 - WM2NA and ANR-12-SAMA-0004), the Fondation de France, the Fondation pour la Recherche M?dicale, the Mission Interminist?rielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-H?pitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012; the NIH, Science Foundation Ireland (Grant No. 16/ERCD/3797), U.S.A. (Axon, Testosterone and Mental Health during Adolescence; Grant No. RO1 MH085772-01A1), and by NIH Consortium Grant No. U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence. 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. See the Supplement for the full list of IMAGEN Consortium collaborators. The supervised fusion code has been released and integrated in the Fusion ICA Toolbox (FIT, https://trendscenter.org/software/fit), which can be downloaded and used directly by users worldwide. The IMAGEN and ADHD multimodal data used in this study can be accessed upon application from IMAGEN and ADHD-200 Consortium. The SZ, MDD, drinking, and smoking data can be accessed upon request to the corresponding authors. The authors report no biomedical financial interests or potential conflicts of interest. Publisher Copyright: {\textcopyright} 2021 Society of Biological Psychiatry",

year = "2021",

month = oct,

day = "15",

doi = "10.1016/j.biopsych.2021.01.011",

language = "English (US)",

volume = "90",

pages = "529--539",

journal = "Biological Psychiatry",

issn = "0006-3223",

publisher = "Elsevier Inc.",

number = "8",

}

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 - Funding Information: This work was supported by the China Natural Science Foundation (Grant Nos. 82022035 and 61773380 [to JS]), the National Institutes of Health (NIH Grant Nos. R01EB005846 , R01MH117107 , P20GM103472 , and P30GM122734 [to VDC]), the National Science Foundation (Grant No. 1539067 [to VDC]), Beijing Municipal Science and Technology Commission (Grant No. Z181100001518005 [to JS]), the European Union–funded FP6 Integrated Project IMAGEN (Reinforcement-related behavior in normal brain function and psychopathology) (Grant No. LSHM-CT-2007-037286 [to GS]), the Horizon 2020–funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders) (Grant No. 695313 [to GS]), ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways) (Grant No. PR-ST-0416-10004), BRIDGET (JPND: BRain Imaging, cognition Dementia and next generation GEnomics) (Grant No. MR/N027558/1), Human Brain Project (HBP SGA 2, Grant No. 785907), the FP7 project MATRICS (Grant No. 603016), the Medical Research Council Grant ‘c-VEDA’ (Consortium on Vulnerability to Externalizing Disorders and Addictions) (Grant No. MR/N000390/1), the National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, the Bundesministerium für Bildung und Forschung (Grant Nos. 01GS08152 and 01EV0711; Forschungsnetz AERIAL Grant Nos. 01EE1406A and 01EE1406B), the Deutsche Forschungsgemeinschaft (DFG Grant Nos. SM 80/7-2, SFB 940/2, and NE 1383/14-1), the Medical Research Foundation and Medical Research Council (Grant Nos. MR/R00465X/1 and MR/S020306/1), and the NIH-funded Enhancing Neuro Imaging Genetics through Meta Analysis (Grant Nos. 5U54EB020403-05 and 1R56AG058854-01). Further support was provided by grants from ANR (project AF12-NEUR0008-01 - WM2NA and ANR-12-SAMA-0004), the Fondation de France, the Fondation pour la Recherche Médicale, the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-Hôpitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012; the NIH, Science Foundation Ireland (Grant No. 16/ERCD/3797), U.S.A. (Axon, Testosterone and Mental Health during Adolescence; Grant No. RO1 MH085772-01A1), and by NIH Consortium Grant No. U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence. Funding Information: This work was supported by the China Natural Science Foundation (Grant Nos. 82022035 and 61773380 [to JS]), the National Institutes of Health (NIH Grant Nos. R01EB005846, R01MH117107, P20GM103472, and P30GM122734 [to VDC]), the National Science Foundation (Grant No. 1539067 [to VDC]), Beijing Municipal Science and Technology Commission (Grant No. Z181100001518005 [to JS]), the European Union?funded FP6 Integrated Project IMAGEN (Reinforcement-related behavior in normal brain function and psychopathology) (Grant No. LSHM-CT-2007-037286 [to GS]), the Horizon 2020?funded ERC Advanced Grant ?STRATIFY? (Brain network based stratification of reinforcement-related disorders) (Grant No. 695313 [to GS]), ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways) (Grant No. PR-ST-0416-10004), BRIDGET (JPND: BRain Imaging, cognition Dementia and next generation GEnomics) (Grant No. MR/N027558/1), Human Brain Project (HBP SGA 2, Grant No. 785907), the FP7 project MATRICS (Grant No. 603016), the Medical Research Council Grant ?c-VEDA? (Consortium on Vulnerability to Externalizing Disorders and Addictions) (Grant No. MR/N000390/1), the National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, the Bundesministerium f?r Bildung und Forschung (Grant Nos. 01GS08152 and 01EV0711; Forschungsnetz AERIAL Grant Nos. 01EE1406A and 01EE1406B), the Deutsche Forschungsgemeinschaft (DFG Grant Nos. SM 80/7-2, SFB 940/2, and NE 1383/14-1), the Medical Research Foundation and Medical Research Council (Grant Nos. MR/R00465X/1 and MR/S020306/1), and the NIH-funded Enhancing Neuro Imaging Genetics through Meta Analysis (Grant Nos. 5U54EB020403-05 and 1R56AG058854-01). Further support was provided by grants from ANR (project AF12-NEUR0008-01 - WM2NA and ANR-12-SAMA-0004), the Fondation de France, the Fondation pour la Recherche M?dicale, the Mission Interminist?rielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-H?pitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012; the NIH, Science Foundation Ireland (Grant No. 16/ERCD/3797), U.S.A. (Axon, Testosterone and Mental Health during Adolescence; Grant No. RO1 MH085772-01A1), and by NIH Consortium Grant No. U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence. 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. See the Supplement for the full list of IMAGEN Consortium collaborators. The supervised fusion code has been released and integrated in the Fusion ICA Toolbox (FIT, https://trendscenter.org/software/fit), which can be downloaded and used directly by users worldwide. The IMAGEN and ADHD multimodal data used in this study can be accessed upon application from IMAGEN and ADHD-200 Consortium. The SZ, MDD, drinking, and smoking data can be accessed upon request to the corresponding authors. The authors report no biomedical financial interests or potential conflicts of interest. 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 - https://www.scopus.com/pages/publications/85107030956

UR - https://www.scopus.com/pages/publications/85107030956#tab=citedBy

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 -

Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker

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