Dataset Information

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.

SUBMITTER: Qi S

PROVIDER: S-EPMC8322149 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Publications

Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker.

Qi Shile S Schumann Gunter G Bustillo Juan J Turner Jessica A JA Jiang Rongtao R Zhi Dongmei D Fu Zening Z Mayer Andrew R AR Vergara Victor M VM Silva Rogers F RF Iraji Armin A Chen Jiayu J Damaraju Eswar E Ma Xiaohong X Yang Xiao X Stevens Michael M Mathalon Daniel H DH Ford Judith M JM Voyvodic James J Mueller Bryon A BA Belger Aysenil A Potkin Steven G SG Preda Adrian A Zhuo Chuanjun C Xu Yong Y Chu Congying C Banaschewski Tobias T Barker Gareth J GJ Bokde Arun L W ALW Quinlan Erin Burke EB Desrivières Sylvane S Flor Herta H Grigis Antoine A Garavan Hugh H Gowland Penny P Heinz Andreas A Martinot Jean-Luc JL Paillère Martinot Marie-Laure ML Artiges Eric E Nees Frauke F Orfanos Dimitri Papadopoulos DP Paus Tomáš T Poustka Luise L Hohmann Sarah S Fröhner Juliane H JH Smolka Michael N MN Walter Henrik H Whelan Robert R Calhoun Vince D VD Sui Jing J

Biological psychiatry 20210130 8

<h4>Background</h4>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.<h4>Methods</h4>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 th ...[more]

PMID: 33875230

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Project description:Computational models of reinforcement learning have helped dissect discrete components of reward-related function and characterize neurocognitive deficits in psychiatric illnesses. Stimulus novelty biases decision-making, even when unrelated to choice outcome, acting as if possessing intrinsic reward value to guide decisions toward uncertain options. Heightened novelty seeking is characteristic of attention deficit hyperactivity disorder, yet how this influences reward-related decision-making is computationally encoded, or is altered by stimulant medication, is currently uncertain. Here we used an established reinforcement-learning task to model effects of novelty on reward-related behaviour during functional MRI in 30 adults with attention deficit hyperactivity disorder and 30 age-, sex- and IQ-matched control subjects. Each participant was tested on two separate occasions, once ON and once OFF stimulant medication. OFF medication, patients with attention deficit hyperactivity disorder showed significantly impaired task performance (P = 0.027), and greater selection of novel options (P = 0.004). Moreover, persistence in selecting novel options predicted impaired task performance (P = 0.025). These behavioural deficits were accompanied by a significantly lower learning rate (P = 0.011) and heightened novelty signalling within the substantia nigra/ventral tegmental area (family-wise error corrected P < 0.05). Compared to effects in controls, stimulant medication improved attention deficit hyperactivity disorder participants' overall task performance (P = 0.011), increased reward-learning rates (P = 0.046) and enhanced their ability to differentiate optimal from non-optimal novel choices (P = 0.032). It also reduced substantia nigra/ventral tegmental area responses to novelty. Preliminary cross-sectional evidence additionally suggested an association between long-term stimulant treatment and a reduction in the rewarding value of novelty. These data suggest that aberrant substantia nigra/ventral tegmental area novelty processing plays an important role in the suboptimal reward-related decision-making characteristic of attention deficit hyperactivity disorder. Compared to effects in controls, abnormalities in novelty processing and reward-related learning were improved by stimulant medication, suggesting that they may be disorder-specific targets for the pharmacological management of attention deficit hyperactivity disorder symptoms.

Project description:Psychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. We used high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N = 1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, showed general well-being. Clusters 1-3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. Depressed patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N = 622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction area under the receiver operating characteristic curve (AUC) = 81%; significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatments.

Dataset Information

Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker.

Background

Methods

Results

Conclusions

Publications

Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker.

Similar Datasets

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