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.

Figure 1.

Study design. (A) Identify high-novelty seeking (NS)–associated multimodal brain networks on the top 20% NS scored adolescents at age 14 (239 out of 1378, IMAGEN). (B) Follow-up study within IMAGEN: to evaluate whether the identified high-NS–associated multimodal features can longitudinally predict five different risk scores for the same subjects (n = 239) and the unseen youth (n = 1100) at 19 years of age. (C) External patient cohort verification, n = 965: to determine whether high-NS–associated features can predict symptom scores for alcohol drinking (Alcohol Use Disorders Identification Test [AUDIT]), smoking (Fagerström Test for Nicotine Dependence [FTND]), attention-deficit/hyperactivity disorder (ADHD) (hyperactivity), major depressive disorder (MDD) (depression), and schizophrenia (SZ) (psychosis). (D) Classification between patients (n = 965) and control subjects (n = 1094) as well as among different patient groups. (E) A generalized dysfunctional multimodal brain circuit spanning alcohol, smoking, hyperactivity, depression, and psychosis. Depress, depression; HDRS, Hamilton Depression Rating Scale; Hyperac, hyperactivity; PANSS, Positive and Negative Syndrome Scale; Psychos, psychosis.

Figure 2.

The analysis flowchart. Novelty-seeking (NS) scores were used as a reference to guide a four-way multimodal fusion to identify a set of multimodal imaging biomarkers, each of which was separated as positive and negative brain regions based on the z-scored brain maps, plus the corresponding biomarker loadings, resulting in 12 features for the following prediction analysis. Multiple linear regression models were constructed for each of the five risk scores including alcohol use, smoking, hyperactivity, depression, and psychosis in the subset of 14-year-old adolescents. Then, the same prediction models were applied to longitudinally predict each of the five risk scores of the same subjects and the large set of unseen healthy adolescents at 19 years of age. The same models were also used to predict corresponding symptom scores of five types of patients, including alcohol drinkers, smokers, and patients with attention-deficit/hyperactivity disorder (ADHD), major depressive disorder (MDD), or schizophrenia (SZ). Finally, binary class and multiclass classification analysis were performed to verify the classification ability of the identified high-NS–associated multimodal imaging features. FTND, Fagerström Test for Nicotine Dependence; GM, gray matter; HC, healthy control subjects.

Figure 3.

The identified high-novelty seeking–associated multimodal joint components in the subset of 14-year-old adolescents. (A) Spatial brain maps visualized at |Z| > 2. (B) Correlation scatter plot between novelty-seeking scores and loadings of component for each modality. GM, gray matter; IC, independent component.

Figure 4.

Prediction results based on the identified high-novelty seeking (NS)–associated multimodal brain imaging networks. (A) Positive (red) and negative (blue) brain maps of the z-scored components and the corresponding loading parameters (columns). Loadings represent the contribution weight of the corresponding component across subjects. (B) Regression models trained on the subset of 14-year-old high-risk adolescents on five different risk scores. (C) Within-IMAGEN longitudinal predictions on five risk scores for the same 19-year-old adolescents (n = 239) using the same prediction models as in panel (B). (D) Within-IMAGEN longitudinal predictions for the other unseen 19-year-old adolescents (n ≈ 1100). (E) Generalized prediction for independent patients diagnosed as drinking, smoking, or having attention-deficit/hyperactivity disorder (ADHD), major depressive disorder (MDD), or schizophrenia (SZ) (n = 964). The n in each subplot represents the number of subjects with that kind of risk scores. Here r represents correlation between true values and the predicted values; p_perm represents the p values of permutation test, and 1−β represents the statistical power. *False discovery rate correction for multiple comparisons; ^{^}Bonferroni correction. A summarized table on the prediction accuracy estimation by including 1) Pearson correlation, 2) Spearman correlation, 3) partial correlation by regressing out gender, 4) normalized root-mean-square prediction error, 5) permutation test, and 6) statistical power can be found in Table S4. AUDIT, Alcohol Use Disorders Identification Test; FTND, Fagerström Test for Nicotine Dependence; GM, gray matter.

Figure 5.

Receiver operating characteristic curves and confusion matrices obtained from the classification based on the identified features. (A) Binary classification between all the patients and healthy control subjects (HC). (B) Six-group classification among HC, alcohol drinking, smoking, attention-deficit/hyperactivity disorder (ADHD), major depressive disorder (MDD), and schizophrenia (SZ). (C) Five-group classification among HC, alcohol drinking, ADHD, MDD, and SZ. (D) Three-group classification among ADHD, MDD, and SZ. The rows in each confusion matrix show the true group label, and the columns show the predicted label. The diagonal colorful cells (true positive rate) show where the true labels and predicted labels match. The off-diagonal cells (gray, false-negative rate) represent the misclassified percentage. ACC, accuracy; AUC, area under the curve; ROC, receiver operating characteristic.