Biology subjects

Huys, Q. J. M.

Publications and source records attributed to Huys, Q. J. M..

7 recordsLinked to original sources

bioRxiv2025

Differential effects of dopamine and serotonin on reward and punishment processes in humans: A systematic review and meta-analysis

ImportanceTo support treatment assignment, mechanistic biomarkers should be selectively sensitive to specific interventions. Here, we examine whether different components of reinforcement learning in humans satisfy this necessary precondition. We focus on pharmacological manipulations of dopamine and serotonin that form the backbone of first-line management of common mental illnesses such as depression and anxiety. ObjectiveTo perform a meta-analysis of pharmacological manipulations of dopamine and serotonin and examine whether they show distinct associations with reinforcement learning components in humans. Data SourcesOvid MEDLINE/PubMed, Embase, and PsycInfo databases were searched for studies published between January 1, 1946 and January 19, 2023 (repeated April 9, 2024, and October 15, 2024) investigating dopaminergic or serotonergic effects on reward/punishment processes in humans, according to PRISMA guidelines. Study SelectionStudies reporting randomized, placebo-controlled, dopaminergic or serotonergic manipulations on a behavioral outcome from a reward/punishment processing task in healthy humans were included. Data Extraction and SynthesisStandardized mean difference (SMD) scores were calculated for the comparison between each drug (dopamine/serotonin) and placebo on a behavioral reward or punishment outcome and quantified in random-effects models for overall reward/punishment processes and four main subcategories. Study quality (Cochrane Collaborations tool), moderators, heterogeneity, and publication bias were also assessed. Main Outcome(s) and Measure(s)Performance on reward/punishment processing tasks. ResultsIn total, 68 dopamine and 39 serotonin studies in healthy volunteers were included (Ndopamine=2291, Nplacebo=2284; Nserotonin=1491, Nplacebo=1523). Dopamine was associated with an increase in overall reward (SMD=0.18, 95%CI [0.09 0.28]) but not punishment function (SMD=-0.06, 95%CI [-0.26,0.13]). Serotonin was not meaningfully associated with overall punishment (SMD=0.22, 95%CI [-0.04,0.49]) or reward (SMD=0.02, 95%CI [-0.33,0.36]). Importantly, dopaminergic and serotonergic manipulations had distinct associations with subcomponents. Dopamine was associated with reward learning/sensitivity (SMD=0.26, 95%CI [0.11,0.40]), reward discounting (SMD=-0.08, 95%CI [-0.14,-0.01]) and reward vigor (SMD=0.32, 95%CI [0.11,0.54]). By contrast, serotonin was associated with punishment learning/sensitivity (SMD=0.32, 95%CI [0.05,0.59]), reward discounting (SMD=-0.35, 95%CI [-0.67,-0.02]), and aversive Pavlovian processes (within-subject studies only; SMD=0.36, 95%CI [0.20,0.53]). Conclusions and RelevancePharmacological manipulations of both dopamine and serotonin have measurable associations with reinforcement learning in humans. The selective associations with different components suggests that reinforcement learning tasks could form the basis of selective, mechanistically interpretable biomarkers to support treatment assignment. Key pointsO_ST_ABSQuestionC_ST_ABSDo pharmacological manipulations of dopamine and serotonin affect components of reinforcement learning in humans? FindingsUpregulating dopamine is associated with increased reward learning/sensitivity and reward response vigor, and decreased reward discounting. Upregulation of serotonin is associated with increased punishment learning/sensitivity and decreased reward discounting. MeaningPharmacological manipulations of dopamine and serotonin have dissociable associations with different components of reinforcement learning. This forms a necessary basis for the development of selective markers for treatment assignment.

neuroscience↗

bioRxiv2022

Individual Variation in Risky Decisions Is Related to Age and Gender but not to Mental Health Symptoms

Risky decisions involve choosing between options where the outcomes are uncertain. Cognitive tasks such as the CANTAB Cambridge Gamble Task (CGT) have revealed that patients with depression make more conservative decisions, but the mechanisms of choice evaluation underlying such decisions, and how they lead to the observed differences in depression, remain unknown. To test this, we used a computational modelling approach in a broad general-population sample (N = 753) who performed the CANTAB CGT and completed questionnaires assessing symptoms of mental illness, including depression. We fit five different computational models to the data, including two novel ones, and found that a novel model that uses an inverse power function in the loss domain (contrary to standard Prospect Theory accounts), and is influenced by the probabilities but not the magnitudes of different outcomes, captures the characteristics of our dataset very well. Surprisingly, model parameters were not significantly associated with any mental health questionnaire scores, including depression scales; but they were related to demographic variables, particularly age, with stronger associations than typical model-agnostic task measures. This study showcases a new methodology to analyse data from CANTAB CGT, describes a noteworthy null finding with respect to mental health symptoms, and demonstrates the added precision that a computational approach can offer.

neuroscience↗

bioRxiv2020

The relationship between resting-state functional connectivity, antidepressant discontinuation and depression relapse

The risk of relapsing into depression after stopping antidepressants is high, but no established predictors exist. Resting-state functional magnetic resonance imaging (rsfMRI) measures may help predict relapse and identify the mechanisms by which relapses occur. rsfMRI data were acquired from healthy controls and from patients with remitted major depressive disorder on antidepressants. Patients were assessed a second time either before or after discontinuation of the antidepressant, and followed up for six months to assess relapse. A seed-based functional connectivity analysis was conducted focusing on the left subgenual anterior cingulate cortex and left posterior cingulate cortex. Seeds in the amygdala and dorsolateral prefrontal cortex were explored. 44 healthy controls (age: 33.8 (10.5), 73% female) and 84 patients (age: 34.23 (10.8), 80% female) were included in the analysis. 29 patients went on to relapse and 38 remained well. The seed-based analysis showed that discontinuation resulted in an increased functional connectivity between the right dorsolateral prefrontal cortex and the parietal cortex in non-relapsers. In an exploratory analysis, this functional connectivity predicted relapse risk with a balanced accuracy of 0.86. Further seed-based analyses, however, failed to reveal differences in functional connectivity between patients and controls, between relapsers and non-relapsers before discontinuation and changes due to discontinuation independent of relapse. In conclusion, changes in the connectivity between the dorsolateral prefrontal cortex and the posterior default mode network were associated with and predictive of relapse after open-label antidepressant discontinuation. This finding requires replication in a larger dataset.

neuroscience↗

bioRxiv2020

Low predictive power of clinical features for relapse prediction after antidepressant discontinuation in a naturalistic setting

1BackgroundThe risk of relapse after antidepressant medication (ADM) discontinuation is high. Predictors of relapse could guide clinical decision-making, but are yet to be established. MethodWe assessed demographic and clinical variables in a longitudinal observational study before antidepressant discontinuation. State-dependent variables were re-assessed either after discontinuation or before discontinuation after a waiting period. Relapse was assessed during six months after discontinuation. We applied logistic general linear models in combination with least absolute shrinkage and selection operator and elastic nets to avoid overfitting in order to identify predictors of relapse and estimated their generalisability using cross-validation. ResultsThe final sample included 104 patients (age: 34.86 (11.1), 77% female) and 57 healthy controls (age: 34.12 (10.6), 70% female). 36% of the patients experienced a relapse. Treatment by a general practitioner increased the risk of relapse. Although within-sample statistical analyses suggested reasonable sensitivity and specificity, out-of-sample prediction of relapse was at chance level. Residual symptoms increased with discontinuation, but did not relate to relapse. Conclusion and RelevanceDemographic and standard clinical variables appear to carry little predictive power and therefore are of limited use for patients and clinicians in guiding clinical decision-making.

neuroscience↗

bioRxiv2019

Stimulation of the vagus nerve reduces learning in a go/no-go reinforcement learning task

When facing decisions to approach rewards or to avoid punishments, we often figuratively go with our gut, and the impact of metabolic states such as hunger on motivation are well documented. However, whether and how vagal feedback signals from the gut influence instrumental actions is unknown. Here, we investigated the effect of non-invasive transcutaneous vagus nerve stimulation (tVNS) vs. sham (randomized cross-over design) on approach and avoidance behavior using an established go/no-go reinforcement learning paradigm (Guitart-Masip et al., 2012) in 39 healthy, participants after an overnight fast. First, mixed-effects logistic regression analysis of choice accuracy showed that tVNS acutely impaired decision-making, p = .045. Computational reinforcement learning models identified the cause of this as a reduction in the learning rate through tVNS ({Delta} = -0.092, pboot= .002), particularly after punishment ({Delta}Pun= -0.081, pboot= .012 vs.{Delta} Rew= -0.031, p = .22). However, tVNS had no effect on go biases, Pavlovian response biases or response time. Hence, tVNS appeared to influence learning rather than action execution. These results highlight a novel role of vagal afferent input in modulating reinforcement learning by tuning the learning rate according to homeostatic needs.

neuroscience↗

bioRxiv2018

Self-regulation of the Dopaminergic Reward Circuit in Cocaine Users with Mental Imagery and Neurofeedback

Background: Enhanced drug-related reward sensitivity accompanied by impaired sensitivity to non-drug related rewards in the mesolimbic dopamine system are thought to underlie the broad motivational deficits and dysfunctional decision-making frequently observed in cocaine use disorder (CUD). Effective approaches to modify this imbalance and reinstate non-drug reward responsiveness are urgently needed. Here we examine whether cocaine users (CU) can use mental imagery of non-drug rewards to self-regulate the ventral tegmental area and substantia nigra (VTA/SN). We expected that compulsive and obsessive thoughts about cocaine consumption would hamper the ability to self-regulate the VTA/SN. Finally, we tested if self-regulation of the VTA/SN can be improved with real-time fMRI (rtfMRI) neurofeedback (NFB).\n\nMethods: Twenty-two CU and 28 healthy controls (HC) were asked to voluntarily up-regulate VTA/SN activity with rewarding non-drug imagery alone, or combined with rtfMRI NFB of VTA/SN activity. Obsessive-compulsive drug use was measured with the Obsessive Compulsive Cocaine Use Scale (OCCUS).\n\nResults: CU were able to induce activity in the dopaminergic midbrain and other reward regions with reward imagery. The ability to self-regulate the VTA/SN was reduced in those with more severe obsessive-compulsive drug use. NFB enhanced the effect of non-drug imagery.\n\nConclusion: CU can voluntary activate their reward system through non-drug related imagery and improve this ability with rtfMRI NFB. Combining reward imagery and rtFMRI NFB has great potential for modifying the maladapted reward sensitivity and reinstating non-drug reward responsiveness. This motivates further work to examine the therapeutic potential of cognitive neurostimulation in CUD.

neuroscience↗

bioRxiv2017

Generalization and search in risky environments

How do people pursue rewards in risky environments, where some outcomes should be avoided at all costs? We investigate how participant search for spatially correlated rewards in scenarios where one must avoid sampling rewards below a given threshold. This requires not only the balancing of exploration and exploitation, but also reasoning about how to avoid potentially risky areas of the search space. Within risky versions of the spatially correlated multi-armed bandit task, we show that participants behavior is aligned well with a Gaussian process function learning algorithm, which chooses points based on a safe optimization routine. Moreover, using leave-one-block-out cross-validation, we find that participants adapt their sampling behavior to the riskiness of the task, although the underlying function learning mechanism remains relatively unchanged. These results show that participants can adapt their search behavior to the adversity of the environment and enrich our understanding of adaptive behavior in the face of risk and uncertainty.

animal behavior and cognition↗