Mapping expectancy-based appetitive placebo effects onto the brain in women.

Suggestions about hunger can generate placebo effects on hunger experiences. But, the underlying neurocognitive mechanisms are unknown. Here, we show in 255 women that hunger expectancies, induced by suggestion-based placebo interventions, determine hunger sensations and economic food choices. Functional magnetic resonance imaging in a subgroup (n = 57/255) provides evidence that the strength of expecting the placebo to decrease hunger moderates medial prefrontal cortex activation at the time of food choice and attenuates ventromedial prefrontal cortex (vmPFC) responses to food value. Dorsolateral prefrontal cortex activation linked to interference resolution formally mediates the suggestion-based placebo effects on hunger. A drift-diffusion model characterizes this effect by showing that the hunger suggestions bias participants' food choices and how much they weigh tastiness against the healthiness of food, which further moderates vmPFC-dlPFC psychophysiological interactions when participants expect decreased hunger. Thus, suggestion-induced beliefs about hunger shape hunger addressing economic choices through cognitive regulation of value computation within the prefrontal cortex.

Whole-brain gray matter maturation trajectories associated with autistic traits from adolescence to early adulthood.

A growing number of evidence supports a continued distribution of autistic traits in the general population. However, brain maturation trajectories of autistic traits as well as the influence of sex on these trajectories remain largely unknown. We investigated the association of autistic traits in the general population, with longitudinal gray matter (GM) maturation trajectories during the critical period of adolescence. We assessed 709 community-based adolescents (54.7% women) at age 14 and 22. After testing the effect of sex, we used whole-brain voxel-based morphometry to measure longitudinal GM volumes changes associated with autistic traits measured by the Social Responsiveness Scale (SRS) total and sub-scores. In women, we observed that the SRS was associated with slower GM volume decrease globally and in the left parahippocampus and middle temporal gyrus. The social communication sub-score correlated with slower GM volume decrease in the left parahippocampal, superior temporal gyrus, and pallidum; and the social cognition sub-score correlated with slower GM volume decrease in the left middle temporal gyrus, the right ventromedial prefrontal and orbitofrontal cortex. No longitudinal association was found in men. Autistic traits in young women were found to be associated with specific brain trajectories in regions of the social brain and the reward circuit known to be involved in Autism Spectrum Disorder. These findings support both the hypothesis of an earlier GM maturation associated with autistic traits in adolescence and of protective mechanisms in women. They advocate for further studies on brain trajectories associated with autistic traits in women.

Storm on predictive brain: A neurocomputational account of ketamine antidepressant effect.

For the past decade, ketamine, an N-methyl-D-aspartate receptor (NMDAr) antagonist, has been considered a promising treatment for major depressive disorder (MDD). Unlike the delayed effect of monoaminergic treatment, ketamine may produce fast-acting antidepressant effects hours after a single administration at subanesthetic dose. Along with these antidepressant effects, it may also induce transient dissociative (disturbing of the sense of self and reality) symptoms during acute administration which resolve within hours. To understand ketamine's rapid-acting antidepressant effect, several biological hypotheses have been explored, but despite these promising avenues, there is a lack of model to understand the timeframe of antidepressant and dissociative effects of ketamine. In this article, we propose a neurocomputational account of ketamine's antidepressant and dissociative effects based on the Predictive Processing (PP) theory, a framework for cognitive and sensory processing. PP theory suggests that the brain produces top-down predictions to process incoming sensory signals, and generates bottom-up prediction errors (PEs) which are then used to update predictions. This iterative dynamic neural process would relies on N-methyl-D-aspartate (NMDAr) and α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic receptors (AMPAr), two major component of the glutamatergic signaling. Furthermore, it has been suggested that MDD is characterized by over-rigid predictions which cannot be updated by the PEs, leading to miscalibration of hierarchical inference and self-reinforcing negative feedback loops. Based on former empirical studies using behavioral paradigms, neurophysiological recordings, and computational modeling, we suggest that ketamine impairs top-down predictions by blocking NMDA receptors, and enhances presynaptic glutamate release and PEs, producing transient dissociative symptoms and fast-acting antidepressant effect in hours following acute administration. Moreover, we present data showing that ketamine may enhance a delayed neural plasticity pathways through AMPAr potentiation, triggering a prolonged antidepressant effect up to seven days for unique administration. Taken together, the two sides of antidepressant effects with distinct timeframe could constitute the keystone of antidepressant properties of ketamine. These PP disturbances may also participate to a ketamine-induced time window of mental flexibility, which can be used to improve the psychotherapeutic process. Finally, these proposals could be used as a theoretical framework for future research into fast-acting antidepressants, and combination with existing antidepressant and psychotherapy.

Larger dlPFC and vmPFC grey matter volumes are associated with high adherence to the Mediterranean diet: A cross-sectional study in older adults.

Dietary self-control is associated with inter-individual differences in neuroanatomy. Yet, whether such inter-individual differences are also associated with healthier dietary patterns is yet to be determined. In this cross-sectional study, a total of 100 northern Portuguese older community-dwellers were assessed with regards to i) the adherence to a healthy dietary eating pattern - the Mediterranean diet (MedDiet), and ii) grey matter density (GMD) of brain regions associated with valuation and dietary self-regulation, the ventromedial (vmPFC) and dorsolateral prefrontal cortex (dlPFC), through voxel-based morphometry. Healthy food choices were ascertained through the Mediterranean Diet Adherence Screener (MEDAS) where higher scores indicated greater adherence to the MedDiet. Voxel-based morphometry showed that greater grey matter density in the dlPFC and vmPFC associated with a higher adherence to the MedDiet. These results replicate previous links between dietary decision-making measured under laboratory conditions and the neuroanatomy of the brain's valuation and self-control system. Importantly, they shed new light on the potential relevance of inter-individual differences in the neuroanatomy of these two brain regions for adhering to healthier dietary patterns in everyday life.

Evaluation of Early Ketamine Effects on Belief-Updating Biases in Patients With Treatment-Resistant Depression.

Importance: Clinical research has shown that persistent negative beliefs maintain depression and that subanesthetic ketamine infusions induce rapid antidepressant responses. Objective: To evaluate whether ketamine alters belief updating and how such cognitive effects are associated with the clinical effects of ketamine. Design, Setting, and Participants: This study used an observational case-control protocol with a mixed-effects design that nested 2 groups by 2 testing time points. Observers were not blinded. Patients with treatment-resistant depression (TRD) and healthy volunteer participants aged 34 to 68 years were included. Patients with TRD were diagnosed with major depressive disorder or bipolar depression, had a Montgomery-Åsberg Depression Rating Scale score greater than 20, a Maudsley Staging Method score greater than 7, and failed to respond to at least 2 prior antidepressant trials. Exclusion criteria were any other psychiatric, neurological, or neurosurgical comorbidities, substance use or addictive disorders, and recreational ketamine consumption. Data were collected from January to February 2019 and from May to December 2019, and data were analyzed from January 2020 to July 2021. Exposures: Patients with TRD were observed 24 hours before single ketamine infusion, 4 hours after the infusion, and 4 hours after the third infusion, which was 1 week after the first infusion. Healthy control participants were observed twice 1 week apart without ketamine exposure. Main Outcomes and Measures: Montgomery-Åsberg Depression Rating Scale score and belief updating after belief updating when patients received good news and bad news measured by a cognitive belief-updating task and mathematically formalized by a computational reinforcement learning model. Results: Of 56 included participants, 29 (52%) were male, and the mean (SEM) age was 52.3 (1.2) years. A total of 26 patients with TRD and 30 control participants were included. A significant group × testing time point × news valence interaction showed that patients with TRD updated their beliefs more after good than bad news following a single ketamine infusion (controlled for age and education: ß = -0.91; 95% CI, -1.58 to -0.24; t216 = -2.67; P = .008) than controls. Computational modeling showed that this effect was associated with asymmetrical learning rates (LRs) after ketamine treatment (good news LRs after ketamine, 0.51 [SEM, 0.04]; bad news LRs after ketamine 0.36 [SEM, 0.03], t25 = 3.8; P < .001) and partially mediated early antidepressant responses (path a*b: ß = -1.00 [SEM, 0.66]; t26 = -1.53; z = -1.98; P = .04). Conclusions and Relevance: These findings provide novel insights into the cognitive mechanisms of the action of ketamine in patients with TRD, with promising perspectives for augmented psychotherapy for individuals with mood disorders.

Individual variability in brain representations of pain.

Characterizing cerebral contributions to individual variability in pain processing is crucial for personalized pain medicine, but has yet to be done. In the present study, we address this problem by identifying brain regions with high versus low interindividual variability in their relationship with pain. We trained idiographic pain-predictive models with 13 single-trial functional MRI datasets (n = 404, discovery set) and quantified voxel-level importance for individualized pain prediction. With 21 regions identified as important pain predictors, we examined the interindividual variability of local pain-predictive weights in these regions. Higher-order transmodal regions, such as ventromedial and ventrolateral prefrontal cortices, showed larger individual variability, whereas unimodal regions, such as somatomotor cortices, showed more stable pain representations across individuals. We replicated this result in an independent dataset (n = 124). Overall, our study identifies cerebral sources of individual differences in pain processing, providing potential targets for personalized assessment and treatment of pain.

Identified Motivation as a Key Factor for School Engagement During the COVID-19 Pandemic-Related School Closure.

On March 16, 2020, French schools suddenly closed due to the COVID-19 pandemic, and middle school students were asked to study from home with no direct interactions with teachers or classmates. However, school plays an important role in the development of social, intellectual, and mental competencies and can counteract the negative effects of adverse life events on learning and early school dropout. In this study, we investigated how the unusual context of school closure during the COVID-19 pandemic affected school engagement. Specifically, we focused on inter-individual differences in the motivational determinants of school engagement. We thus performed an online survey of 170 students focusing on the time spent on mathematics assignments, motivation regulation, implicit theories of intelligence, such as adopting a growth or a fixed mindset about his/her intellectual abilities, and optimism. Importantly, the students participated in the online survey during the first lockdown period, with schools closed (T1), and the second lockdown period, with schools remaining open (T2). During T1, identified motivation positively predicted the time spent on math homework assignments: the more the students thought that working on math exercises was useful for their future life, the more time they spent studying. Importantly, the link between identified motivation and school engagement was specific to T1, when schools were closed, as indicated by a significant interaction between identified motivations by type of lockdown. These results suggest that having self-determined motivation is of particular importance when students are deprived of social and intellectual interactions with classmates and teachers. This finding paves the way toward the development of wise rational interventions that target identified motivation and can be applied during challenging societal times and adverse, common life events to keep students engaged with school.

Resting-state connectivity within the brain's reward system predicts weight loss and correlates with leptin.

Weight gain is often associated with the pleasure of eating food rich in calories. This idea is based on the findings that people with obesity showed increased neural activity in the reward and motivation systems of the brain in response to food cues. Such correlations, however, overlook the possibility that obesity may be associated with a metabolic state that impacts the functioning of reward and motivation systems, which in turn could be linked to reactivity to food and eating behaviour and weight gain. In a study involving 44 female participants [14 patients with obesity, aged 20-63 years (mean: 42, SEM: 3.2 years), and 30 matched lean controls, aged 22-60 years (mean: 37, SEM: 1.8 years)], we investigated how ventromedial prefrontal cortex seed-to-voxel resting-state connectivity distinguished between lean and obese participants at baseline. We used the results of this first step of our analyses to examine whether changes in ventromedial prefrontal cortex resting-state connectivity over 8 months could formally predict weight gain or loss. It is important to note that participants with obesity underwent bariatric surgery at the beginning of our investigation period. We found that ventromedial prefrontal cortex-ventral striatum resting-state connectivity and ventromedial-dorsolateral prefrontal cortex resting-state connectivity were sensitive to obesity at baseline. However, only the ventromedial prefrontal cortex-ventral striatum resting-state connectivity predicted weight changes over time using cross-validation, out-of-sample prediction analysis. Such an out-of-sample prediction analysis uses the data of all participants of a training set to predict the actually observed data in one independent participant in the hold-out validation sample and is then repeated for all participants. In seeking to explain the reason why ventromedial pre-frontal cortex-ventral striatum resting-state connectivity as the central hub of the brain's reward and motivational system may predict weight change over time, we linked weight loss surgery-induced changes in ventromedial prefrontal cortex-ventral striatum resting-state connectivity to surgery-induced changes in homeostatic hormone regulation. More specifically, we focussed on changes in fasting state systemic leptin, a homeostatic hormone signalling satiety, and inhibiting reward-related dopamine signalling. We found that the surgery-induced increase in ventromedial prefrontal cortex-ventral striatum resting-state connectivity was correlated with a decrease in fasting-state systemic leptin. These findings establish the first link between individual differences in brain connectivity in reward circuits in a more tonic state at rest, weight change over time and homeostatic hormone regulation.

Multiple Brain Networks Mediating Stimulus-Pain Relationships in Humans.

The brain transforms nociceptive input into a complex pain experience comprised of sensory, affective, motivational, and cognitive components. However, it is still unclear how pain arises from nociceptive input and which brain networks coordinate to generate pain experiences. We introduce a new high-dimensional mediation analysis technique to estimate distributed, network-level patterns that formally mediate the relationship between stimulus intensity and pain. We applied the model to a large-scale analysis of functional magnetic resonance imaging data (N = 284), focusing on brain mediators of the relationship between noxious stimulus intensity and trial-to-trial variation in pain reports. We identify mediators in both traditional nociceptive pathways and in prefrontal, midbrain, striatal, and default-mode regions unrelated to nociception in standard analyses. The whole-brain mediators are specific for pain versus aversive sounds and are organized into five functional networks. Brain mediators predicted pain ratings better than previous brain measures, including the neurologic pain signature (Wager et al. 2013). Our results provide a broader view of the networks underlying pain experience, as well as novel brain targets for interventions.

Pain-Evoked Reorganization in Functional Brain Networks.

Recent studies indicate that a significant reorganization of cerebral networks may occur in patients with chronic pain, but how immediate pain experience influences the organization of large-scale functional networks is not yet well characterized. To investigate this question, we used functional magnetic resonance imaging in 106 participants experiencing both noxious and innocuous heat. Painful stimulation caused network-level reorganization of cerebral connectivity that differed substantially from organization during innocuous stimulation and standard resting-state networks. Noxious stimuli increased somatosensory network connectivity with (a) frontoparietal networks involved in context representation, (b) "ventral attention network" regions involved in motivated action selection, and (c) basal ganglia and brainstem regions. This resulted in reduced "small-worldness," modularity (fewer networks), and global network efficiency and in the emergence of an integrated "pain supersystem" (PS) whose activity predicted individual differences in pain sensitivity across 5 participant cohorts. Network hubs were reorganized ("hub disruption") so that more hubs were localized in PS, and there was a shift from "connector" hubs linking disparate networks to "provincial" hubs connecting regions within PS. Our findings suggest that pain reorganizes the network structure of large-scale brain systems. These changes may prioritize responses to painful events and provide nociceptive systems privileged access to central control of cognition and action during pain.

Neuroanatomy of the vmPFC and dlPFC Predicts Individual Differences in Cognitive Regulation During Dietary Self-Control Across Regulation Strategies.

Making healthy food choices is challenging for many people. Individuals differ greatly in their ability to follow health goals in the face of temptation, but it is unclear what underlies such differences. Using voxel-based morphometry, we investigated in healthy humans (i.e., men and women) the links between structural variation in gray matter volume and individuals' level of success in shifting toward healthier food choices. We combined MRI and choice data into a joint dataset by pooling across three independent studies that used a task prompting participants to explicitly focus on the healthiness of food items before making their food choices. Within this dataset, we found that individual differences in gray matter volume in the ventromedial prefrontal cortex (vmPFC) and dorsolateral prefrontal cortex (dlPFC) predicted regulatory success. We extended and confirmed these initial findings by predicting regulatory success out of sample and across tasks in a second dataset requiring participants to apply a different regulation strategy that entailed distancing from cravings for unhealthy, appetitive foods. Our findings suggest that neuroanatomical markers in the vmPFC and dlPFC generalized to different forms of dietary regulation strategies across participant groups. They provide novel evidence that structural differences in neuroanatomy of two key regions for valuation and its control, the vmPFC and dlPFC, predict an individual's ability to exert control in dietary choices.SIGNIFICANCE STATEMENT Dieting involves regulating food choices to eat healthier foods and fewer unhealthy foods. People differ dramatically in their ability to achieve or maintain this regulation, but it is unclear why. Here, we show that individuals with more gray matter volume in the dorsolateral and ventromedial prefrontal cortex are better at exercising dietary self-control. This relationship was observed across four different studies examining two different forms of dietary self-regulation, suggesting that neuroanatomical differences in the ventromedial prefrontal cortex and dorsolateral prefrontal cortex may represent a general marker for self-control abilities. These results identify candidate neuroanatomical markers for dieting success and failure, and suggest potential targets for therapies aimed at preventing or treating obesity and related eating disorders.

How context alters value: The brain's valuation and affective regulation system link price cues to experienced taste pleasantness.

Informational cues such as the price of a wine can trigger expectations about its taste quality and thereby modulate the sensory experience on a reported and neural level. Yet it is unclear how the brain translates such expectations into sensory pleasantness. We used a whole-brain multilevel mediation approach with healthy participants who tasted identical wines cued with different prices while their brains were scanned using fMRI. We found that the brain's valuation system (BVS) in concert with the anterior prefrontal cortex played a key role in implementing the effect of price cues on taste pleasantness ratings. The sensitivity of the BVS to monetary rewards outside the taste domain moderated the strength of these effects. These findings provide novel evidence for the fundamental role that neural pathways linked to motivation and affective regulation play for the effect of informational cues on sensory experiences.

Quantifying cerebral contributions to pain beyond nociception.

Cerebral processes contribute to pain beyond the level of nociceptive input and mediate psychological and behavioural influences. However, cerebral contributions beyond nociception are not yet well characterized, leading to a predominant focus on nociception when studying pain and developing interventions. Here we use functional magnetic resonance imaging combined with machine learning to develop a multivariate pattern signature-termed the stimulus intensity independent pain signature-1 (SIIPS1)-that predicts pain above and beyond nociceptive input in four training data sets (Studies 1-4, N=137). The SIIPS1 includes patterns of activity in nucleus accumbens, lateral prefrontal and parahippocampal cortices, and other regions. In cross-validated analyses of Studies 1-4 and in two independent test data sets (Studies 5-6, N=46), SIIPS1 responses explain variation in trial-by-trial pain ratings not captured by a previous fMRI-based marker for nociceptive pain. In addition, SIIPS1 responses mediate the pain-modulating effects of three psychological manipulations of expectations and perceived control. The SIIPS1 provides an extensible characterization of cerebral contributions to pain and specific brain targets for interventions.

OCCURRENCE AND RELEVANCE OF MYCOPLASMA STURNI IN FREE-RANGING CORVIDS IN GERMANY.

Several Mycoplasma spp. are well-known pathogens in poultry. In birds of prey, White Storks ( Ciconia ciconia ), and some waterfowl (Anatidae, Pelecanidae) species, mycoplasmas occur commonly and seem to be apathogenic or commensal and most likely belong to the physiologic microbial flora of the respiratory tract. In other bird species, such as Common Nightingales ( Luscinia megarhynchos ) and tits (Paridae), Mycoplasma spp. are absent in healthy birds. In corvids, the prevalence and role of Mycoplasma spp. in disease remains unclear. In previous studies, Mycoplasma sturni was detected in diseased corvids; however, those studies included only a limited sample size or preselected individuals. We collected tracheal swabs of 97 free-ranging Corvidae, including 68 randomly selected individuals from hunting bags and 29 birds that had been admitted to a veterinary clinic. Tracheal swabs were examined for Mycoplasma spp. using culture and genus-specific PCR. If Mycoplasma spp. were detected, the species were identified by sequencing the 16S ribosomal (r) RNA gene and 16-23S rRNA intergenic transcribed spacer region. Five of 68 (7%) of the hunted birds and nine of 29 (31%) of the birds admitted to the veterinary clinic were PCR positive. In 13 of 14 PCR-positive samples, mycoplasmas were cultured and M. sturni was the only mycoplasmal species identified. None of the positive corvids from the hunting bags had clinical signs, whereas five of nine birds admitted to the veterinary clinic showed apathy, lameness, injuries, or fractures, which may not be associated with mycoplasmal infections. These data support the notion that M. sturni is the Mycoplasma sp. most frequently found in corvids, though its prevalence and ability to cause disease may involve interaction with other aspects of bird health.

Group-regularized individual prediction: theory and application to pain.

Multivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods can be used to predict or 'decode' psychological states in individual subjects. Single-subject MVPA approaches, however, are limited by the amount and quality of individual-subject data. In spite of higher spatial resolution, predictive accuracy from single-subject data often does not exceed what can be accomplished using coarser, group-level maps, because single-subject patterns are trained on limited amounts of often-noisy data. Here, we present a method that combines population-level priors, in the form of biomarker patterns developed on prior samples, with single-subject MVPA maps to improve single-subject prediction. Theoretical results and simulations motivate a weighting based on the relative variances of biomarker-based prediction-based on population-level predictive maps from prior groups-and individual-subject, cross-validated prediction. Empirical results predicting pain using brain activity on a trial-by-trial basis (single-trial prediction) across 6 studies (N=180 participants) confirm the theoretical predictions. Regularization based on a population-level biomarker-in this case, the Neurologic Pain Signature (NPS)-improved single-subject prediction accuracy compared with idiographic maps based on the individuals' data alone. The regularization scheme that we propose, which we term group-regularized individual prediction (GRIP), can be applied broadly to within-person MVPA-based prediction. We also show how GRIP can be used to evaluate data quality and provide benchmarks for the appropriateness of population-level maps like the NPS for a given individual or study.

Computational Dissection of Dopamine Motor and Motivational Functions in Humans.

UNLABELLED: Motor dysfunction (e.g., bradykinesia) and motivational deficit (i.e., apathy) are hallmarks of Parkinson's disease (PD). Yet, it remains unclear whether these two symptoms arise from a same dopaminergic dysfunction. Here, we develop a computational model that articulates motor control to economic decision theory, to dissect the motor and motivational functions of dopamine in humans. This model can capture different aspects of the behavior: choice (which action is selected) and vigor (action speed and intensity). It was used to characterize the behavior of 24 PD patients, tested both when medicated and unmedicated, in two behavioral tasks: an incentive motivation task that involved producing a physical effort, knowing that it would be multiplied by reward level to calculate the payoff, and a binary choice task that involved choosing between high reward/high effort and low reward/low effort options. Model-free analyses in both tasks showed the same two effects when comparing unmedicated patients to medicated patients: dopamine depletion (1) decreased the amount of effort that patients were willing to produce for a given reward and (2) slowed down the production of this effort, regardless of reward level. Model-based analyses captured these effects with two independent parameters, namely reward sensitivity and motor activation rate. These two parameters were respectively predictive of medication effects on clinical measures of apathy and motor dysfunction. More generally, we suggest that such computational phenotyping might help characterizing deficits and refining treatments in neuropsychiatric disorders. SIGNIFICANCE STATEMENT: Many neurological conditions are characterized by motor and motivational deficits, which both result in reduced behavior. It remains extremely difficult to disentangle whether these patients are simply unable or do not want to produce a behavior. Here, we propose a model-based analysis of the behavior produced in tasks that involve trading physical efforts for monetary rewards, to quantify parameters that capture motor dynamics as well as sensitivity to reward, effort, and fatigue. Applied to Parkinson's disease, this computational analysis revealed two independent effects of dopamine enhancers, which predicted clinical improvement in motor and motivational deficits. Such computational profiling might provide a useful explanatory level, between neural dysfunction and clinical manifestations, for characterizing neuropsychiatric disorders and personalizing treatments.

Mind matters: placebo enhances reward learning in Parkinson's disease.

Expectations have a powerful influence on how we experience the world. Neurobiological and computational models of learning suggest that dopamine is crucial for shaping expectations of reward and that expectations alone may influence dopamine levels. However, because expectations and reinforcers are typically manipulated together, the role of expectations per se has remained unclear. We separated these two factors using a placebo dopaminergic manipulation in individuals with Parkinson's disease. We combined a reward learning task with functional magnetic resonance imaging to test how expectations of dopamine release modulate learning-related activity in the brain. We found that the mere expectation of dopamine release enhanced reward learning and modulated learning-related signals in the striatum and the ventromedial prefrontal cortex. These effects were selective to learning from reward: neither medication nor placebo had an effect on learning to avoid monetary loss. These findings suggest a neurobiological mechanism by which expectations shape learning and affect.

Hemispheric dissociation of reward processing in humans: insights from deep brain stimulation.

Rewards have various effects on human behavior and multiple representations in the human brain. Behaviorally, rewards notably enhance response vigor in incentive motivation paradigms and bias subsequent choices in instrumental learning paradigms. Neurally, rewards affect activity in different fronto-striatal regions attached to different motor effectors, for instance in left and right hemispheres for the two hands. Here we address the question of whether manipulating reward-related brain activity has local or general effects, with respect to behavioral paradigms and motor effectors. Neuronal activity was manipulated in a single hemisphere using unilateral deep brain stimulation (DBS) in patients with Parkinson's disease. Results suggest that DBS amplifies the representation of reward magnitude within the targeted hemisphere, so as to affect the behavior of the contralateral hand specifically. These unilateral DBS effects on behavior include both boosting incentive motivation and biasing instrumental choices. Furthermore, using computational modeling we show that DBS effects on incentive motivation can predict DBS effects on instrumental learning (or vice versa). Thus, we demonstrate the feasibility of causally manipulating reward-related neuronal activity in humans, in a manner that is specific to a class of motor effectors but that generalizes to different computational processes. As these findings proved independent from therapeutic effects on parkinsonian motor symptoms, they might provide insight into DBS impact on non-motor disorders, such as apathy or hypomania.

Neural mechanisms underlying motivation of mental versus physical effort.

Mental and physical efforts, such as paying attention and lifting weights, have been shown to involve different brain systems. These cognitive and motor systems, respectively, include cortical networks (prefronto-parietal and precentral regions) as well as subregions of the dorsal basal ganglia (caudate and putamen). Both systems appeared sensitive to incentive motivation: their activity increases when we work for higher rewards. Another brain system, including the ventral prefrontal cortex and the ventral basal ganglia, has been implicated in encoding expected rewards. How this motivational system drives the cognitive and motor systems remains poorly understood. More specifically, it is unclear whether cognitive and motor systems can be driven by a common motivational center or if they are driven by distinct, dedicated motivational modules. To address this issue, we used functional MRI to scan healthy participants while performing a task in which incentive motivation, cognitive, and motor demands were varied independently. We reasoned that a common motivational node should (1) represent the reward expected from effort exertion, (2) correlate with the performance attained, and (3) switch effective connectivity between cognitive and motor regions depending on task demand. The ventral striatum fulfilled all three criteria and therefore qualified as a common motivational node capable of driving both cognitive and motor regions of the dorsal striatum. Thus, we suggest that the interaction between a common motivational system and the different task-specific systems underpinning behavioral performance might occur within the basal ganglia.