Cortical Grey Matter Mediates Increases in Model-Based Control and Learning from Positive Feedback from Adolescence to Adulthood.

Cognition and brain structure undergo significant maturation from adolescence into adulthood. Model-based (MB) control is known to increase across development, which is mediated by cognitive abilities. Here, we asked two questions unaddressed in previous developmental studies. First, what are the brain structural correlates of age-related increases in MB control? Second, how are age-related increases in MB control from adolescence to adulthood influenced by motivational context? A human developmental sample (n = 103; age, 12-50, male/female, 55:48) completed structural MRI and an established task to capture MB control. The task was modified with respect to outcome valence by including (1) reward and punishment blocks to manipulate the motivational context and (2) an additional choice test to assess learning from positive versus negative feedback. After replicating that an age-dependent increase in MB control is mediated by cognitive abilities, we demonstrate first-time evidence that gray matter density (GMD) in the parietal cortex mediates the increase of MB control with age. Although motivational context did not relate to age-related changes in MB control, learning from positive feedback improved with age. Meanwhile, negative feedback learning showed no age effects. We present a first report that an age-related increase in positive feedback learning was mediated by reduced GMD in the parietal, medial, and dorsolateral prefrontal cortex. Our findings indicate that brain maturation, putatively reflected in lower GMD, in distinct and partially overlapping brain regions could lead to a more efficient brain organization and might thus be a key developmental step toward age-related increases in planning and value-based choice.SIGNIFICANCE STATEMENT Changes in model-based decision-making are paralleled by extensive maturation in cognition and brain structure across development. Still, to date the neuroanatomical underpinnings of these changes remain unclear. Here, we demonstrate for the first time that parietal GMD mediates age-dependent increases in model-based control. Age-related increases in positive feedback learning were mediated by reduced GMD in the parietal, medial, and dorsolateral prefrontal cortex. A manipulation of motivational context did not have an impact on age-related changes in model-based control. These findings highlight that brain maturation in distinct and overlapping cortical regions constitutes a key developmental step toward improved value-based choices.

The ReCoDe addiction research consortium: Losing and regaining control over drug intake-Findings and future perspectives.

Substance use disorders (SUDs) are seen as a continuum ranging from goal-directed and hedonic drug use to loss of control over drug intake with aversive consequences for mental and physical health and social functioning. The main goals of our interdisciplinary German collaborative research centre on Losing and Regaining Control over Drug Intake (ReCoDe) are (i) to study triggers (drug cues, stressors, drug priming) and modifying factors (age, gender, physical activity, cognitive functions, childhood adversity, social factors, such as loneliness and social contact/interaction) that longitudinally modulate the trajectories of losing and regaining control over drug consumption under real-life conditions. (ii) To study underlying behavioural, cognitive and neurobiological mechanisms of disease trajectories and drug-related behaviours and (iii) to provide non-invasive mechanism-based interventions. These goals are achieved by: (A) using innovative mHealth (mobile health) tools to longitudinally monitor the effects of triggers and modifying factors on drug consumption patterns in real life in a cohort of 900 patients with alcohol use disorder. This approach will be complemented by animal models of addiction with 24/7 automated behavioural monitoring across an entire disease trajectory; i.e. from a naïve state to a drug-taking state to an addiction or resilience-like state. (B) The identification and, if applicable, computational modelling of key molecular, neurobiological and psychological mechanisms (e.g., reduced cognitive flexibility) mediating the effects of such triggers and modifying factors on disease trajectories. (C) Developing and testing non-invasive interventions (e.g., Just-In-Time-Adaptive-Interventions (JITAIs), various non-invasive brain stimulations (NIBS), individualized physical activity) that specifically target the underlying mechanisms for regaining control over drug intake. Here, we will report on the most important results of the first funding period and outline our future research strategy.

Working memory gating in obesity: Insights from a case-control fMRI study.

Computational models and neurophysiological data propose that a 'gating mechanism' coordinates distractor-resistant maintenance and flexible updating of working memory contents: While maintenance of information is mainly implemented in the prefrontal cortex, updating of information is signaled by phasic increases in dopamine in the striatum. Previous literature demonstrates structural and functional alterations in these brain areas, as well as differential dopamine transmission among individuals with obesity, suggesting potential impairments in these processes. To test this hypothesis, we conducted an observational case-control fMRI study, dividing participants into groups with and without obesity based on their BMI. We probed maintenance and updating of working memory contents using a modified delayed match to sample task and investigated the effects of SNPs related to the dopaminergic system. While the task elicited the anticipated brain responses, our findings revealed no evidence for group differences in these two processes, neither at the neural level nor behaviorally. However, depending on Taq1A genotype, which affects dopamine receptor density in the striatum, participants with obesity performed worse on the task. In conclusion, this study does not support the existence of overall obesity-related differences in working memory gating. Instead, we propose that potentially subtle alterations may manifest specifically in individuals with a 'vulnerable' genotype.

Measuring self-regulation in everyday life: Reliability and validity of smartphone-based experiments in alcohol use disorder.

Self-regulation, the ability to guide behavior according to one's goals, plays an integral role in understanding loss of control over unwanted behaviors, for example in alcohol use disorder (AUD). Yet, experimental tasks that measure processes underlying self-regulation are not easy to deploy in contexts where such behaviors usually occur, namely outside the laboratory, and in clinical populations such as people with AUD. Moreover, lab-based tasks have been criticized for poor test-retest reliability and lack of construct validity. Smartphones can be used to deploy tasks in the field, but often require shorter versions of tasks, which may further decrease reliability. Here, we show that combining smartphone-based tasks with joint hierarchical modeling of longitudinal data can overcome at least some of these shortcomings. We test four short smartphone-based tasks outside the laboratory in a large sample (N = 488) of participants with AUD. Although task measures indeed have low reliability when data are analyzed traditionally by modeling each session separately, joint modeling of longitudinal data increases reliability to good and oftentimes excellent levels. We next test the measures' construct validity and show that extracted latent factors are indeed in line with theoretical accounts of cognitive control and decision-making. Finally, we demonstrate that a resulting cognitive control factor relates to a real-life measure of drinking behavior and yields stronger correlations than single measures based on traditional analyses. Our findings demonstrate how short, smartphone-based task measures, when analyzed with joint hierarchical modeling and latent factor analysis, can overcome frequently reported shortcomings of experimental tasks.

Distributed networks for auditory memory differentially contribute to recall precision.

Re-directing attention to objects in working memory can enhance their representational fidelity. However, how this attentional enhancement of memory representations is implemented across distinct, sensory and cognitive-control brain network is unspecified. The present fMRI experiment leverages psychophysical modelling and multivariate auditory-pattern decoding as behavioral and neural proxies of mnemonic fidelity. Listeners performed an auditory syllable pitch-discrimination task and received retro-active cues to selectively attend to a to-be-probed syllable in memory. Accompanied by increased neural activation in fronto-parietal and cingulo-opercular networks, valid retro-cues yielded faster and more perceptually sensitive responses in recalling acoustic detail of memorized syllables. Information about the cued auditory object was decodable from hemodynamic response patterns in superior temporal sulcus (STS), fronto-parietal, and sensorimotor regions. However, among these regions retaining auditory memory objects, neural fidelity in the left STS and its enhancement through attention-to-memory best predicted individuals' gain in auditory memory recall precision. Our results demonstrate how functionally discrete brain regions differentially contribute to the attentional enhancement of memory representations.

Computational Theories of Alcohol Use Disorder: Mapping Learning and Choice Mechanisms on Symptoms.

Alcohol use disorder (AUD) is characterized by a combination of symptoms including excessive craving, loss of control, and progressive neglect of alternative pleasures. A mechanistic understanding of what drives these symptoms is needed to improve diagnostic stratification and to develop new treatment and prevention strategies for AUD. To date, there is no consensus regarding a unifying mechanistic framework that accounts for the different symptoms of AUD. Reinforcement learning (RL) and economic choice theories may be key to elucidating the underlying processes of symptom development and maintenance in AUD. These algorithms may account for the different behavioral and physiological phenomena and are suited to dissect mechanisms linked to different symptoms of AUD. We here review different RL and economic choice models and how they map onto three symptoms of AUD: (1) cue-induced craving, (2) neglect of alternative rewards, and (3) consumption despite adverse consequences. For each symptom and theory, we describe findings from animal and human studies. In humans, we focus on empirical studies that investigated RL models in the context of treatment outcome in AUD. The review indicates important gaps to be addressed in the future by highlighting the challenges in transferring findings from RL and economic choice studies to clinical application. We also critically evaluate the potential and pitfalls of a symptom-oriented approach and highlight the importance of elucidating the role of learning and decision-making processes across diagnostic boundaries.

Mobile Data Collection of Cognitive-Behavioral Tasks in Substance Use Disorders: Where Are We Now?

INTRODUCTION: Over the last decades, our understanding of the cognitive, motivational, and neural processes involved in addictive behavior has increased enormously. A plethora of laboratory-based and cross-sectional studies has linked cognitive-behavioral measures to between-subject differences in drinking behavior. However, such laboratory-based studies inevitably suffer from small sample sizes and the inability to link temporal fluctuations in task measures to fluctuations in real-life substance use. To overcome these problems, several existing behavioral tasks have been transferred to smartphones to allow studying cognition in the field. METHOD: In this narrative review, we first summarize studies that used existing behavioral tasks in the laboratory and self-reports of substance use with ecological momentary assessment (EMA) in the field. Next, we review studies on psychometric properties of smartphone-based behavioral tasks. Finally, we review studies that used both smartphone-based tasks and self-reports with EMA in the field. RESULTS: Overall, studies were scarce and heterogenous both in tasks and in study outcomes. Nevertheless, existing findings are promising and point toward several methodological recommendations: concerning psychometrics, studies show that - although more systematic studies are necessary - task validity and reliability can be improved, for example, by analyzing several measurement sessions at once rather than analyzing sessions separately. Studies that use tasks in the field, moreover, show that power can be improved by choosing sampling schemes that combine time-based with event-based sampling, rather than relying on time-based sampling alone. Increasing sampling frequency can further increase power. However, as this also increases the burden to participants, more research is necessary to determine the ideal sampling frequency for each task. CONCLUSION: Although more research is necessary to systematically study both the psychometrics of smartphone-based tasks and the frequency at which task measures fluctuate, existing studies are promising and reveal important methodological recommendations useful for researchers interested in implementing behavioral tasks in EMA studies.

Emotional maltreatment and neglect impact neural activation upon exclusion in early and mid-adolescence: An event-related fMRI study.

Child maltreatment gives rise to atypical patterns of social functioning with peers which might be particularly pronounced in early adolescence when peer influence typically peaks. Yet, few neuroimaging studies in adolescents use peer interaction paradigms to parse neural correlates of distinct maltreatment exposures. This fMRI study examines effects of abuse, neglect, and emotional maltreatment (EM) among 98 youth (n = 58 maltreated; n = 40 matched controls) using an event-related Cyberball paradigm affording assessment of both social exclusion and inclusion across early and mid-adolescence (≤13.5 years, n = 50; >13.5 years, n = 48). Younger adolescents showed increased activation to social exclusion versus inclusion in regions implicated in mentalizing (e.g., superior temporal gyrus). Individual exposure-specific analyses suggested that neglect and EM coincided with less reduction of activation to social exclusion relative to inclusion in the dorsal anterior cingulate cortex/pre-supplementary motor area (dACC/pre-SMA) among younger versus older adolescents. Integrative follow-up analyses showed that EM accounted for this dACC/pre-SMA activation pattern over and above other exposures. Moreover, age-independent results within respective exposure groups revealed that greater magnitude of neglect predicted blunted exclusion-related activity in the parahippocampal gyrus, while EM predicted increased activation to social exclusion in the precuneus/posterior cingulate cortex.

Sufficient reliability of the behavioral and computational readouts of a probabilistic reversal learning task.

Task-based measures that capture neurocognitive processes can help bridge the gap between brain and behavior. To transfer tasks to clinical application, reliability is a crucial benchmark because it imposes an upper bound to potential correlations with other variables (e.g., symptom or brain data). However, the reliability of many task readouts is low. In this study, we scrutinized the retest reliability of a probabilistic reversal learning task (PRLT) that is frequently used to characterize cognitive flexibility in psychiatric populations. We analyzed data from N = 40 healthy subjects, who completed the PRLT twice. We focused on how individual metrics are derived, i.e., whether data were partially pooled across participants and whether priors were used to inform estimates. We compared the reliability of the resulting indices across sessions, as well as the internal consistency of a selection of indices. We found good to excellent reliability for behavioral indices as derived from mixed-effects models that included data from both sessions. The internal consistency was good to excellent. For indices derived from computational modeling, we found excellent reliability when using hierarchical estimation with empirical priors and including data from both sessions. Our results indicate that the PRLT is well equipped to measure individual differences in cognitive flexibility in reinforcement learning. However, this depends heavily on hierarchical modeling of the longitudinal data (whether sessions are modeled separately or jointly), on estimation methods, and on the combination of parameters included in computational models. We discuss implications for the applicability of PRLT indices in psychiatric research and as diagnostic tools.

Addiction Research Consortium: Losing and regaining control over drug intake (ReCoDe)-From trajectories to mechanisms and interventions.

One of the major risk factors for global death and disability is alcohol, tobacco, and illicit drug use. While there is increasing knowledge with respect to individual factors promoting the initiation and maintenance of substance use disorders (SUDs), disease trajectories involved in losing and regaining control over drug intake (ReCoDe) are still not well described. Our newly formed German Collaborative Research Centre (CRC) on ReCoDe has an interdisciplinary approach funded by the German Research Foundation (DFG) with a 12-year perspective. The main goals of our research consortium are (i) to identify triggers and modifying factors that longitudinally modulate the trajectories of losing and regaining control over drug consumption in real life, (ii) to study underlying behavioral, cognitive, and neurobiological mechanisms, and (iii) to implicate mechanism-based interventions. These goals will be achieved by: (i) using mobile health (m-health) tools to longitudinally monitor the effects of triggers (drug cues, stressors, and priming doses) and modify factors (eg, age, gender, physical activity, and cognitive control) on drug consumption patterns in real-life conditions and in animal models of addiction; (ii) the identification and computational modeling of key mechanisms mediating the effects of such triggers and modifying factors on goal-directed, habitual, and compulsive aspects of behavior from human studies and animal models; and (iii) developing and testing interventions that specifically target the underlying mechanisms for regaining control over drug intake.

Reduced parietofrontal effective connectivity during a working-memory task in people with high delusional ideation

Background: Working-memory impairment is a core cognitive dysfunction in people with schizophrenia and people at mental high risk. Recent imaging studies on working memory have suggested that abnormalities in prefrontal activation and in connectivity between the frontal and parietal regions could be neural underpinnings of the different stages of psychosis. However, it remains to be explored whether comparable alterations are present in people with subclinical levels of psychosis, as experienced by a small proportion of the general population who neither seek help nor show constraints in daily functioning. Methods: We compared 24 people with subclinical high delusional ideation and 24 people with low delusional ideation. Both groups performed an n-back working-memory task during functional magnetic resonance imaging. We characterized frontoparietal effective connectivity using dynamic causal modelling. Results: Compared to people who had low delusional ideation, people with high delusional ideation showed a significant increase in dorsolateral prefrontal activation during the working-memory task, as well as reduced working-memory-dependent parietofrontal effective connectivity in the left hemisphere. Group differences were not evident at the behavioural level. Limitations: The current experimental design did not distinguish among the working-memory subprocesses; it remains unexplored whether differences in connectivity exist at that level. Conclusion: These findings suggest that alterations in the working-memory network are also present in a nonclinical population with psychotic experiences who do not display cognitive deficits. They also suggest that alterations in working-memory-dependent connectivity show a putative continuity along the spectrum of psychotic symptoms.

Modeling subjective relevance in schizophrenia and its relation to aberrant salience.

In schizophrenia, increased aberrant salience to irrelevant events and reduced learning of relevant information may relate to an underlying deficit in relevance detection. So far, subjective estimates of relevance have not been probed in schizophrenia patients. The mechanisms underlying belief formation about relevance and their translation into decisions are unclear. Using novel computational methods, we investigated relevance detection during implicit learning in 42 schizophrenia patients and 42 healthy individuals. Participants underwent functional magnetic resonance imaging while detecting the outcomes in a learning task. These were preceded by cues differing in color and shape, which were either relevant or irrelevant for outcome prediction. We provided a novel definition of relevance based on Bayesian precision and modeled reaction times as a function of relevance weighted unsigned prediction errors (UPE). For aberrant salience, we assessed responses to subjectively irrelevant cue manifestations. Participants learned the contingencies and slowed down their responses following unexpected events. Model selection revealed that individuals inferred the relevance of cue features and used it for behavioral adaption to the relevant cue feature. Relevance weighted UPEs correlated with dorsal anterior cingulate cortex activation and hippocampus deactivation. In patients, the aberrant salience bias to subjectively task-irrelevant information was increased and correlated with decreased striatal UPE activation and increased negative symptoms. This study shows that relevance estimates based on Bayesian precision can be inferred from observed behavior. This underscores the importance of relevance detection as an underlying mechanism for behavioral adaptation in complex environments and enhances the understanding of aberrant salience in schizophrenia.

Dopaminergic modulation of hemodynamic signal variability and the functional connectome during cognitive performance.

Dopamine underlies important aspects of cognition, and has been suggested to boost cognitive performance. However, how dopamine modulates the large-scale cortical dynamics during cognitive performance has remained elusive. Using functional MRI during a working memory task in healthy young human listeners, we investigated the effect of levodopa (l-dopa) on two aspects of cortical dynamics, blood oxygen-level-dependent (BOLD) signal variability and the functional connectome of large-scale cortical networks. We here show that enhanced dopaminergic signaling modulates the two potentially interrelated aspects of large-scale cortical dynamics during cognitive performance, and the degree of these modulations is able to explain inter-individual differences in l-dopa-induced behavioral benefits. Relative to placebo, l-dopa increased BOLD signal variability in task-relevant temporal, inferior frontal, parietal and cingulate regions. On the connectome level, however, l-dopa diminished functional integration across temporal and cingulo-opercular regions. This hypo-integration was expressed as a reduction in network efficiency and modularity in more than two thirds of the participants and to different degrees. Hypo-integration co-occurred with relative hyper-connectivity in paracentral lobule and precuneus, as well as posterior putamen. Both, l-dopa-induced BOLD signal variability modulation and functional connectome modulations proved predictive of an individual's l-dopa-induced benefits in behavioral performance, namely response speed and perceptual sensitivity. Lastly, l-dopa-induced modulations of BOLD signal variability were correlated with l-dopa-induced modulation of nodal connectivity and network efficiency. Our findings underline the role of dopamine in maintaining the dynamic range of, and communication between, cortical systems, and their explanatory power for inter-individual differences in benefits from dopamine during cognitive performance.

Inhibition of Information Flow to the Default Mode Network During Self-Reference Versus Reference to Others.

The default mode network (DMN), a network centered around the cortical midline, shows deactivation during most cognitive tasks and pronounced resting-state connectivity, but is actively engaged in self-reference and social cognition. It is, however, yet unclear how information reaches the DMN during social cognitive processing. Here, we addressed this question using dynamic causal modeling (DCM) of functional magnetic resonance imaging (fMRI) data acquired during self-reference (SR) and reference to others (OR). Both conditions engaged the left inferior frontal gyrus (LIFG), most likely reflecting semantic processing. Within the DMN, self-reference preferentially elicited rostral anterior cingulate and ventromedial prefrontal cortex (rACC/vmPFC) activity, whereas OR engaged posterior cingulate and precuneus (PCC/PreCun). DCM revealed that the regulation of information flow to the DMN was primarily inhibitory. Most prominently, SR elicited inhibited information flow from the LIFG to the PCC/PreCun, while OR was associated with suppression of the connectivity from the LIFG to the rACC/vmPFC. These results suggest that task-related DMN activation is enabled by inhibitory down-regulation of task-irrelevant information flow when switching from rest to stimulus-specific processing.

Ventral striatal dopamine reflects behavioral and neural signatures of model-based control during sequential decision making.

Dual system theories suggest that behavioral control is parsed between a deliberative "model-based" and a more reflexive "model-free" system. A balance of control exerted by these systems is thought to be related to dopamine neurotransmission. However, in the absence of direct measures of human dopamine, it remains unknown whether this reflects a quantitative relation with dopamine either in the striatum or other brain areas. Using a sequential decision task performed during functional magnetic resonance imaging, combined with striatal measures of dopamine using [(18)F]DOPA positron emission tomography, we show that higher presynaptic ventral striatal dopamine levels were associated with a behavioral bias toward more model-based control. Higher presynaptic dopamine in ventral striatum was associated with greater coding of model-based signatures in lateral prefrontal cortex and diminished coding of model-free prediction errors in ventral striatum. Thus, interindividual variability in ventral striatal presynaptic dopamine reflects a balance in the behavioral expression and the neural signatures of model-free and model-based control. Our data provide a novel perspective on how alterations in presynaptic dopamine levels might be accompanied by a disruption of behavioral control as observed in aging or neuropsychiatric diseases such as schizophrenia and addiction.

Behavioral and Neural Signatures of Reduced Updating of Alternative Options in Alcohol-Dependent Patients during Flexible Decision-Making.

Addicted individuals continue substance use despite the knowledge of harmful consequences and often report having no choice but to consume. Computational psychiatry accounts have linked this clinical observation to difficulties in making flexible and goal-directed decisions in dynamic environments via consideration of potential alternative choices. To probe this in alcohol-dependent patients (n = 43) versus healthy volunteers (n = 35), human participants performed an anticorrelated decision-making task during functional neuroimaging. Via computational modeling, we investigated behavioral and neural signatures of inference regarding the alternative option. While healthy control subjects exploited the anticorrelated structure of the task to guide decision-making, alcohol-dependent patients were relatively better explained by a model-free strategy due to reduced inference on the alternative option after punishment. Whereas model-free prediction error signals were preserved, alcohol-dependent patients exhibited blunted medial prefrontal signatures of inference on the alternative option. This reduction was associated with patients' behavioral deficit in updating the alternative choice option and their obsessive-compulsive drinking habits. All results remained significant when adjusting for potential confounders (e.g., neuropsychological measures and gray matter density). A disturbed integration of alternative choice options implemented by the medial prefrontal cortex appears to be one important explanation for the puzzling question of why addicted individuals continue drug consumption despite negative consequences. SIGNIFICANCE STATEMENT: In addiction, patients maintain substance use despite devastating consequences and often report having no choice but to consume. These clinical observations have been theoretically linked to disturbed mechanisms of inference, for example, to difficulties when learning statistical regularities of the environmental structure to guide decisions. Using computational modeling, we demonstrate disturbed inference on alternative choice options in alcohol addiction. Patients neglecting "what might have happened" was accompanied by blunted coding of inference regarding alternative choice options in the medial prefrontal cortex. An impaired integration of alternative choice options implemented by the medial prefrontal cortex might contribute to ongoing drug consumption in the face of evident negative consequences.

Targeted intervention: Computational approaches to elucidate and predict relapse in alcoholism.

Alcohol use disorder (AUD) and addiction in general is characterized by failures of choice resulting in repeated drug intake despite severe negative consequences. Behavioral change is hard to accomplish and relapse after detoxification is common and can be promoted by consumption of small amounts of alcohol as well as exposure to alcohol-associated cues or stress. While those environmental factors contributing to relapse have long been identified, the underlying psychological and neurobiological mechanism on which those factors act are to date incompletely understood. Based on the reinforcing effects of drugs of abuse, animal experiments showed that drug, cue and stress exposure affect Pavlovian and instrumental learning processes, which can increase salience of drug cues and promote habitual drug intake. In humans, computational approaches can help to quantify changes in key learning mechanisms during the development and maintenance of alcohol dependence, e.g. by using sequential decision making in combination with computational modeling to elucidate individual differences in model-free versus more complex, model-based learning strategies and their neurobiological correlates such as prediction error signaling in fronto-striatal circuits. Computational models can also help to explain how alcohol-associated cues trigger relapse: mechanisms such as Pavlovian-to-Instrumental Transfer can quantify to which degree Pavlovian conditioned stimuli can facilitate approach behavior including alcohol seeking and intake. By using generative models of behavioral and neural data, computational approaches can help to quantify individual differences in psychophysiological mechanisms that underlie the development and maintenance of AUD and thus promote targeted intervention.

Reversal learning strategy in adolescence is associated with prefrontal cortex activation.

Adolescence is a critical maturation period for human cognitive control and executive function. In this study, a large sample of adolescents (n = 85) performed a reversal learning task during functional magnetic resonance imaging. We analyzed behavioral data using a reinforcement learning model to provide individually fitted parameters and imaging data with regard to reward prediction errors (PE). Following a model-based approach, we formed two groups depending on whether individuals tended to update expectations predominantly for the chosen stimulus or also for the unchosen one. These groups significantly differed in their problem behavior score obtained using the child behavior checklist (CBCL) and in a measure of their developmental stage. Imaging results showed that dorsolateral striatal areas covaried with PE. Participants who relied less on learning based on task structure showed less prefrontal activation compared with participants who relied more on task structure. An exploratory analysis revealed that PE-related activity was associated with pubertal development in prefrontal areas, insula and anterior cingulate. These findings support the hypothesis that the prefrontal cortex is implicated in mediating flexible goal-directed behavioral control.

Prefrontal and Striatal Glutamate Differently Relate to Striatal Dopamine: Potential Regulatory Mechanisms of Striatal Presynaptic Dopamine Function?

Theoretical and animal work has proposed that prefrontal cortex (PFC) glutamate inhibits dopaminergic inputs to the ventral striatum (VS) indirectly, whereas direct VS glutamatergic afferents have been suggested to enhance dopaminergic inputs to the VS. In the present study, we aimed to investigate relationships of glutamate and dopamine measures in prefrontostriatal circuitries of healthy humans. We hypothesized that PFC and VS glutamate, as well as their balance, are differently associated with VS dopamine. Glutamate concentrations in the left lateral PFC and left striatum were assessed using 3-Tesla proton magnetic resonance spectroscopy. Striatal presynaptic dopamine synthesis capacity was measured by fluorine-18-l-dihydroxyphenylalanine (F-18-FDOPA) positron emission tomography. First, a negative relationship was observed between glutamate concentrations in lateral PFC and VS dopamine synthesis capacity (n = 28). Second, a positive relationship was revealed between striatal glutamate and VS dopamine synthesis capacity (n = 26). Additionally, the intraindividual difference between PFC and striatal glutamate concentrations correlated negatively with VS dopamine synthesis capacity (n = 24). The present results indicate an involvement of a balance in PFC and striatal glutamate in the regulation of VS dopamine synthesis capacity. This notion points toward a potential mechanism how VS presynaptic dopamine levels are kept in a fine-tuned range. A disruption of this mechanism may account for alterations in striatal dopamine turnover as observed in mental diseases (e.g., in schizophrenia). SIGNIFICANCE STATEMENT: The present work demonstrates complementary relationships between prefrontal and striatal glutamate and ventral striatal presynaptic dopamine using human imaging measures: a negative correlation between prefrontal glutamate and presynaptic dopamine and a positive relationship between striatal glutamate and presynaptic dopamine are revealed. The results may reflect a regulatory role of prefrontal and striatal glutamate for ventral striatal presynaptic dopamine levels. Such glutamate-dopamine relationships improve our understanding of neurochemical interactions in prefrontostriatal circuits and have implications for the neurobiology of mental disease.