Computational heterogeneity in the human mesencephalic dopamine system

A computational investigation of cis-gene regulation in evolution

In biological processes involving gene networks, genes regulate other genes that determine the phenotypic traits. Gene regulation plays an important role in evolutionary dynamics. In a genetic algorithm, a trans-gene regulatory mechanism was shown to speed up adaptation and evolution. Here, we examine the effect of cis-gene regulation on an adaptive system. The model is haploid. A chromosome is partitioned into regulatory loci and structural loci. The regulatory genes regulate the expression and functioning of structural genes via the cis-elements in a probabilistic manner. In the simulation, the change in the allele frequency, the mean population fitness and the efficiency of phenotypic selection are monitored. Cis-gene regulation increases adaption and accelerates the evolutionary process in comparison with the case involving absence of gene regulation. Some special features of the simulation results are as follows. A low ratio of regulatory loci and structural loci gives higher adaptation for fixed total number of loci. Plasticity is advantageous beyond a threshold value. Adaptation is better for large number of total loci when the ratio of regulatory loci to structural loci is one. However, it reaches a saturation beyond which the increase in the total loci is not advantageous. Efficiency of the phenotypic selection is higher for larger value of the initial plasticity.

Brain reward response in adolescents and young adults with anorexia nervosa is moderated by changes in body weight and sweetness perception

OBJECTIVE

Anorexia nervosa (AN) is a severe psychiatric illness with complex etiology. Recently, we found elevated striatal brain response to sweet taste stimuli in adolescents and young adults with AN. Here, we tested the hypothesis that nutritional rehabilitation normalizes prediction error activation, a measure for dopamine-related reward circuit response, to salient caloric taste stimuli in AN.

METHODS

A total of 28 individuals with AN (age = 16 ± 2 years; body mass index [BMI] = 16 ± 1) who previously underwent brain imaging while performing a taste prediction error task using sucrose as salient caloric stimulus, participated in a second brain imaging scan (BMI = 18 ± 1) after intensive specialized eating disorder treatment (41 ± 15 days). A total of 31 healthy controls (age = 16 ± 3 years; BMI = 21 ± 2) were also studied on two occasions.

RESULTS

At baseline, individuals with AN demonstrated an elevated salience response in bilateral caudate head and nucleus accumbens, and right ventral striatum. At the second scan, elevated response was only found in the right nucleus accumbens. A moderator analysis indicated that greater increase in BMI and greater decrease in sweetness perception predicted lesser prediction error response at the second scan in AN.

CONCLUSION

Consistent with the previously reported monetary stimulus-response, elevated taste prediction error response in AN was largely absent after weight restoration. This study indicates that changes in BMI and sweet taste perception are independent moderators of change of brain salience response in adolescents and young adults with AN. The study points toward dynamic changes in the brain reward circuitry in AN and highlights the importance of nutrition and weight restoration in that process.

PUBLIC SIGNIFICANCE STATEMENT

AN is a severe psychiatric illness. Biological factors that integrate neurobiology and behavior could become important targets to improve treatment outcome. This study highlights the importance of weight normalization and taste perception the normalization of brain function, and food type or taste-specific interventions could help in the recovery process. Furthermore, the study suggests that food-related and nonfood-related reward processing adapts to illness state in AN.

Reinforcement learning with associative or discriminative generalization across states and actions: fMRI at 3 T and 7 T

The model-free algorithms of "reinforcement learning" (RL) have gained clout across disciplines, but so too have model-based alternatives. The present study emphasizes other dimensions of this model space in consideration of associative or discriminative generalization across states and actions. This "generalized reinforcement learning" (GRL) model, a frugal extension of RL, parsimoniously retains the single reward-prediction error (RPE), but the scope of learning goes beyond the experienced state and action. Instead, the generalized RPE is efficiently relayed for bidirectional counterfactual updating of value estimates for other representations. Aided by structural information but as an implicit rather than explicit cognitive map, GRL provided the most precise account of human behavior and individual differences in a reversal-learning task with hierarchical structure that encouraged inverse generalization across both states and actions. Reflecting inference that could be true, false (i.e., overgeneralization), or absent (i.e., undergeneralization), state generalization distinguished those who learned well more so than action generalization. With high-resolution high-field fMRI targeting the dopaminergic midbrain, the GRL model's RPE signals (alongside value and decision signals) were localized within not only the striatum but also the substantia nigra and the ventral tegmental area, including specific effects of generalization that also extend to the hippocampus. Factoring in generalization as a multidimensional process in value-based learning, these findings shed light on complexities that, while challenging classic RL, can still be resolved within the bounds of its core computations.

Association of Brain Reward Response With Body Mass Index and Ventral Striatal-Hypothalamic Circuitry Among Young Women With Eating Disorders

IMPORTANCE

Eating disorders are severe psychiatric disorders; however, disease models that cross subtypes and integrate behavior and neurobiologic factors are lacking.

OBJECTIVE

To assess brain response during unexpected receipt or omission of a salient sweet stimulus across a large sample of individuals with eating disorders and healthy controls and test for evidence of whether this brain response is associated with the ventral striatal-hypothalamic circuitry, which has been associated with food intake control, and whether salient stimulus response and eating disorder related behaviors are associated.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional functional brain imaging study, young adults across the eating disorder spectrum were matched with healthy controls at a university brain imaging facility and eating disorder treatment program. During a sucrose taste classic conditioning paradigm, violations of learned associations between conditioned visual and unconditioned taste stimuli evoked the dopamine-related prediction error. Dynamic effective connectivity during expected sweet taste receipt was studied to investigate hierarchical brain activation between food intake relevant brain regions. The study was conducted from June 2014 to November 2019. Data were analyzed from December 2019 to February 2020.

MAIN OUTCOMES AND MEASURES

Prediction error brain reward response across insula and striatum; dynamic effective connectivity between hypothalamus and ventral striatum; and demographic and behavior variables and their correlations with prediction error brain response and connectivity edge coefficients.

RESULTS

Of 317 female participants (197 with eating disorders and 120 healthy controls), the mean (SD) age was 23.8 (5.6) years and mean (SD) body mass index was 20.8 (5.4). Prediction error response was elevated in participants with anorexia nervosa (Wilks λ, 0.843; P = .001) and in participants with eating disorders inversely correlated with body mass index (left nucleus accumbens: r = -0.291; 95% CI, -0.413 to -0.167; P < .001; right dorsal anterior insula: r = -0.228; 95% CI, -0.366 to -0.089; P = .001), eating disorder inventory-3 binge eating tendency (left nucleus accumbens: r = -0.207; 95% CI, -0.333 to -0.073; P = .004; right dorsal anterior insula: r = -0.220; 95% CI, -0.354 to -0.073; P = .002), and trait anxiety (left nucleus accumbens: r = -0.148; 95% CI, -0.288 to -0.003; P = .04; right dorsal anterior insula: r = -0.221; 95% CI, -0.357 to -0.076; P = .002). Ventral striatal to hypothalamus directed connectivity was positively correlated with ventral striatal prediction error in eating disorders (r = 0.189; 95% CI, 0.045-0.324; P = .01) and negatively correlated with feeling out of control after eating (right side: r = -0.328; 95% CI, -0.480 to -0.164; P < .001; left side: r = -0.297; 95% CI, -0.439 to -0.142; P = .001).

CONCLUSIONS AND RELEVANCE

The results of this cross-sectional imaging study support that body mass index modulates prediction error and food intake control circuitry in the brain. Once altered, this circuitry may reinforce eating disorder behaviors when paired with behavioral traits associated with overeating or undereating.

From Desire to Dread-A Neurocircuitry Based Model for Food Avoidance in Anorexia Nervosa

Anorexia nervosa is a severe psychiatric illness associated with food avoidance. Animal models from Berridge et al. over the past decade showed that environmental ambience, pleasant or fear inducing, can trigger either appetitive (desire) or avoidance (dread) behaviors in animals via frontal cortex, nucleus accumbens dopamine D1 and D2 receptors, and hypothalamus. Those mechanisms could be relevant for understanding anorexia nervosa. However, models that translate animal research to explain the psychopathology of anorexia nervosa are sparse. This article reviews animal and human research to find evidence for whether this model can explain food avoidance behaviors in anorexia nervosa. Research on anorexia nervosa suggests fear conditioning to food, activation of the corticostriatal brain circuitry, sensitization of ventral striatal dopamine response, and alterations in hypothalamic function. The results support the applicability of the animal neurocircuitry derived model and provide directions to further study the pathophysiology that underlies anorexia nervosa.

Neural representation of prediction error signals in substance users

Abnormal decision making can result in detrimental outcomes of clinical importance, and decision making is strongly linked to neural prediction error signalling. Activation likelihood estimation (ALE) meta-analyses were used to examine the neural correlates of prediction error signals of individuals taking different types of substances and healthy controls with contrast and conjunction analyses. Twenty-eight studies were included in the meta-analysis, representing 424 substance users' individuals and 834 healthy control individuals. Robust brain activity associated with prediction error signals in substance users was found for the bilateral striatum and insula. Healthy control subjects also activated bilateral striatum, midbrain, right insula and right medial-inferior frontal gyrus. Compared with healthy controls, substance users showed blunted activity in the bilateral putamen, right medial-inferior frontal gyrus and insula. The current meta-analysis of cross-sectional findings investigated neural prediction error signals in substance users. PE abnormalities in substance users might be related to poor decision making. In conclusion, the present study helps identify the pathophysiological underpinnings of maladaptive decision making in substance users.

Financial gain- and loss-related BOLD signals in the human ventral tegmental area and substantia nigra pars compacta

Neurons in the ventral tegmental area (VTA) and substantia nigra pars compacta (SNC) play central roles in reward-related behaviours. Nonhuman animal studies suggest that these neurons also process aversive events. However, our understanding of how the human VTA and SNC responds to such events is limited and has been hindered by the technical challenge of using functional magnetic resonance imaging (fMRI) to investigate a small structure where the signal is particularly vulnerable to physiological noise. Here we show, using methods optimized specifically for the midbrain (including high-resolution imaging, a novel registration protocol, and physiological noise modelling), a BOLD (blood-oxygen-level dependent) signal to both financial gain and loss in the VTA and SNC, along with a response to nil outcomes that are better or worse than expected in the VTA. Taken together, these findings suggest that the human VTA and SNC are involved in the processing of both appetitive and aversive financial outcomes in humans.

Human substantia nigra and ventral tegmental area involvement in computing social error signals during the ultimatum game

As models of shared expectations, social norms play an essential role in our societies. Since our social environment is changing constantly, our internal models of it also need to change. In humans, there is mounting evidence that neural structures such as the insula and the ventral striatum are involved in detecting norm violation and updating internal models. However, because of methodological challenges, little is known about the possible involvement of midbrain structures in detecting norm violation and updating internal models of our norms. Here, we used high-resolution cardiac-gated functional magnetic resonance imaging and a norm adaptation paradigm in healthy adults to investigate the role of the substantia nigra/ventral tegmental area (SN/VTA) complex in tracking signals related to norm violation that can be used to update internal norms. We show that the SN/VTA codes for the norm’s variance prediction error (PE) and norm PE with spatially distinct regions coding for negative and positive norm PE. These results point to a common role played by the SN/VTA complex in supporting both simple reward-based and social decision making.

No association of goal-directed and habitual control with alcohol consumption in young adults

Alcohol dependence is a mental disorder that has been associated with an imbalance in behavioral control favoring model-free habitual over model-based goal-directed strategies. It is as yet unknown, however, whether such an imbalance reflects a predisposing vulnerability or results as a consequence of repeated and/or excessive alcohol exposure. We, therefore, examined the association of alcohol consumption with model-based goal-directed and model-free habitual control in 188 18-year-old social drinkers in a two-step sequential decision-making task while undergoing functional magnetic resonance imaging before prolonged alcohol misuse could have led to severe neurobiological adaptations. Behaviorally, participants showed a mixture of model-free and model-based decision-making as observed previously. Measures of impulsivity were positively related to alcohol consumption. In contrast, neither model-free nor model-based decision weights nor the trade-off between them were associated with alcohol consumption. There were also no significant associations between alcohol consumption and neural correlates of model-free or model-based decision quantities in either ventral striatum or ventromedial prefrontal cortex. Exploratory whole-brain functional magnetic resonance imaging analyses with a lenient threshold revealed early onset of drinking to be associated with an enhanced representation of model-free reward prediction errors in the posterior putamen. These results suggest that an imbalance between model-based goal-directed and model-free habitual control might rather not be a trait marker of alcohol intake per se.

Distinct prediction errors in mesostriatal circuits of the human brain mediate learning about the values of both states and actions: evidence from high-resolution fMRI

Prediction-error signals consistent with formal models of "reinforcement learning" (RL) have repeatedly been found within dopaminergic nuclei of the midbrain and dopaminoceptive areas of the striatum. However, the precise form of the RL algorithms implemented in the human brain is not yet well determined. Here, we created a novel paradigm optimized to dissociate the subtypes of reward-prediction errors that function as the key computational signatures of two distinct classes of RL models-namely, "actor/critic" models and action-value-learning models (e.g., the Q-learning model). The state-value-prediction error (SVPE), which is independent of actions, is a hallmark of the actor/critic architecture, whereas the action-value-prediction error (AVPE) is the distinguishing feature of action-value-learning algorithms. To test for the presence of these prediction-error signals in the brain, we scanned human participants with a high-resolution functional magnetic-resonance imaging (fMRI) protocol optimized to enable measurement of neural activity in the dopaminergic midbrain as well as the striatal areas to which it projects. In keeping with the actor/critic model, the SVPE signal was detected in the substantia nigra. The SVPE was also clearly present in both the ventral striatum and the dorsal striatum. However, alongside these purely state-value-based computations we also found evidence for AVPE signals throughout the striatum. These high-resolution fMRI findings suggest that model-free aspects of reward learning in humans can be explained algorithmically with RL in terms of an actor/critic mechanism operating in parallel with a system for more direct action-value learning.

Separate mesocortical and mesolimbic pathways encode effort and reward learning signals

Optimal decision making mandates organisms learn the relevant features of choice options. Likewise, knowing how much effort we should expend can assume paramount importance. A mesolimbic network supports reward learning, but it is unclear whether other choice features, such as effort learning, rely on this same network. Using computational fMRI, we show parallel encoding of effort and reward prediction errors (PEs) within distinct brain regions, with effort PEs expressed in dorsomedial prefrontal cortex and reward PEs in ventral striatum. We show a common mesencephalic origin for these signals evident in overlapping, but spatially dissociable, dopaminergic midbrain regions expressing both types of PE. During action anticipation, reward and effort expectations were integrated in ventral striatum, consistent with a computation of an overall net benefit of a stimulus. Thus, we show that motivationally relevant stimulus features are learned in parallel dopaminergic pathways, with formation of an integrated utility signal at choice.

The Effects of Age, from Young to Middle Adulthood, and Gender on Resting State Functional Connectivity of the Dopaminergic Midbrain

Dysfunction of the dopaminergic ventral tegmental area (VTA) and substantia nigra pars compacta (SNc) is implicated in psychiatric disorders including attention-deficit/ hyperactivity disorder (ADHD), addiction, schizophrenia and movement disorders such as Parkinson's disease (PD). Although the prevalence of these disorders varies by age and sex, the underlying neural mechanism is not well understood. The objective of this study was to delineate the distinct resting state functional connectivity (rsFC) of the VTA and SNc and examine the effects of age, from young to middle-adulthood, and sex on the rsFC of these two dopaminergic structures in a data set of 250 healthy adults (18-49 years of age, 104 men). Using blood oxygenation level dependent (BOLD) signals, we correlated the time course of the VTA and SNc to the time courses of all other brain voxels. At a corrected threshold, paired t-test showed stronger VTA connectivity to bilateral angular gyrus and superior/middle and orbital frontal regions and stronger SNc connectivity to the insula, thalamus, parahippocampal gyrus (PHG) and amygdala. Compared to women, men showed a stronger VTA/SNc connectivity to the left posterior orbital gyrus. In linear regressions, men but not women showed age-related changes in VTA/SNc connectivity to a number of cortical and cerebellar regions. Supporting shared but also distinct cerebral rsFC of the VTA and SNc and gender differences in age-related changes from young and middle adulthood in VTA/SNc connectivity, these new findings help advance our understanding of the neural bases of many neuropsychiatric illnesses that implicate the dopaminergic systems.

Dopamine Does Double Duty in Motivating Cognitive Effort

Cognitive control is subjectively costly, suggesting that engagement is modulated in relationship to incentive state. Dopamine appears to play key roles. In particular, dopamine may mediate cognitive effort by two broad classes of functions: (1) modulating the functional parameters of working memory circuits subserving effortful cognition, and (2) mediating value-learning and decision-making about effortful cognitive action. Here, we tie together these two lines of research, proposing how dopamine serves "double duty", translating incentive information into cognitive motivation.

Computational neuroimaging strategies for single patient predictions

Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches - Bayesian model selection and generative embedding - which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning.

Safety out of control: dopamine and defence

We enjoy a sophisticated understanding of how animals learn to predict appetitive outcomes and direct their behaviour accordingly. This encompasses well-defined learning algorithms and details of how these might be implemented in the brain. Dopamine has played an important part in this unfolding story, appearing to embody a learning signal for predicting rewards and stamping in useful actions, while also being a modulator of behavioural vigour. By contrast, although choosing correct actions and executing them vigorously in the face of adversity is at least as important, our understanding of learning and behaviour in aversive settings is less well developed. We examine aversive processing through the medium of the role of dopamine and targets such as D2 receptors in the striatum. We consider critical factors such as the degree of control that an animal believes it exerts over key aspects of its environment, the distinction between 'better' and 'good' actual or predicted future states, and the potential requirement for a particular form of opponent to dopamine to ensure proper calibration of state values.

Distinct Contributions of Ventromedial and Dorsolateral Subregions of the Human Substantia Nigra to Appetitive and Aversive Learning

The role of neurons in the substantia nigra (SN) and ventral tegmental area (VTA) of the midbrain in contributing to the elicitation of reward prediction errors during appetitive learning has been well established. Less is known about the differential contribution of these midbrain regions to appetitive versus aversive learning, especially in humans. Here we scanned human participants with high-resolution fMRI focused on the SN and VTA while they participated in a sequential Pavlovian conditioning paradigm involving an appetitive outcome (a pleasant juice), as well as an aversive outcome (an unpleasant bitter and salty flavor). We found a degree of regional specialization within the SN: Whereas a region of ventromedial SN correlated with a temporal difference reward prediction error during appetitive Pavlovian learning, a dorsolateral area correlated instead with an aversive expected value signal in response to the most distal cue, and to a reward prediction error in response to the most proximal cue to the aversive outcome. Furthermore, participants' affective reactions to both the appetitive and aversive conditioned stimuli more than 1 year after the fMRI experiment was conducted correlated with activation in the ventromedial and dorsolateral SN obtained during the experiment, respectively. These findings suggest that, whereas the human ventromedial SN contributes to long-term learning about rewards, the dorsolateral SN may be particularly important for long-term learning in aversive contexts.

SIGNIFICANCE STATEMENT

The role of the substantia nigra (SN) and ventral tegmental area (VTA) in appetitive learning is well established, but less is known about their contribution to aversive compared with appetitive learning, especially in humans. We used high-resolution fMRI to measure activity in the SN and VTA while participants underwent higher-order Pavlovian learning. We found a regional specialization within the SN: a ventromedial area was selectively engaged during appetitive learning, and a dorsolateral area during aversive learning. Activity in these areas predicted affective reactions to appetitive and aversive conditioned stimuli over 1 year later. These findings suggest that, whereas the human ventromedial SN contributes to long-term learning about rewards, the dorsolateral SN may be particularly important for long-term learning in aversive contexts.

Using fMRI to study reward processing in humans: past, present, and future

Functional magnetic resonance imaging (fMRI) is a noninvasive tool used to probe cognitive and affective processes. Although fMRI provides indirect measures of neural activity, the advent of fMRI has allowed for1) the corroboration of significant animal findings in the human brain, and2) the expansion of models to include more common human attributes that inform behavior. In this review, we briefly consider the neural basis of the blood oxygenation level dependent signal to set up a discussion of how fMRI studies have applied it in examining cognitive models in humans and the promise of using fMRI to advance such models. Specifically, we illustrate the contribution that fMRI has made to the study of reward processing, focusing on the role of the striatum in encoding reward-related learning signals that drive anticipatory and consummatory behaviors. For instance, we discuss how fMRI can be used to link neural signals (e.g., striatal responses to rewards) to individual differences in behavior and traits. While this functional segregation approach has been constructive to our understanding of reward-related functions, many fMRI studies have also benefitted from a functional integration approach that takes into account how interconnected regions (e.g., corticostriatal circuits) contribute to reward processing. We contend that future work using fMRI will profit from using a multimodal approach, such as combining fMRI with noninvasive brain stimulation tools (e.g., transcranial electrical stimulation), that can identify causal mechanisms underlying reward processing. Consequently, advancements in implementing fMRI will promise new translational opportunities to inform our understanding of psychopathologies.

Cognitive neuroscience of human counterfactual reasoning

Counterfactual reasoning is a hallmark of human thought, enabling the capacity to shift from perceiving the immediate environment to an alternative, imagined perspective. Mental representations of counterfactual possibilities (e.g., imagined past events or future outcomes not yet at hand) provide the basis for learning from past experience, enable planning and prediction, support creativity and insight, and give rise to emotions and social attributions (e.g., regret and blame). Yet remarkably little is known about the psychological and neural foundations of counterfactual reasoning. In this review, we survey recent findings from psychology and neuroscience indicating that counterfactual thought depends on an integrative network of systems for affective processing, mental simulation, and cognitive control. We review evidence to elucidate how these mechanisms are systematically altered through psychiatric illness and neurological disease. We propose that counterfactual thinking depends on the coordination of multiple information processing systems that together enable adaptive behavior and goal-directed decision making and make recommendations for the study of counterfactual inference in health, aging, and disease.

Expression of the μ, κ, and δ-opioid receptors and tyrosine hydroxylase in MN9D cells

Dopaminergic neurons are suggested to be a critical physiopathology substrate for addiction disorders. It is not well known whether the clonal mesencephalic dopaminergic cell line MN9D cells can be applied to study morphine addiction. Immunofluorescence staining and reverse transcription-polymerase chain reaction (RT-PCR) were used to detect protein and mRNA expression of the μ, κ, and δ-opioid receptors in MN9D cells. Immunofluorescence staining of TH was applied to quantify the number of dopaminergic neurons. The results showed that the μ, κ, and δ-receptors were all expressed in MN9D cells, and the number of TH-positive cells was significantly greater in the MN9D cells than SH-SY5Y cells. The data suggest that MN9D cells can be used as an in vitro models in future studies to explore the mechanisms of morphine addiction related to dopaminergic neurons.

Distinct midbrain and habenula pathways are involved in processing aversive events in humans

Emerging evidence implicates the midbrain dopamine system and its interactions with the lateral habenula in processing aversive information and learning to avoid negative outcomes. We examined neural responses to unexpected, aversive events using methods specialized for imaging the midbrain and habenula in humans. Robust activation to aversive relative to neutral events was observed in the habenula and two regions within the ventral midbrain: one located within the ventral tegmental area (VTA) and the other in the substantia nigra (SN). Aversive processing increased functional connectivity between the VTA and the habenula, putamen, and medial prefrontal cortex, whereas the SN exhibited a different pattern of functional connectivity. Our findings provide evidence for a network comprising the VTA and SN, the habenula, and mesocorticolimbic structures that supports processing aversive events in humans.

The subcortical cocktail problem; mixed signals from the subthalamic nucleus and substantia nigra

The subthalamic nucleus and the directly adjacent substantia nigra are small and important structures in the basal ganglia. Functional magnetic resonance imaging studies have shown that the subthalamic nucleus and substantia nigra are selectively involved in response inhibition, conflict processing, and adjusting global and selective response thresholds. However, imaging these nuclei is complex, because they are in such close proximity, they can vary in location, and are very small relative to the resolution of most fMRI sequences. Here, we investigated the consistency in localization of these nuclei in BOLD fMRI studies, comparing reported coordinates with probabilistic atlas maps of young human participants derived from ultra-high resolution 7T MRI scanning. We show that the fMRI signal reported in previous studies is likely not unequivocally arising from the subthalamic nucleus but represents a mixture of subthalamic nucleus, substantia nigra, and surrounding tissue. Using a simulation study, we also tested to what extent spatial smoothing, often used in fMRI preprocessing pipelines, influences the mixture of BOLD signals. We propose concrete steps how to analyze fMRI BOLD data to allow inferences about the functional role of small subcortical nuclei like the subthalamic nucleus and substantia nigra.

Mechanisms of motivation-cognition interaction: challenges and opportunities

Recent years have seen a rejuvenation of interest in studies of motivation-cognition interactions arising from many different areas of psychology and neuroscience. The present issue of Cognitive, Affective, & Behavioral Neuroscience provides a sampling of some of the latest research from a number of these different areas. In this introductory article, we provide an overview of the current state of the field, in terms of key research developments and candidate neural mechanisms receiving focused investigation as potential sources of motivation-cognition interaction. However, our primary goal is conceptual: to highlight the distinct perspectives taken by different research areas, in terms of how motivation is defined, the relevant dimensions and dissociations that are emphasized, and the theoretical questions being targeted. Together, these distinctions present both challenges and opportunities for efforts aiming toward a more unified and cross-disciplinary approach. We identify a set of pressing research questions calling for this sort of cross-disciplinary approach, with the explicit goal of encouraging integrative and collaborative investigations directed toward them.

Effects of fictive reward on rat's choice behavior

Choices of humans and non-human primates are influenced by both actually experienced and fictive outcomes. To test whether this is also the case in rodents, we examined rat's choice behavior in a binary choice task in which variable magnitudes of actual and fictive rewards were delivered. We found that the animal's choice was significantly influenced by the magnitudes of both actual and fictive rewards in the previous trial. A model-based analysis revealed, however, that the effect of fictive reward was more transient and influenced mostly the choice in the next trial, whereas the effect of actual reward was more sustained, consistent with incremental learning of action values. Our results suggest that the capacity to modify future choices based on fictive outcomes might be shared by many different animal species, but fictive outcomes are less effective than actual outcomes in the incremental value learning system.