Effects of asymmetric dopamine depletion on sensitivity to rewarding and aversive stimuli in Parkinson's disease

Imbalanced motivated behaviors according to motor sign asymmetry in drug-naïve Parkinson's disease

Few studies have considered the influence of motor sign asymmetry on motivated behaviors in de novo drug-naïve Parkinson's disease (PD). We tested whether motor sign asymmetry could be associated with different motivated behavior patterns in de novo drug-naïve PD. We performed a cross-sectional study in 128 de novo drug-naïve PD patients and used the Ardouin Scale of Behavior in Parkinson's disease (ASBPD) to assess a set of motivated behaviors. We assessed motor asymmetry based on (i) side of motor onset and (ii) MDS-UPDRS motor score, then we compared right hemibody Parkinson's disease to left hemibody Parkinson's disease. According to the MDS-UPDRS motor score, patients with de novo right hemibody PD had significantly lower frequency of approach behaviors (p = 0.031), including nocturnal hyperactivity (p = 0.040), eating behavior (p = 0.040), creativity (p = 0.040), and excess of motivation (p = 0.017) than patients with de novo left hemibody PD. Patients with de novo left hemibody PD did not significantly differ from those with de novo right hemibody PD regarding avoidance behaviors including apathy, anxiety and depression. Our findings suggest that motor sign asymmetry may be associated with an imbalance between motivated behaviors in de novo drug-naïve Parkinson's disease.

Longitudinal clustering analysis and prediction of Parkinson's disease progression using radiomics and hybrid machine learning

BACKGROUND

We employed machine learning approaches to (I) determine distinct progression trajectories in Parkinson's disease (PD) (unsupervised clustering task), and (II) predict progression trajectories (supervised prediction task), from early (years 0 and 1) data, making use of clinical and imaging features.

METHODS

We studied PD-subjects derived from longitudinal datasets (years 0, 1, 2 & 4; Parkinson's Progressive Marker Initiative). We extracted and analyzed 981 features, including motor, non-motor, and radiomics features extracted for each region-of-interest (ROIs: left/right caudate and putamen) using our standardized standardized environment for radiomics analysis (SERA) radiomics software. Segmentation of ROIs on dopamine transposer - single photon emission computed tomography (DAT SPECT) images were performed via magnetic resonance images (MRI). After performing cross-sectional clustering on 885 subjects (original dataset) to identify disease subtypes, we identified optimal longitudinal trajectories using hybrid machine learning systems (HMLS), including principal component analysis (PCA) + K-Means algorithms (KMA) followed by Bayesian information criterion (BIC), Calinski-Harabatz criterion (CHC), and elbow criterion (EC). Subsequently, prediction of the identified trajectories from early year data was performed using multiple HMLSs including 16 Dimension Reduction Algorithms (DRA) and 10 classification algorithms.

RESULTS

We identified 3 distinct progression trajectories. Hotelling's t squared test (HTST) showed that the identified trajectories were distinct. The trajectories included those with (I, II) disease escalation (2 trajectories, 27% and 38% of patients) and (III) stable disease (1 trajectory, 35% of patients). For trajectory prediction from early year data, HMLSs including the stochastic neighbor embedding algorithm (SNEA, as a DRA) as well as locally linear embedding algorithm (LLEA, as a DRA), linked with the new probabilistic neural network classifier (NPNNC, as a classifier), resulted in accuracies of 78.4% and 79.2% respectively, while other HMLSs such as SNEA + Lib_SVM (library for support vector machines) and t_SNE (t-distributed stochastic neighbor embedding) + NPNNC resulted in 76.5% and 76.1% respectively.

CONCLUSIONS

This study moves beyond cross-sectional PD subtyping to clustering of longitudinal disease trajectories. We conclude that combining medical information with SPECT-based radiomics features, and optimal utilization of HMLSs, can identify distinct disease trajectories in PD patients, and enable effective prediction of disease trajectories from early year data.

Exploring reward-related attention selectivity deficits in Parkinson's disease

An important aspect of managing a limited cognitive resource like attention is to use the reward value of stimuli to prioritize the allocation of attention to higher-value over lower-value stimuli. Recent evidence suggests this depends on dopaminergic signaling of reward. In Parkinson's disease, both reward sensitivity and attention are impaired, but whether these deficits are directly related to one another is unknown. We tested whether Parkinson's patients use reward information when automatically allocating their attention and whether this is modulated by dopamine replacement. We compared patients, tested both ON and OFF dopamine replacement medication, to older controls using a standard attention capture task. First, participants learned the different reward values of stimuli. Then, these reward-associated stimuli were used as distractors in a visual search task. We found that patients were generally distracted by the presence of the distractors but that the degree of distraction caused by the high-value and low-value distractors was similar. Furthermore, we found no evidence to support the possibility that dopamine replacement modulates the effect of reward on automatic attention allocation. Our results suggest a possible inability in Parkinson's patients to use the reward value of stimuli when automatically allocating their attention, and raise the possibility that reward-driven allocation of resources may affect the adaptive modulation of other cognitive processes.

Recovering Reliable Idiographic Biological Parameters from Noisy Behavioral Data: the Case of Basal Ganglia Indices in the Probabilistic Selection Task

Behavioral data, despite being a common index of cognitive activity, is under scrutiny for having poor reliability as a result of noise or lacking replications of reliable effects. Here, we argue that cognitive modeling can be used to enhance the test-retest reliability of the behavioral measures by recovering individual-level parameters from behavioral data. We tested this empirically with the Probabilistic Stimulus Selection (PSS) task, which is used to measure a participant’s sensitivity to positive or negative reinforcement. An analysis of 400,000 simulations from an Adaptive Control of Thought-Rational (ACT-R) model of this task showed that the poor reliability of the task is due to the instability of the end-estimates: because of the way the task works, the same participants might sometimes end up having apparently opposite scores. To recover the underlying interpretable parameters and enhance reliability, we used a Bayesian Maximum A Posteriori (MAP) procedure. We were able to obtain reliable parameters across sessions (intraclass correlation coefficient ≈ 0.5). A follow-up study on a modified version of the task also found the same pattern of results, with very poor test-retest reliability in behavior but moderate reliability in recovered parameters (intraclass correlation coefficient ≈ 0.4). Collectively, these results imply that this approach can further be used to provide superior measures in terms of reliability, and bring greater insights into individual differences.

Feature selection and machine learning methods for optimal identification and prediction of subtypes in Parkinson's disease

OBJECTIVES

The present work focuses on assessment of Parkinson's disease (PD), including both PD subtype identification (unsupervised task) and prediction (supervised task). We specifically investigate optimal feature selection and machine learning algorithms for these tasks.

METHODS

We selected 885 PD subjects as derived from longitudinal datasets (years 0-4; Parkinson's Progressive Marker Initiative), and investigated 981 features including motor, non-motor, and imaging features (SPECT-based radiomics features extracted using our standardized SERA software). Two different hybrid machine learning systems (HMLS) were constructed and applied to the data in order to select optimal combinations in both tasks: (i) identification of subtypes in PD (unsupervised-clustering), and (ii) prediction of these subtypes in year 4 (supervised-classification). From the original data based on years 0 (baseline) and 1, we created new datasets as inputs to the prediction task: (i,ii) CSD0 and CSD01: cross-sectional datasets from year 0 only and both years 0 & 1, respectively; (iii) TD01: timeless dataset from both years 0 & 1. In addition, PD subtype in year 4 was considered as outcome. Finally, high score features were derived via ensemble voting based on their prioritizations from feature selector algorithms (FSAs).

RESULTS

In clustering task, the most optimal combinations (out of 981) were selected by individual FSAs to enable high correlation compared to using all features (arriving at 547). In prediction task, we were able to select optimal combinations, resulting in an accuracy >90% only for timeless dataset (TD01); there, we were able to select the most optimal combination using 77 features, directly selected by FSAs. In both tasks, however, using combination of only high score features from ensemble voting did not enable acceptable performances, showing optimal feature selection via individual FSAs to be more effective.

CONCLUSION

Combining non-imaging information with SPECT-based radiomics features, and optimal utilization of HMLSs, can enable robust identification of subtypes as well as appropriate prediction of these subtypes in PD patients. Moreover, use of timeless dataset, beyond cross-sectional datasets, enabled predictive accuracies over 90%. Overall, we showed that radiomics features extracted from SPECT images are important in clustering as well as prediction of PD subtypes.

Robust identification of Parkinson's disease subtypes using radiomics and hybrid machine learning

OBJECTIVES

It is important to subdivide Parkinson's disease (PD) into subtypes, enabling potentially earlier disease recognition and tailored treatment strategies. We aimed to identify reproducible PD subtypes robust to variations in the number of patients and features.

METHODS

We applied multiple feature-reduction and cluster-analysis methods to cross-sectional and timeless data, extracted from longitudinal datasets (years 0, 1, 2 & 4; Parkinson's Progressive Marker Initiative; 885 PD/163 healthy-control visits; 35 datasets with combinations of non-imaging, conventional-imaging, and radiomics features from DAT-SPECT images). Hybrid machine-learning systems were constructed invoking 16 feature-reduction algorithms, 8 clustering algorithms, and 16 classifiers (C-index clustering evaluation used on each trajectory). We subsequently performed: i) identification of optimal subtypes, ii) multiple independent tests to assess reproducibility, iii) further confirmation by a statistical approach, iv) test of reproducibility to the size of the samples.

RESULTS

When using no radiomics features, the clusters were not robust to variations in features, whereas, utilizing radiomics information enabled consistent generation of clusters through ensemble analysis of trajectories. We arrived at 3 distinct subtypes, confirmed using the training and testing process of k-means, as well as Hotelling's T2 test. The 3 identified PD subtypes were 1) mild; 2) intermediate; and 3) severe, especially in terms of dopaminergic deficit (imaging), with some escalating motor and non-motor manifestations.

CONCLUSION

Appropriate hybrid systems and independent statistical tests enable robust identification of 3 distinct PD subtypes. This was assisted by utilizing radiomics features from SPECT images (segmented using MRI). The PD subtypes provided were robust to the number of the subjects, and features.

Functional lateralization in the prefrontal cortex of dopaminergic modulation of memory consolidation

There is increasing evidence of functional lateralization within the rat brain. Here, we have examined the lateralization of dopamine (DA) function in the medial prefrontal cortex (PFC) in relation to memory consolidation in the novel object recognition test (NOR). Male Wistar rats received single bilateral or unilateral injections into prelimbic-PFC of agonists (SKF81297; 0.2 µg, quinpirole; 1 µg, SB277,011; 0.5 µg) and antagonists (SCH23390; 3 µg, L-741,626; 1 µg, 7-OH-DPAT; 3 µg) at DA D1, D2, or D3 receptors, immediately following the exposure trial in the NOR, and were tested either 1 or 24 h later for discrimination between a novel and a familiar object. As previously reported, bilateral injection of a D1 antagonist (SCH23390, 3 µg/side), a D2 antagonist (L-741,626, 1 µg/side) or a D3 agonist (7-OH-DPAT, 3 µg/side) impaired NOR at 1 h, while a D1 agonist (SKF81297, 0.2 µg/side), a D2 agonist (quinpirole, 1 µg/side) or a D3 antagonist (SB277,011, 0.5 µg/side) improved NOR at 24 h. The same effects were seen with left-sided unilateral injections. No effects were seen with right-sided unilateral injections. Endogenous DA release in the prelimbic-PFC promotes memory consolidation in the NOR, but only on the left side of the brain.

Unilateral accumbal dopamine depletion affects decision-making in a side-specific manner

Mechanisms underlying affective and cognitive deficits in Parkinson's disease (PD) remain less studied than motor symptoms. Nucleus accumbens (NAc) is affected in PD and due to its well-known involvement in motivation is an interesting target in this context. Furthermore, PD is frequently asymmetrical, with side-specific deficits aligning with evidences of accumbal laterality. We therefore used a 6-hydroxydopamine (6-OHDA) model to study the role of left and right NAc dopamine depletion in a battery of behavioral tasks. 2 months old male rats were used in all experiments. Habitual-based and goal-directed decision-making, impulsivity, anxiety- and depressive-like behavior and motor performance were tested 3 weeks after left (6-OHDA L) or right (6-OHDA R) NAc lesion was induced. Upon contingency degradation, 6-OHDA R decrease their lever press rate less than Sham and 6-OHDA L, indicating an impairment in the shift from habit-based to goal-directed strategies. On the other hand, 6-OHDA L lesions lead to increased rates of premature responding when delays where increased in the variable delay-to-signal test. Importantly, in both paradigms task acquisition was similar between groups. In the same line we found no differences in the amount of sugared pellets eaten when freely available as well as in both general and fine motor behaviors. In conclusion, left and right NAc play distinct roles in the contingency degradation and impulsivity. More studies are needed to understand the mechanisms behind this functional lateralization and its implications for PD patients.

Dopaminergic medication reduces striatal sensitivity to negative outcomes in Parkinson's disease

Reduced levels of dopamine in Parkinson's disease contribute to changes in learning, resulting from the loss of midbrain neurons that transmit a dopaminergic teaching signal to the striatum. Dopamine medication used by patients with Parkinson's disease has previously been linked to behavioural changes during learning as well as to adjustments in value-based decision-making after learning. To date, however, little is known about the specific relationship between dopaminergic medication-driven differences during learning and subsequent changes in approach/avoidance tendencies in individual patients. Twenty-four Parkinson's disease patients ON and OFF dopaminergic medication and 24 healthy controls subjects underwent functional MRI while performing a probabilistic reinforcement learning experiment. During learning, dopaminergic medication reduced an overemphasis on negative outcomes. Medication reduced negative (but not positive) outcome learning rates, while concurrent striatal blood oxygen level-dependent responses showed reduced prediction error sensitivity. Medication-induced shifts in negative learning rates were predictive of changes in approach/avoidance choice patterns after learning, and these changes were accompanied by systematic striatal blood oxygen level-dependent response alterations. These findings elucidate the role of dopamine-driven learning differences in Parkinson's disease, and show how these changes during learning impact subsequent value-based decision-making.

Left-shifting prism adaptation boosts reward-based learning

Visuospatial cognition has an inherent lateralized bias. Individual differences in the direction and magnitude of this bias are associated with asymmetrical D2/3 dopamine binding and dopamine system genotypes. Dopamine level affects feedback-based learning and dopamine signaling asymmetry is related to differential learning from reward and punishment. High D2 binding in the left hemisphere is associated with preference for reward. Prism adaptation (PA) is a simple sensorimotor technique, which modulates visuospatial bias according to the direction of the deviation. Left-deviating prism adaptation (LPA) induces rightward bias in healthy subjects. It is therefore possible that the right side of space increases in saliency along with left hemisphere dopaminergic activity. Right-deviating prism adaptation (RPA) has been used mainly as a control condition because it does not modulate behavior in healthy individuals. Since LPA induces a rightward visuospatial bias as a result of left hemisphere modulation, and higher dopaminergic activity in the left hemisphere is associated with preference for rewarding events we hypothesized that LPA would increase the preference for learning with reward. Healthy volunteers performed a computer-based probabilistic classification task before and after LPA or RPA. Consistent with our predictions, PA altered the preference for rewarded versus punished learning, with the LPA group exhibiting increased learning from reward. These results suggest that PA modulates dopaminergic activity in a lateralized fashion.

Effects of dopamine on reinforcement learning and consolidation in Parkinson's disease

Emerging evidence suggests that dopamine may modulate learning and memory with important implications for understanding the neurobiology of memory and future therapeutic targeting. An influential hypothesis posits that dopamine biases reinforcement learning. More recent data also suggest an influence during both consolidation and retrieval. Eighteen Parkinson's disease patients learned through feedback ON or OFF medication, with memory tested 24 hr later ON or OFF medication (4 conditions, within-subjects design with matched healthy control group). Patients OFF medication during learning decreased in memory accuracy over the following 24 hr. In contrast to previous studies, however, dopaminergic medication during learning and testing did not affect expression of positive or negative reinforcement. Two further experiments were run without the 24 hr delay, but they too failed to reproduce effects of dopaminergic medication on reinforcement learning. While supportive of a dopaminergic role in consolidation, this study failed to replicate previous findings on reinforcement learning.

Distinct neural processes support post-success and post-error slowing in the stop signal task

Executive control requires behavioral adaptation to environmental contingencies. In the stop signal task (SST), participants exhibit slower go trial reaction time (RT) following a stop trial, whether or not they successfully interrupt the motor response. In previous fMRI studies, we demonstrated activation of the right-hemispheric ventrolateral prefrontal cortex, in the area of inferior frontal gyrus, pars opercularis (IFGpo) and anterior insula (AI), during post-error slowing (PES). However, in similar analyses we were not able to identify regional activities during post-success slowing (PSS). Here, we revisited this issue in a larger sample of participants (n=100) each performing the SST for 40 min during fMRI. We replicated IFGpo/AI activation to PES (p≤0.05, FWE corrected). Further, PSS engages decreased activation in a number of cortical regions including the left inferior frontal cortex (IFC; p≤0.05, FWE corrected). We employed Granger causality mapping to identify areas that provide inputs each to the right IFGpo/AI and left IFC, and computed single-trial amplitude (STA) of stop trials of these input regions as well as the STA of post-stop trials of the right IFGpo/AI and left IFC. The STAs of the right inferior precentral sulcus and supplementary motor area (SMA) and right IFGpo/AI were positively correlated and the STAs of the left SMA and left IFC were positively correlated (slope>0, p's≤0.01, one-sample t test), linking regional responses during stop success and error trials to those during PSS and PES. These findings suggest distinct neural mechanisms to support PSS and PES.

Stimulus discriminability may bias value-based probabilistic learning

Reinforcement learning tasks are often used to assess participants' tendency to learn more from the positive or more from the negative consequences of one's action. However, this assessment often requires comparison in learning performance across different task conditions, which may differ in the relative salience or discriminability of the stimuli associated with more and less rewarding outcomes, respectively. To address this issue, in a first set of studies, participants were subjected to two versions of a common probabilistic learning task. The two versions differed with respect to the stimulus (Hiragana) characters associated with reward probability. The assignment of character to reward probability was fixed within version but reversed between versions. We found that performance was highly influenced by task version, which could be explained by the relative perceptual discriminability of characters assigned to high or low reward probabilities, as assessed by a separate discrimination experiment. Participants were more reliable in selecting rewarding characters that were more discriminable, leading to differences in learning curves and their sensitivity to reward probability. This difference in experienced reinforcement history was accompanied by performance biases in a test phase assessing ability to learn from positive vs. negative outcomes. In a subsequent large-scale web-based experiment, this impact of task version on learning and test measures was replicated and extended. Collectively, these findings imply a key role for perceptual factors in guiding reward learning and underscore the need to control stimulus discriminability when making inferences about individual differences in reinforcement learning.

Linking Individual Learning Styles to Approach-Avoidance Motivational Traits and Computational Aspects of Reinforcement Learning

Learning how to gain rewards (approach learning) and avoid punishments (avoidance learning) is fundamental for everyday life. While individual differences in approach and avoidance learning styles have been related to genetics and aging, the contribution of personality factors, such as traits, remains undetermined. Moreover, little is known about the computational mechanisms mediating differences in learning styles. Here, we used a probabilistic selection task with positive and negative feedbacks, in combination with computational modelling, to show that individuals displaying better approach (vs. avoidance) learning scored higher on measures of approach (vs. avoidance) trait motivation, but, paradoxically, also displayed reduced learning speed following positive (vs. negative) outcomes. These data suggest that learning different types of information depend on associated reward values and internal motivational drives, possibly determined by personality traits.

The left hemisphere learns what is right: Hemispatial reward learning depends on reinforcement learning processes in the contralateral hemisphere

Orienting biases refer to consistent, trait-like direction of attention or locomotion toward one side of space. Recent studies suggest that such hemispatial biases may determine how well people memorize information presented in the left or right hemifield. Moreover, lesion studies indicate that learning rewarded stimuli in one hemispace depends on the integrity of the contralateral striatum. However, the exact neural and computational mechanisms underlying the influence of individual orienting biases on reward learning remain unclear. Because reward-based behavioural adaptation depends on the dopaminergic system and prediction error (PE) encoding in the ventral striatum, we hypothesized that hemispheric asymmetries in dopamine (DA) function may determine individual spatial biases in reward learning. To test this prediction, we acquired fMRI in 33 healthy human participants while they performed a lateralized reward task. Learning differences between hemispaces were assessed by presenting stimuli, assigned to different reward probabilities, to the left or right of central fixation, i.e. presented in the left or right visual hemifield. Hemispheric differences in DA function were estimated through differential fMRI responses to positive vs. negative feedback in the left vs. right ventral striatum, and a computational approach was used to identify the neural correlates of PEs. Our results show that spatial biases favoring reward learning in the right (vs. left) hemifield were associated with increased reward responses in the left hemisphere and relatively better neural encoding of PEs for stimuli presented in the right (vs. left) hemifield. These findings demonstrate that trait-like spatial biases implicate hemisphere-specific learning mechanisms, with individual differences between hemispheres contributing to reinforcing spatial biases.

Experiential and Doctrinal Religious Knowledge Categorization in Parkinson's Disease: Behavioral and Brain Correlates

Recent studies suggest changes in religious cognition in a subgroup of patients with Parkinson's disease (PD e.g., Butler et al., 2011). It is unclear whether this deficit extends to both doctrinal and experiential categorization forms of religious cognition. Kapogiannis et al. (2009b) dissociated experiential and doctrinal religious knowledge to different neural networks using fMRI. We examined Kapogiannis' dissociation against the background of PD side of onset (LOPD, ROPD), assessing performance both On- and Off-medication. In the behavioral portion of the study, we used a statement classification task in combination with scholar derived test sets for experiential and doctrinal religious knowledge categorization in conjunction with neuropsychological measures. In the neuroimaging portion of the study, we expanded on Kapogiannis' study by examining the same networks in PD. The behavioral data revealed that all groups rated (categorized) the scholar derived tests of experiential and doctrinal significantly differently than the scholars. All groups, including the scholars, classified more phrases as doctrinal than experiential. Religious cognition differed in the PD groups: those with PD Off-medication and LOPD Off-medication comprehended scholar defined experiential phrases with more difficulty, making them more likely to be classified as mixed or doctrinal. This was in contrast to the subjective frequency of classification of phrases as experiential paired with a cognitive decline in PD Off-medication; whereas PD On-medication showed a positive correlation with cognitive state and subjective doctrinal classification. For ROPD, cognitive state was associated with subjective experiential and doctrinal frequency of classification. With more intact intellect, there was a greater likelihood of classifying phrases subjectively as mixed, and the converse for experiential. Furthermore, religiosity negatively predicted subjective doctrinal frequency in LOPD, with the converse in ROPD. In fcMRI in PD, we found resting state functional intrinsic connectivity of reward networks associated with classification of statements using seeds in bilateral nucleus accumbens in PD. For experiential regressors, there was a negative correlation in bilateral frontal lobes paired with a positive correlation in left occipital visual areas (BAs 17, 18). For doctrinal regressors, there was a positive correlation in right BA 20.

Hemispheric Asymmetries in Striatal Reward Responses Relate to Approach-Avoidance Learning and Encoding of Positive-Negative Prediction Errors in Dopaminergic Midbrain Regions

Some individuals are better at learning about rewarding situations, whereas others are inclined to avoid punishments (i.e., enhanced approach or avoidance learning, respectively). In reinforcement learning, action values are increased when outcomes are better than predicted (positive prediction errors [PEs]) and decreased for worse than predicted outcomes (negative PEs). Because actions with high and low values are approached and avoided, respectively, individual differences in the neural encoding of PEs may influence the balance between approach-avoidance learning. Recent correlational approaches also indicate that biases in approach-avoidance learning involve hemispheric asymmetries in dopamine function. However, the computational and neural mechanisms underpinning such learning biases remain unknown. Here we assessed hemispheric reward asymmetry in striatal activity in 34 human participants who performed a task involving rewards and punishments. We show that the relative difference in reward response between hemispheres relates to individual biases in approach-avoidance learning. Moreover, using a computational modeling approach, we demonstrate that better encoding of positive (vs negative) PEs in dopaminergic midbrain regions is associated with better approach (vs avoidance) learning, specifically in participants with larger reward responses in the left (vs right) ventral striatum. Thus, individual dispositions or traits may be determined by neural processes acting to constrain learning about specific aspects of the world.

Is it Worth the Effort? Novel Insights into Obesity-Associated Alterations in Cost-Benefit Decision-Making

Cost-benefit decision-making entails the process of evaluating potential actions according to the trade-off between the expected reward (benefit) and the anticipated effort (costs). Recent research revealed that dopaminergic transmission within the fronto-striatal circuitry strongly modulates cost-benefit decision-making. Alterations within the dopaminergic fronto-striatal system have been associated with obesity, but little is known about cost-benefit decision-making differences in obese compared with lean individuals. With a newly developed experimental task we investigate obesity-associated alterations in cost-benefit decision-making, utilizing physical effort by handgrip-force exertion and both food and non-food rewards. We relate our behavioral findings to alterations in local gray matter volume assessed by structural MRI. Obese compared with lean subjects were less willing to engage in physical effort in particular for high-caloric sweet snack food. Further, self-reported body dissatisfaction negatively correlated with the willingness to invest effort for sweet snacks in obese men. On a structural level, obesity was associated with reductions in gray matter volume in bilateral prefrontal cortex. Nucleus accumbens volume positively correlated with task induced implicit food craving. Our results challenge the common notion that obese individuals are willing to work harder to obtain high-caloric food and emphasize the need for further exploration of the underlying neural mechanisms regarding cost-benefit decision-making differences in obesity.

A comparison of neural responses to appetitive and aversive stimuli in humans and other mammals

Distinguishing potentially harmful or beneficial stimuli is necessary for the self-preservation and well-being of all organisms. This assessment requires the ongoing valuation of environmental stimuli. Despite much work on the processing of aversive- and appetitive-related brain signals, it is not clear to what degree these two processes interact across the brain. To help clarify this issue, this report used a cross-species comparative approach in humans (i.e. meta-analysis of imaging data) and other mammals (i.e. targeted review of functional neuroanatomy in rodents and non-human primates). Human meta-analysis results suggest network components that appear selective for appetitive (e.g. ventromedial prefrontal cortex, ventral tegmental area) or aversive (e.g. cingulate/supplementary motor cortex, periaqueductal grey) processing, or that reflect overlapping (e.g. anterior insula, amygdala) or asymmetrical, i.e. apparently lateralized, activity (e.g. orbitofrontal cortex, ventral striatum). However, a closer look at the known value-related mechanisms from the animal literature suggests that all of these macroanatomical regions are involved in the processing of both appetitive and aversive stimuli. Differential spatiotemporal network dynamics may help explain similarities and differences in appetitive- and aversion-related activity.