Effects of Dopamine Medication on Sequence Learning with Stochastic Feedback in Parkinson's Disease

A growing body of evidence suggests that the midbrain dopamine system plays a key role in reinforcement learning and disruption of the midbrain dopamine system in Parkinson's disease (PD) may lead to deficits on tasks that require learning from feedback. We examined how changes in dopamine levels (“ON” and “OFF” their dopamine medication) affect sequence learning from stochastic positive and negative feedback using Bayesian reinforcement learning models. We found deficits in sequence learning in patients with PD when they were “ON” and “OFF” medication relative to healthy controls, but smaller differences between patients “OFF” and “ON”. The deficits were mainly due to decreased learning from positive feedback, although across all participant groups learning was more strongly associated with positive than negative feedback in our task. The learning in our task is likely mediated by the relatively depleted dorsal striatum and not the relatively intact ventral striatum. Therefore, the changes we see in our task may be due to a strong loss of phasic dopamine signals in the dorsal striatum in PD

A Selective Emotional Decision-Making Bias Elicited by Facial Expressions

Emotional and social information can sway otherwise rational decisions. For example, when participants decide between two faces that are probabilistically rewarded, they make biased choices that favor smiling relative to angry faces. This bias may arise because facial expressions evoke positive and negative emotional responses, which in turn may motivate social approach and avoidance. We tested a wide range of pictures that evoke emotions or convey social information, including animals, words, foods, a variety of scenes, and faces differing in trustworthiness or attractiveness, but we found only facial expressions biased decisions. Our results extend brain imaging and pharmacological findings, which suggest that a brain mechanism supporting social interaction may be involved. Facial expressions appear to exert special influence over this social interaction mechanism, one capable of biasing otherwise rational choices. These results illustrate that only specific types of emotional experiences can best sway our choices

Sequence Learning Under Uncertainty in Children: Self-Reflection vs. Self-Assertion

We know that stochastic feedback impairs children’s associative stimulus–response (S–R) learning (Crone et al., 2004a; Eppinger et al., 2009), but the impact of stochastic feedback on sequence learning that involves deductive reasoning has not been not tested so far. In the current study, 8- to 11-year-old children (N = 171) learned a sequence of four left and right button presses, LLRR, RRLL, LRLR, RLRL, LRRL, and RLLR, which needed to be deduced from feedback because no directional cues were given. One group of children experienced consistent feedback only (deterministic feedback, 100% correct). In this condition, green feedback on the screen indicated that the children had been right when they were right, and red feedback indicated that the children had been wrong when they were wrong. Another group of children experienced inconsistent feedback (stochastic feedback, 85% correct, 15% false), where in some trials, green feedback on the screen could signal that children were right when in fact they were wrong, and red feedback could indicate that they were wrong when in fact they had been right. Independently of age, children’s sequence learning in the stochastic condition was initially much lower than in the deterministic condition, but increased gradually and improved with practice. Responses toward positive vs. negative feedback varied with age. Children were increasingly able to understand that they could have been wrong when feedback indicated they were right (self-reflection), but they remained unable to understand that they could have been right when feedback indicated they were wrong (self-assertion)

Salivary cortisol levels in Parkinson's disease and its correlation to risk behaviour

Objective To investigate salivary cortisol samples in patients with Parkinson's disease (PD) with and without impulsive compulsive behaviours (ICB) during a risk task.Methods Salivary cortisol levels were measured in 13 PD patients without ICB (PD-ICB) and in 15 PD patients with ICB (PD+ICB) before, after medication and throughout the day, and were compared with results with 14 healthy controls. All participants also performed a gambling task to assess risk taking behaviour.Results Significantly higher diurnal cortisol levels were found in the PD-ICB group compared with healthy controls but no differences were seen between the PD+ICB and the control group. Increased cortisol levels were significantly correlated with increased risk taking in PD+ICB patients but no interaction was found in the PD-ICB group.Conclusions The findings are in keeping with previous studies which have linked low cortisol levels with antisocial behaviour. The higher cortisol levels during the risk task in the PD+ICB group are consistent with reports in pathological gamblers during gambling and addicts during drug abuse. The results support the hypothesis that cortisol plays an important role in risk taking in ICBs

Impulsivity in Parkinson’s disease is associated with alterations in affective and sensorimotor striatal networks

A subset of patients with Parkinson’s disease (PD) experiences problems with impulse control, characterized by a loss of voluntary control over impulses, drives, or temptations regarding excessive hedonic behavior. The present study aimed to better understand the neural basis of such impulse control disorders (ICDs) in PD. We collected resting-state functional connectivity and structural MRI data from 21 PD patients with ICDs and 30 patients without such disorders. To assess impulsivity, all patients completed the Barratt Impulsiveness Scale and performed an information-gathering task. MRI results demonstrated substantial differences in neural characteristics between PD patients with and without ICDs. Results showed that impulsivity was linked to alterations in affective basal ganglia circuitries. Specifically, reduced frontal–striatal connectivity and GPe volume were associated with more impulsivity. We suggest that these changes affect decision making and result in a preference for risky or inappropriate actions. Results further showed that impulsivity was linked to alterations in sensorimotor striatal networks. Enhanced connectivity within this network and larger putamen volume were associated with more impulsivity. We propose that these changes affect sensorimotor processing such that patients have a greater propensity to act. Our findings suggest that the two mechanisms jointly contribute to impulsive behaviors in PD

The statistical neuroanatomy of frontal networks in the macaque

We were interested in gaining insight into the functional properties of frontal networks based upon their anatomical inputs. We took a neuroinformatics approach, carrying out maximum likelihood hierarchical cluster analysis on 25 frontal cortical areas based upon their anatomical connections, with 68 input areas representing exterosensory, chemosensory, motor, limbic, and other frontal inputs. The analysis revealed a set of statistically robust clusters. We used these clusters to divide the frontal areas into 5 groups, including ventral-lateral, ventral-medial, dorsal-medial, dorsal-lateral, and caudal-orbital groups. Each of these groups was defined by a unique set of inputs. This organization provides insight into the differential roles of each group of areas and suggests a gradient by which orbital and ventral-medial areas may be responsible for decision-making processes based on emotion and primary reinforcers, and lateral frontal areas are more involved in integrating affective and rational information into a common framework

Computational modeling of threat learning reveals links with anxiety and neuroanatomy in humans

Influential theories implicate variations in the mechanisms supporting threat learning in the severity of anxiety symptoms. We use computational models of associative learning in conjunction with structural imaging to explicate links among the mechanisms underlying threat learning, their neuroanatomical substrates, and anxiety severity in humans. We recorded skin-conductance data during a threat-learning task from individuals with and without anxiety disorders (N=251; 8-50 years; 116 females). Reinforcement-learning model variants quantified processes hypothesized to relate to anxiety: threat conditioning, threat generalization, safety learning, and threat extinction. We identified the best-fitting models for these processes and tested associations among latent learning parameters, whole-brain anatomy, and anxiety severity. Results indicate that greater anxiety severity related specifically to slower safety learning and slower extinction of response to safe stimuli. Nucleus accumbens gray-matter volume moderated learning-anxiety associations. Using a modeling approach, we identify computational mechanisms linking threat learning and anxiety severity and their neuroanatomical substrates

Performance on a probabilistic inference task in healthy subjects receiving ketamine compared with patients with schizophrenia

Evidence suggests that some aspects of schizophrenia can be induced in healthy volunteers through acute administration of the non-competitive NMDA-receptor antagonist, ketamine. In probabilistic inference tasks, patients with schizophrenia have been shown to 'jump to conclusions' (JTC) when asked to make a decision. We aimed to test whether healthy participants receiving ketamine would adopt a JTC response pattern resembling that of patients. The paradigmatic task used to investigate JTC has been the 'urn' task, where participants are shown a sequence of beads drawn from one of two 'urns', each containing coloured beads in different proportions. Participants make a decision when they think they know the urn from which beads are being drawn. We compared performance on the urn task between controls receiving acute ketamine or placebo with that of patients with schizophrenia and another group of controls matched to the patient group. Patients were shown to exhibit a JTC response pattern relative to their matched controls, whereas JTC was not evident in controls receiving ketamine relative to placebo. Ketamine does not appear to promote JTC in healthy controls, suggesting that ketamine does not affect probabilistic inferences

Effects of Dopamine on Sensitivity to Social Bias in Parkinson's Disease

Patients with Parkinson's disease (PD) sometimes develop impulsive compulsive behaviours (ICBs) due to their dopaminergic medication. We compared 26 impulsive and 27 non-impulsive patients with PD, both on and off medication, on a task that examined emotion bias in decision making. No group differences were detected, but patients on medication were less biased by emotions than patients off medication and the strongest effects were seen in patients with ICBs. PD patients with ICBs on medication also showed more learning from negative feedback and less from positive feedback, whereas off medication they showed the opposite effect

Individual associations of adolescent alcohol use disorder versus cannabis use disorder symptoms in neural prediction error signaling and the response to novelty

Two of the most commonly used illegal substances by adolescents are alcohol and cannabis. Alcohol use disorder (AUD) and cannabis use disorder (CUD) are associated with poorer decision-making in adolescents. In adolescents, level of AUD symptomatology has been negatively associated with striatal reward responsivity. However, little work has explored the relationship with striatal reward prediction error (RPE) representation and the extent to which any augmentation of RPE by novel stimuli is impacted. One-hundred fifty-one adolescents participated in the Novelty Task while undergoing functional magnetic resonance imaging (fMRI). In this task, participants learn to choose novel or non-novel stimuli to gain monetary reward. Level of AUD symptomatology was negatively associated with both optimal decision-making and BOLD response modulation by RPE within striatum and regions of prefrontal cortex. The neural alterations in RPE representation were particularly pronounced when participants were exploring novel stimuli. Level of CUD symptomatology moderated the relationship between novelty propensity and RPE representation within inferior parietal lobule and dorsomedial prefrontal cortex. These data expand on an emerging literature investigating individual associations of AUD symptomatology levels versus CUD symptomatology levels and RPE representation during reinforcement processing and provide insight on the role of neuro-computational processes underlying reinforcement learning/decision-making in adolescents