McCoy et al. show that dopaminergic medication in Parkinson’s disease leads to changes in striatal signalling and in behaviour during learning, but that changes are specific to the processing of negative reinforcements. These within-patient changes are predictive of changes in future value-based choice behaviour and striatal responses.

Research has shown that dynamic functional connectivity (dFC) in Parkinson’s disease (PD) is associated with better attention performance and with motor symptom severity. In the current study, we aimed to investigate dFC of both the default mode network (DMN) and the frontoparietal network (FPN) as neural correlates of cognitive functioning in patients with PD. Additionally, we investigated pain and motor problems as symptoms of PD in relation to dFC. Twenty-four PD patients and 27 healthy controls participated in this study. Memory and executive functioning were assessed with neuropsychological tests. Pain was assessed with the Numeric Rating Scale (NRS); motor symptom severity was assessed with the Unified Parkinson’s Disease Rating Scale (UPDRS). All subjects underwent resting-state functional magnetic resonance imaging (fMRI), from which dFC was defined by calculating the variability of functional connectivity over a number of sliding windows within each scan. dFC of both the DMN and FPN with the rest of the brain was calculated. Patients performed worse on tests of visuospatial memory, verbal memory and working memory. No difference existed between groups regarding dFC of the DMN nor the FPN with the rest of the brain. A positive correlation existed between dFC of the DMN and visuospatial memory. Our results suggest that dynamics during the resting state are a neural correlate of visuospatial memory in PD patients. Furthermore, we suggest that brain dynamics of the DMN, as measured with dFC, could be a phenomenon specifically linked to cognitive functioning in PD, but not to other symptoms.

Reduced levels of dopamine in Parkinson's disease (PD) contribute to changes in learning, resulting from the loss of midbrain dopamine neurons that transmit a teaching signal to the striatum. Dopamine medication used by PD patients has previously been linked to either behavioral changes during learning itself or adjustments in approach and avoidance behavior after learning. To date, however, very little is known about the specific relationship between dopaminergic medication-driven differences during learning and subsequent changes in approach/avoidance tendencies in individual patients. We assessed 24 PD patients on and off dopaminergic medication and 24 healthy controls (HC) performing a probabilistic reinforcement learning task, while undergoing functional magnetic resonance imaging.During learning, medication in PD reduced an overemphasis on negative outcomes. When patients were on medication, learning rates were lower for negative (but not positive) outcomes and concurrent striatal BOLD 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 striatal BOLD response alterations. These findings highlight dopaminedriven learning differences in PD and provide new insight into how changes in learning impact the transfer of learned value to approach/avoidance responses in novel contexts.

Pain is an important non-motor symptom in Parkinson’s disease (PD), but its underlying pathophysiological mechanisms are still unclear. Research has shown that functional connectivity during the resting-state may be involved in persistent pain in PD. In the present cross-sectional study, 24 PD patients (both during on and off medication phase) and 27 controls participated. We assessed pain with the colored analogue scale and the McGill pain questionnaire. We examined a possible pathophysiological mechanism with resting-state fMRI using functional network topology, i.e., the architecture of functional connections. We took betweenness centrality (BC) to assess hubness, and global efficiency (GE) to assess integration of the network. We aimed to (1) assess the differences between PD patients and controls with respect to pain and resting-state network topology, and (2) investigate how resting-state network topology (BC and GE) is associated with clinical pain in both PD patients and controls. Results show that PD patients experienced more pain than controls. GE of the whole brain was higher in PD patients (on as well as off medication) compared to healthy controls. GE of the specialized pain network was also higher in PD patients compared to controls, but only when patients were on medication. BC of the pain network was lower in PD patients off medication compared to controls. We found a positive association between pain and GE of the pain network in PD patients off medication. For healthy controls, a negative association was found between pain and GE of the pain network, and also between pain and BC of the pain network. Our results suggest that functional network topology differs between PD patients and healthy controls, and that this topology can be used to investigate the underlying neural mechanisms of pain symptoms in PD.

The present study examines the extent to which distractors that signal the availability of monetary reward on a given trial affect eye movements. We used a novel eye movement task in which observers had to follow a target around the screen while ignoring distractors presented at varying locations. We examined the effects of reward magnitude and distractor location on a host of oculomotor properties, including saccade latency, amplitude, landing position, curvature, and erroneous saccades toward the distractor. We found consistent effects of reward magnitude on classic oculomotor phenomena such as the remote distractor effect, the global effect, and oculomotor capture by the distractor. We also show that a distractor in the visual hemifield opposite to the target had a larger effect on oculomotor control than an equidistant distractor in the same hemifield as the target. Bayesian hierarchical drift diffusion modeling revealed large differences in drift rate depending on the reward value, location, and visual hemifield of the distractor stimulus. Our findings suggest that high reward distractors not only capture the eyes but also affect a multitude of oculomotor properties associated with oculomotor inhibition and control.