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Parkinson's disease causes a characteristic combination of motor symptoms due to progressive neurodegeneration of dopaminergic neurons in the substantia nigra pars compacta. The core impairment of dopaminergic neurotransmission has motivated the use of functional magnetic resonance imaging (fMRI) in patients with Parkinson's disease to elucidate the role of dopamine in motor control and cognition in humans. Here we review the main insights from functional brain imaging in Parkinson's disease. Task-related fMRI revealed many disease-related alterations in brain activation patterns. However, the interpretation of these findings is complicated by the fact that task-dependent activity is influenced by complex interactions between the amount of dopaminergic neurodegeneration in the task-relevant nuclei, the state of medication, genetic factors and performance. Despite these ambiguities, fMRI studies in Parkinson's disease demonstrated a central role of dopamine in the generation of movement vigour (bradykinesia) and the control of excessive movements (dyskinesia), involving changes of both activity and connectivity of the putamen, premotor and motor regions, and right inferior frontal gyrus (rIFG). The fMRI studies addressing cognitive flexibility provided convergent evidence for a non-linear, U-shaped, relationship between dopamine levels and performance. The amount of neurodegeneration in the task-relevant dopaminergic nuclei and pharmacological dopamine replacement can therefore move performance either away or towards the task-specific optimum. Dopamine levels also strongly affect processing of reward and punishment for optimal learning. However, further studies are needed for a detailed understanding of the mechanisms underlying these effects. •Parkinson's disease (PD) results in neurodegeneration of dopaminergic neurons.•Functional MRI in patients examines the role of dopamine in the human brain.•Evidence point to a central role of dopamine in securing movement vigour.•Dopamine levels and cognitive performance show an inverse U-shape relationship.•Dopamine is central for learning from rewards and punishments.

Ergodicity describes an equivalence between the expectation value and the time average of observables. Applied to human behaviour, ergodic theories of decision-making reveal how individuals should tolerate risk in different environments. To optimize wealth over time, agents should adapt their utility function according to the dynamical setting they face. Linear utility is optimal for additive dynamics, whereas logarithmic utility is optimal for multiplicative dynamics. Whether humans approximate time optimal behavior across different dynamics is unknown. Here we compare the effects of additive versus multiplicative gamble dynamics on risky choice. We show that utility functions are modulated by gamble dynamics in ways not explained by prevailing decision theories. Instead, as predicted by time optimality, risk aversion increases under multiplicative dynamics, distributing close to the values that maximize the time average growth of in-game wealth. We suggest that our findings motivate a need for explicitly grounding theories of decision-making on ergodic considerations.

Decisions are based on value expectations derived from experience. We show that dorsal anterior cingulate cortex and three other brain regions hold multiple representations of choice value based on different timescales of experience organized in terms of systematic gradients across the cortex. Some parts of each area represent value estimates based on recent reward experience while others represent value estimates based on experience over the longer term. The value estimates within these areas interact with one another according to their temporal scaling. Some aspects of the representations change dynamically as the environment changes. The spectrum of value estimates may act as a flexible selection mechanism for combining experience-derived value information with other aspects of value to allow flexible and adaptive decisions in changing environments. Activity in dorsal anterior cingulate cortex (dACC) dynamically tracks the value of the choice after every outcome. Here the authors report that dACC represents topographic maps of value estimates for different learning rates and interacts with similar maps in other areas at the time of the decision.

Parkinson’s disease causes a progressive loss of dopaminergic neurons and iron accumulation in the substantia nigra pars compacta. Using ultra-high field magnetic resonance imaging (MRI) at 7 tesla in 43 Parkinson’s patients and 24 healthy controls, we analyzed the voxel-wise pattern of structural disintegration of dopamine neurons with neuromelanin-sensitive MRI, along with assessing iron accumulation using R2* and quantitative susceptibility mapping (QSM). We also explored correlations between these measures and the severity of residual motor symptoms in the on-medication state and other clinical variables. Differences were most notable in the nigrosomes within the pars compacta, with patients showing reduced neuromelanin signals and increased QSM values. Severity and asymmetry of motor symptoms correlated with higher R2* and QSM values in nigrosome N1. Thus, ultra-high field MRI provides high-resolution maps of various aspects of the underlying neurodegenerative process which reflect individual motor impairment in Parkinson’s disease.

Adaptive actions build on internal probabilistic models of possible outcomes that are tuned according to the errors of their predictions when experiencing an actual outcome. Prediction errors (PEs) inform choice behavior across a diversity of outcome domains and dimensions, yet neuroimaging studies have so far only investigated such signals in singular experimental contexts. It is thus unclear whether the neuroanatomical distribution of PE encoding reported previously pertains to computational features that are invariant with respect to outcome valence, sensory domain, or some combination of the two. We acquired functional MRI data while volunteers performed four probabilistic reversal learning tasks which differed in terms of outcome valence (reward-seeking versus punishment-avoidance) and domain (abstract symbols versus facial expressions) of outcomes. We found that ventral striatum and frontopolar cortex coded increasingly positive PEs, whereas dorsal anterior cingulate cortex (dACC) traced increasingly negative PEs, irrespectively of the outcome dimension. Individual reversal behavior was unaffected by context manipulations and was predicted by activity in dACC and right inferior frontal gyrus (IFG). The stronger the response to negative PEs in these areas, the lower was the tendency to reverse choice behavior in response to negative events, suggesting that these regions enforce a rule-based strategy across outcome dimensions. Outcome valence influenced PE-related activity in left amygdala, IFG, and dorsomedial prefrontal cortex, where activity selectively scaled with increasingly positive PEs in the reward-seeking but not punishment-avoidance context, irrespective of sensory domain. Left amygdala displayed an additional influence of sensory domain. In the context of avoiding punishment, amygdala activity increased with increasingly negative PEs, but only for facial stimuli, indicating an integration of outcome valence and sensory domain during probabilistic choices.

Learning from experience is theorized to be driven by reward prediction error (RPE) signals that reflect updates to our expectations of reward. Despite numerous studies on the neural correlates of RPEs, the question of how large-scale networks (LSNs) in the brain reconfigure in response to an RPE learning signal remains open. Here, we examine how functional networks change in response to RPEs depending on the context. In our study, participants performed a probabilistic reversal learning task while we acquired fMRI data in two experimental settings: reward-seeking and punishment-avoiding. Participants’ behavior was best explained by models with different learning rates for positive and negative RPEs. Furthermore, no evidence was found for context-dependent learning rates. Using behaviorally fitted RPE models, we performed a whole-brain network analysis. This analysis revealed classical reward structures, where striatal reward networks emerge as modules when the community structure is examined at a finer resolution, and a ventromedial prefrontal network emerges at a coarser resolution. Using the same behavioral model, we found that, compared with negative RPEs, positive RPEs increased within-network integration and decreased between-network integration. This indicates that there are distinctly different neural processes for positive and negative RPEs.

Parkinson’s disease (PD) causes progressive degeneration of noradrenergic neurons in the locus coeruleus (LC), contributing to non-motor symptoms. Using neuromelanin-sensitive ultra-high field (7T) MRI, we previously identified a reduction in neuromelanin signal in the caudal LC, indicating a rostro-caudal gradient of noradrenergic cell loss. Caudal LC degeneration was associated with greater severity of non-motor symptoms such as orthostatic hypotension and apathy. In the current study, we expanded the PD cohort to further validate the structure–symptom relationships within the LC and investigate how degeneration along the rostro-caudal LC axis affects arousal-related functional responsivity. To this end, 71 people with PD in the ON-medication state and 40 age- and sex-matched healthy controls underwent clinical assessments and 7T magnetization transfer-weighted (MTw) MRI to quantify structural changes along the rostro-caudal LC axis. A subgroup of 30 people with PD and 27 controls underwent 7T fMRI to assess LC responsivity to arousing auditory and visual stimuli in two fMRI sessions on separate days. Healthy controls were scanned twice without medication, while people with PD were studied on and off dopaminergic medication in counterbalanced order. In the PD group, MTw MRI confirmed a significant reduction of the regional neuromelanin signal in caudal LC relative to the control group (P = 0.0099). This structural disintegration correlated with orthostatic hypotension (P = 0.0087) and cognitive impairment (P = 0.036), corroborating its clinical relevance. Functional MRI revealed reduced activation of the caudal LC to arousing visual and auditory stimuli in people with PD relative to controls (P = 0.012). This difference reached statistical significance only in the ON-medication state, with a similar but non-significant trend in the OFF-medication state (P = 0.10). In an exploratory analysis of a smaller sub-sample, structural and functional caudal LC signals were significantly correlated in both people with PD and healthy controls (P = 0.0069). Together, the findings provide evidence for a rostro-caudal gradient of LC pathology in PD at both structural and functional levels. While structural MRI provides fine-grained insights into spatial gradients of disease-related pathology, functional MRI captures impaired functional responsivity of caudal LC. The presence of arousal-induced hypoactivation of caudal LC in the ON-medication state indicates that LC dysfunction extends beyond dopamine deficits in PD, highlighting complex interactions between dopaminergic and noradrenergic systems.

Learning from experience is driven by reward prediction errors—signals that reflect updates to our expectations of reward. Despite numerous studies on neural correlates of reward prediction errors, the question of how large-scale brain networks reconfigure in response to reward prediction error signalling remains open. Here we ask how functional networks change in response to reward prediction errors depending on the context. In our study participants performed the probabilistic reversal learning task in functional magnetic resonance imaging (fMRI) scanner in two experimental contexts: a reward-seeking setting and a punishment-avoiding. We found that the participants’ learning speed depended on the sign of the prediction error but not on the experimental context. Whole-brain network analysis revealed a multi-scale community structure with a separate striatal reward network emerging at a finer topological scale and a ventromedial prefrontal network emerging at a coarser scale. We also found that the integration between large-scale networks increased when switching from positive to negative prediction error events. This pattern of large-scale network reconfiguration aligns with the broad range of research showing increased network integration with increased cognitive demands. Our findings offer a first sketch of how processing reward prediction error affects the functional connectivity of brain-wide networks.

Impulsivity refers to the tendency to act prematurely or without forethought, and excessive impulsivity is a key problem in many neuropsychiatric disorders. Since the pre-supplementary motor area (preSMA) has been implicated in inhibitory control, this region may also contribute to impulsivity. Here, we examined whether functional recruitment of preSMA may contribute to risky choice behavior (state impulsivity) during sequential gambling and its relation to self-reported trait impulsivity. To this end, we performed task-based functional MRI (fMRI) after low-frequency (1 Hz) repetitive transcranial magnetic stimulation (rTMS) of the preSMA. We expected low-frequency rTMS to modulate task-related engagement of the preSMA and hereby, tune the tendency to make risky choices. 24 healthy volunteers (12 females, 19-52 years) received real or sham rTMS on separate days in counterbalanced order. Thereafter, participants performed a sequential gambling task with concurrently increasing stakes and risk during whole-brain fMRI. In the sham-rTMS session, self-reported trait impulsivity scaled positively with state impulsivity (riskier choice behavior) during gambling. The higher the trait-impulsivity, the lower was the task-related increase in preSMA activity with increasingly risky choices. Following real-rTMS, low-impulsivity participants increased their preference for risky choices, while the opposite was true for high-impulsivity participants resulting in an overall decoupling of trait impulsivity and state impulsivity during gambling. This rTMS-induced behavioral shift was mirrored in the rTMS-induced change in preSMA activation. These results provide converging evidence for a causal link between the level of task-related preSMA activity and the propensity for impulsive risk-taking behavior in the context of sequential gambling. Competing Interest Statement Conflict of interest statement: Al.L.: None. An.L.: speaking fees, Abbvie. H.R.S.: Has received honoraria as speaker from Lundbeck AS, Denmark, Sanofi Genzyme, Denmark and Novartis, Denmark, as consultant from Lundbeck AS, Denmark and Sanofi Genzyme, Denmark and as senior editor (NeuroImage) and editor-in-chief (Neuroimage Clinical) from Elsevier Publishers, Amsterdam, The Netherlands. He has received royalties as book editor from Springer Publishers, Stuttgart, Germany. D.M.: None.

Ergodicity describes an equivalence between the expectation value and the time average of observables. Applied to human behaviour, ergodic theories of decision-making reveal how individuals should tolerate risk in different environments. To optimise wealth over time, agents should adapt their utility function according to the dynamical setting they face. Linear utility is optimal for additive dynamics, whereas logarithmic utility is optimal for multiplicative dynamics. Whether humans approximate time optimal behavior across different dynamics is unknown. Here we compare the effects of additive versus multiplicative gamble dynamics on risky choice. We show that utility functions are modulated by gamble dynamics in ways not explained by prevailing decision theory. Instead, as predicted by time optimality, risk aversion increases under multiplicative dynamics, distributing close to the values that maximise the time average growth of wealth. We suggest that our findings motivate a need for explicitly grounding theories of decision-making on ergodic considerations.