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The relative inability to produce effortful movements is the most specific motor sign of Parkinson's disease, which is primarily characterized by loss of dopaminergic terminals in the putamen. The motor motivation hypothesis suggests that this motor deficit may not reflect a deficiency in motor control per se , but a deficiency in cost-benefit considerations for motor effort. For the first time, we investigated the quantitative effect of dopamine depletion on the motivation of motor effort in Parkinson's disease. A total of 21 early-stage, unmedicated patients with Parkinson's disease and 26 healthy controls were included. An incentivized force task was used to capture the amount of effort participants were willing to invest for different monetary incentive levels and dopamine transporter depletion in the bilateral putamen was assessed. Our results demonstrate that patients with Parkinson's disease applied significantly less grip force than healthy controls, especially for low incentive levels. Congruously, decrease of motor effort with greater loss of putaminal dopaminergic terminals was most pronounced for low incentive levels. This signifies that putaminal dopamine is most critical to motor effort when the trade-off with the benefit is poor. Taken together, we provide direct evidence that the reduction of effortful movements in Parkinson's disease depends on motivation and that this effect is associated with putaminal dopaminergic degeneration. [ABSTRACT FROM AUTHOR]

Recent studies in Parkinson's disease (PD) patients reported disruptions in dynamic functional connectivity (dFC, i.e., a characterization of spontaneous fluctuations in functional connectivity over time). Here, we assessed whether the integrity of striatal dopamine terminals directly modulates dFC metrics in two separate PD cohorts, indexing dopamine‐related changes in large‐scale brain network dynamics and its implications in clinical features. We pooled data from two disease‐control cohorts reflecting early PD. From the Parkinson's Progression Marker Initiative (PPMI) cohort, resting‐state functional magnetic resonance imaging (rsfMRI) and dopamine transporter (DaT) single‐photon emission computed tomography (SPECT) were available for 63 PD patients and 16 age‐ and sex‐matched healthy controls. From the clinical research group 219 (KFO) cohort, rsfMRI imaging was available for 52 PD patients and 17 age‐ and sex‐matched healthy controls. A subset of 41 PD patients and 13 healthy control subjects additionally underwent 18F‐DOPA‐positron emission tomography (PET) imaging. The striatal synthesis capacity of 18F‐DOPA PET and dopamine terminal quantity of DaT SPECT images were extracted for the putamen and the caudate. After rsfMRI pre‐processing, an independent component analysis was performed on both cohorts simultaneously. Based on the derived components, an individual sliding window approach (44 s window) and a subsequent k‐means clustering were conducted separately for each cohort to derive dFC states (reemerging intra‐ and interindividual connectivity patterns). From these states, we derived temporal metrics, such as average dwell time per state, state attendance, and number of transitions and compared them between groups and cohorts. Further, we correlated these with the respective measures for local dopaminergic impairment and clinical severity. The cohorts did not differ regarding age and sex. Between cohorts, PD groups differed regarding disease duration, education, cognitive scores and L‐dopa equivalent daily dose. In both cohorts, the dFC analysis resulted in three distinct states, varying in connectivity patterns and strength. In the PPMI cohort, PD patients showed a lower state attendance for the globally integrated (GI) state and a lower number of transitions than controls. Significantly, worse motor scores (Unified Parkinson's Disease Rating Scale Part III) and dopaminergic impairment in the putamen and the caudate were associated with low average dwell time in the GI state and a low total number of transitions. These results were not observed in the KFO cohort: No group differences in dFC measures or associations between dFC variables and dopamine synthesis capacity were observed. Notably, worse motor performance was associated with a low number of bidirectional transitions between the GI and the lesser connected (LC) state across the PD groups of both cohorts. Hence, in early PD, relative preservation of motor performance may be linked to a more dynamic engagement of an interconnected brain state. Specifically, those large‐scale network dynamics seem to relate to striatal dopamine availability. Notably, most of these results were obtained only for one cohort, suggesting that dFC is impacted by certain cohort features like educational level, or disease severity. As we could not pinpoint these features with the data at hand, we suspect that other, in our case untracked, demographical features drive connectivity dynamics in PD. Practitioner Points: Exploring dopamine's role in brain network dynamics in two Parkinson's disease (PD) cohorts, we unraveled PD‐specific changes in dynamic functional connectivity.Results in the Parkinson's Progression Marker Initiative (PPMI) and the KFO cohort suggest motor performance may be linked to a more dynamic engagement and disengagement of an interconnected brain state.Results only in the PPMI cohort suggest striatal dopamine availability influences large‐scale network dynamics that are relevant in motor control. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is currently diagnosed largely on the basis of expert judgement with neuroimaging serving only as a supportive tool. In a recent study, we identified a hypometabolic midbrain cluster, which includes parts of the substantia nigra, as the best differentiating metabolic feature for PD- patients based on group comparison of [18F]-fluorodeoxyglucose ([18F]-FDG) PET scans. Longitudinal analyses confirmed progressive metabolic changes in this region and, an independent study showed great potential of nigral metabolism for diagnostic workup of parkinsonian syndromes. In this study, we applied amachine learning approach to evaluatemidbrainmetabolismmeasured by [18F]-FDG PET as a diagnostic marker for PD. In total, 51 mid-stage PD- patients and 16 healthy control subjects underwent high-resolution [18F]-FDG PET. Normalized tracer update values of the midbrain cluster identified by between-group comparison were extracted voxel-wise from individuals' scans. Extracted uptake values were subjected to a random forest feature classification algorithm. An adapted leave-one-out cross validation approach was applied for testing robustness of the model for differentiating between patients and controls. Performance of the model across all runs was evaluated by calculating sensitivity, specificity and model accuracy for the validation data set and the percentage of correctly categorized subjects for test data sets. The randomforest feature classification of voxel-based uptake values from the midbrain cluster identified patients in the validation data set with an average sensitivity of 0.91 (Min: 0.82, Max: 0.94). For all 67 runs, in which each of the individuals was treated once as test data set, the test data set was correctly categorized by our model. The applied feature importance extraction consistently identified a subset of voxels within the midbrain cluster with highest importance across all runs which spatially converged with the left substantia nigra. Our data suggest midbrain metabolism measured by [18F]-FDG PET as a promising diagnostic imaging tool for PD. Given its close relationship to PD pathophysiology and very high discriminatory accuracy, this approach could help to objectify PD diagnosis and enable more accurate classification in relation to clinical trials, which could also be applicable to patients with prodromal disease. [ABSTRACT FROM AUTHOR]

Objective: While variations in the first intron of the fat mass and obesityassociated gene (FTO, rs9939609 T/A variant) have long been identified as a major contributor to polygenic obesity, the mechanisms underlying weight gain in risk allele carriers still remain elusive. On a behavioral level, FTO variants have been robustly linked to trait impulsivity. The regulation of dopaminergic signaling in the meso-striatal neurocircuitry by these FTO variants might represent one mechanism for this behavioral alteration. Notably, recent evidence indicates that variants of FTO also modulate several genes involved in cell proliferation and neuronal development. Hence, FTO polymorphisms might establish a predisposition to heightened trait impulsivity during neurodevelopment by altering structural meso-striatal connectivity. We here explored whether the greater impulsivity of FTO variant carriers was mediated by structural differences in the connectivity between the dopaminergic midbrain and the ventral striatum. Methods: Eighty-seven healthy normal-weight volunteers participated in the study; 42 FTO risk allele carriers (rs9939609 T/A variant, FTO+ group: AT, AA) and 39 non-carriers (FTO-group: TT) were matched for age, sex and body mass index (BMI). Trait impulsivity was assessed via the Barratt Impulsiveness Scale (BIS-11) and structural connectivity between the ventral tegmental area/substantia nigra (VTA/SN) and the nucleus accumbens (NAc) was measured via diffusion weighted MRI and probabilistic tractography. Results: We found that FTO risk allele carriers compared to non-carriers, demonstrated greater motor impulsivity (p = 0.04) and increased structural connectivity between VTA/SN and the NAc (p< 0.05). Increased connectivity partially mediated the effect of FTO genetic status on motor impulsivity. Conclusion: We report altered structural connectivity as one mechanism by which FTO variants contribute to increased impulsivity, indicating that FTO variants may exert their effect on obesity-promoting behavioral traits at least partially through neuroplastic alterations in humans. [ABSTRACT FROM AUTHOR]