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Ventral striatal network connectivity reflects reward learning and behavior in patients with Parkinson's disease.

ABSTRACT: A subgroup of Parkinson's disease (PD) patients treated with dopaminergic therapy develop compulsive reward-driven behaviors, which can result in life-altering morbidity. The mesocorticolimbic dopamine network guides reward-motivated behavior; however, its role in this treatment-related behavioral phenotype is incompletely understood. Here, mesocorticolimbic network function in PD patients who develop impulsive and compulsive behaviors (ICB) in response to dopamine agonists was assessed using BOLD fMRI. The tested hypothesis was that network connectivity between the ventral striatum and the limbic cortex is elevated in patients with ICB and that reward-learning proficiency reflects the extent of mesocorticolimbic network connectivity. To evaluate this hypothesis, 3.0T BOLD-fMRI was applied to measure baseline functional connectivity on and off dopamine agonist therapy in age and sex-matched PD patients with (n = 19) or without (n = 18) ICB. An incentive-based task was administered to a subset of patients (n = 20) to quantify positively or negatively reinforced learning. Whole-brain voxelwise analyses and region-of-interest-based mixed linear effects modeling were performed. Elevated ventral striatal connectivity to the anterior cingulate gyrus (P = 0.013), orbitofrontal cortex (P = 0.034), insula (P = 0.044), putamen (P = 0.014), globus pallidus (P < 0.01), and thalamus (P < 0.01) was observed in patients with ICB. A strong trend for elevated amygdala-to-midbrain connectivity was found in ICB patients on dopamine agonist. Ventral striatum-to-subgenual cingulate connectivity correlated with reward learning (P < 0.01), but not with punishment-avoidance learning. These data indicate that PD-ICB patients have elevated network connectivity in the mesocorticolimbic network. Behaviorally, proficient reward-based learning is related to this enhanced limbic and ventral striatal connectivity. Hum Brain Mapp 39:509-521, 2018. © 2017 Wiley Periodicals, Inc.

SUBMITTER: Petersen K

PROVIDER: S-EPMC5718974 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundGrowing evidence points towards dysfunction of the ventral striatum as a neural substrate of motivational impairments in schizophrenia. Ventral striatal activity during reward anticipation is generally reduced in patients with schizophrenia and specifically correlates with apathy. However, little is known about the cortico-striatal functional connectivity in patients with schizophrenia during reward anticipation and its relation to negative symptoms.ObjectivesThe aim of this study was to identify categorical group differences in ventral striatal functional connectivity during reward anticipation between patients with schizophrenia and healthy controls, and dimensional associations between cortico-striatal functional connectivity and negative symptom severity.MethodA total of 40 patients with schizophrenia (10 females) and 33 healthy controls (8 females) were included from two previously published studies. All participants performed a variant of the Monetary Incentive Delay Task while undergoing event-related fMRI. Functional connectivity was assessed using psychophysical interactions (PPI) with the left and right ventral striatum as seeds and the contrast [High Reward Anticipation - No Reward Anticipation]. Negative symptoms were assessed using the Brief Negative Symptom Scale.ResultsCompared to controls, patients with schizophrenia showed increased functional connectivity between the left ventral striatum and the left precuneus and right parahippocampal gyrus, two hubs of the default mode network (cluster-level threshold: FWE, p < .05). In addition, we found a negative association between apathy scores on the BNSS and increased functional connectivity between the left ventral striatum and the left ventral anterior insula / putamen and the left inferior frontal gyrus / dorsal anterior insula (cluster-level threshold: FWE, p < .05).ConclusionsOur results indicate that the patterns of increased functional connectivity between the ventral striatum and the dorsal default mode network during reward anticipation could act as a compensatory mechanism to regulate the activity of the ventral striatum. Our results also showed that functional connectivity patterns from the ventral striatum, much like its local activity, is specifically related to apathy, and not diminished expression.

Project description:OBJECTIVE:Evaluation of a data-driven, model-based classification approach to discriminate idiopathic Parkinson's disease (PD) patients from healthy controls (HC) based on between-network connectivity in whole-brain resting-state functional MRI (rs-fMRI). METHODS:Whole-brain rs-fMRI (EPI, TR = 2.2?s, TE = 30?ms, flip angle = 90°. resolution = 3.1 × 3.1 × 3.1?mm, acquisition time ? 11?min) was assessed in 42 PD patients (medical OFF) and 47 HC matched for age and gender. Between-network connectivity based on full and L2-regularized partial correlation measures were computed for each subject based on canonical functional network architectures of two cohorts at different levels of granularity (Human Connectome Project: 15/25/50/100/200 networks; 1000BRAINS: 15/25/50/70 networks). A Boosted Logistic Regression model was trained on the correlation matrices using a nested cross-validation (CV) with 10 outer and 10 inner folds for an unbiased performance estimate, treating the canonical functional network architecture and the type of correlation as hyperparameters. The number of boosting iterations was fixed at 100. The model with the highest mean accuracy over the inner folds was trained using an non-nested 10-fold 20-repeats CV over the whole dataset to determine feature importance. RESULTS:Over the outer folds the mean accuracy was found to be 76.2% (median 77.8%, SD 18.2, IQR 69.4 - 87.1%). Mean sensitivity was 81% (median 80%, SD 21.1, IQR 75 - 100%) and mean specificity was 72.7% (median 75%, SD 20.4, IQR 66.7 - 80%). The 1000BRAINS 50-network-parcellation, using full correlations, performed best over the inner folds. The top features predominantly included sensorimotor as well as sensory networks. CONCLUSION:A rs-fMRI whole-brain-connectivity, data-driven, model-based approach to discriminate PD patients from healthy controls shows a very good accuracy and a high sensitivity. Given the high sensitivity of the approach, it may be of use in a screening setting. ADVANCES IN KNOWLEDGE:Resting-state functional MRI could prove to be a valuable, non-invasive neuroimaging biomarker for neurodegenerative diseases. The current model-based, data-driven approach on whole-brain between-network connectivity to discriminate Parkinson's disease patients from healthy controls shows promising results with a very good accuracy and a very high sensitivity.

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Ventral striatal network connectivity reflects reward learning and behavior in patients with Parkinson's disease.

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