Dataset Information

Dopaminergic modulation of resting-state functional connectivity in de novo patients with Parkinson's disease.

ABSTRACT: Parkinson's disease (PD) is characterized by degenerative changes of nigral dopamine neurons, resulting in the dopaminergic denervation of the striatum. Resting state networks studies have demonstrated that dopamine modulates distinct network connectivity patterns in both a linear and a nonlinear fashion, but quantitative analyses of dopamine-dependent functional connectivity secondary to PD pathology were less informative. In the present study, we performed a correlation analysis between striatal dopamine levels assessed quantitatively by FP-CIT positron emission tomography imaging and resting-state functional connectivity in 23 drug naïve de novo patients with PD to elucidate dopamine-dependent functional networks. The major finding is that the patterns of dopamine-dependent positive functional connectivity varied depending on the location of striatal seeds. Dopamine-dependent functional connectivity with the caudate predominantly overlay pericentral cortical areas, whereas dopamine-dependent structures functionally connected with the posterior putamen predominantly involved cerebellar areas. The dorsolateral frontal area overlapped as a dopamine-dependent cortical region that was positively connected with the anterior and posterior putamen. On the other hand, cortical areas where functional connectivity from the posterior cingulate was negatively correlated with dopaminergic status in the posterior putamen were localized in the left anterior prefrontal area and the parietal area. Additionally, functional connectivity between the anterior putamen and mesiofrontal areas was negatively coupled with striatal dopamine levels. The present study demonstrated that dopamine-dependent functional network connectivity secondary to PD pathology mainly exhibits a consistent pattern, albeit with some variation. These patterns may reflect the diverse effects of dopaminergic medication on parkinsonian-related motor and cognitive performance.

SUBMITTER: Baik K

PROVIDER: S-EPMC6869560 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Dopaminergic modulation of resting-state functional connectivity in de novo patients with Parkinson's disease.

Baik KyoungWon K Cha Jungho J Ham Jee Hyun JH Baek Gwang-Min GM Sunwoo Mun Kyung MK Hong Jin Yong JY Shin Na-Young NY Kim Jae Seung JS Lee Jong-Min JM Lee Seung-Koo SK Sohn Young Ho YH Lee Phil Hyu PH

Human brain mapping 20140617 11

Parkinson's disease (PD) is characterized by degenerative changes of nigral dopamine neurons, resulting in the dopaminergic denervation of the striatum. Resting state networks studies have demonstrated that dopamine modulates distinct network connectivity patterns in both a linear and a nonlinear fashion, but quantitative analyses of dopamine-dependent functional connectivity secondary to PD pathology were less informative. In the present study, we performed a correlation analysis between striat ...[more]

PMID: 24938993

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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.

Dataset Information

Dopaminergic modulation of resting-state functional connectivity in de novo patients with Parkinson's disease.

Publications

Dopaminergic modulation of resting-state functional connectivity in de novo patients with Parkinson's disease.

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