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Resting state functional connectivity of the striatum in Parkinson's disease.

ABSTRACT: Classical accounts of the pathophysiology of Parkinson's disease have emphasized degeneration of dopaminergic nigrostriatal neurons with consequent dysfunction of cortico-striatal-thalamic loops. In contrast, post-mortem studies indicate that pathological changes in Parkinson's disease (Lewy neurites and Lewy bodies) first appear primarily in the lower brainstem with subsequent progression to more rostral parts of the neuraxis. The nigrostriatal and histological perspectives are not incompatible, but they do emphasize different anatomical structures. To address the question of which brain structures are functionally most affected by Parkinson's disease, we performed a resting-state functional magnetic resonance imaging study focused on striatal functional connectivity. We contrasted 13 patients with advanced Parkinson's disease versus 19 age-matched control subjects, using methodology incorporating scrupulous attention to minimizing the effects of head motion during scanning. The principal finding in the Parkinson's disease group was markedly lower striatal correlations with thalamus, midbrain, pons and cerebellum. This result reinforces the importance of the brainstem in the pathophysiology of Parkinson's disease. Focally altered functional connectivity also was observed in sensori-motor and visual areas of the cerebral cortex, as well the supramarginal gyrus. Striatal functional connectivity with the brainstem was graded (posterior putamen > anterior putamen > caudate), in both patients with Parkinson's disease and control subjects, in a manner that corresponds to well-documented gradient of striatal dopaminergic function loss in Parkinson's disease. We hypothesize that this gradient provides a clue to the pathogenesis of Parkinson's disease.

SUBMITTER: Hacker CD

PROVIDER: S-EPMC3525055 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Resting state functional connectivity of the striatum in Parkinson's disease.

Hacker Carl D CD Perlmutter Joel S JS Criswell Susan R SR Ances Beau M BM Snyder Abraham Z AZ

Brain : a journal of neurology 20121128 Pt 12

Classical accounts of the pathophysiology of Parkinson's disease have emphasized degeneration of dopaminergic nigrostriatal neurons with consequent dysfunction of cortico-striatal-thalamic loops. In contrast, post-mortem studies indicate that pathological changes in Parkinson's disease (Lewy neurites and Lewy bodies) first appear primarily in the lower brainstem with subsequent progression to more rostral parts of the neuraxis. The nigrostriatal and histological perspectives are not incompatible ...[more]

PMID: 23195207

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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

Resting state functional connectivity of the striatum in Parkinson's disease.

Publications

Resting state functional connectivity of the striatum in Parkinson's disease.

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