Dataset Information

Cognition and the course of prodromal Parkinson's disease.

ABSTRACT: BACKGROUND:Prospective data on cognition in prodromal Parkinson's disease are limited. The objectives of this study were to assess in prodromal PD (1) if baseline cognition predicts conversion to clinical PD, (2) if baseline dopamine transporter binding predicts longitudinal changes in cognition, and (3) if impaired olfaction predicts future cognitive decline. METHODS:Prodromal participants were 136 hyposmic individuals enrolled in the Parkinson Associated Risk Study. We examined baseline neuropsychological test performance in PD converters versus nonconverters and the association between baseline dopamine transporter binding and change in cognition. An additional 73 normosmic individuals were included in analyses of the relationship between hyposmia and cognitive decline. RESULTS:In prodromal participants, baseline cognitive scores did not significantly predict conversion, but converters performed numerically worse on 5 of the 6 cognitive domains assessed, with the greatest differences in executive function/working memory (0.68 standard deviation lower) and global cognition (0.64 standard deviation lower). Lower baseline dopamine transporter binding predicted greater future decline in processing speed/attention (P?=?0.02). Hyposmia predicted greater future decline in language (P?=?0.005) and memory (P?=?0.01) abilities. CONCLUSIONS:Given hyposmia in the general population predicts cognitive decline, the role of cognition in predicting conversion in prodromal PD needs to be assessed in large cohorts followed long-term. The dopamine system may be associated with changes in processing speed/attention in individuals at risk for PD. © 2017 International Parkinson and Movement Disorder Society.

SUBMITTER: Weintraub D

PROVIDER: S-EPMC5722226 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Cognition and the course of prodromal Parkinson's disease.

Weintraub Daniel D Chahine Lana M LM Hawkins Keith A KA Siderowf Andrew A Eberly Shirley S Oakes David D Seibyl John J Stern Matthew B MB Marek Kenneth K Jennings Danna D

Movement disorders : official journal of the Movement Disorder Society 20171024 11

<h4>Background</h4>Prospective data on cognition in prodromal Parkinson's disease are limited. The objectives of this study were to assess in prodromal PD (1) if baseline cognition predicts conversion to clinical PD, (2) if baseline dopamine transporter binding predicts longitudinal changes in cognition, and (3) if impaired olfaction predicts future cognitive decline.<h4>Methods</h4>Prodromal participants were 136 hyposmic individuals enrolled in the Parkinson Associated Risk Study. We examined ...[more]

PMID: 29063713

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Project description:In Parkinson's disease, electroencephalographic abnormalities during wakefulness and non-rapid eye movement sleep (spindles) were found to be predictive biomarkers of dementia. Because rapid eye movement sleep is regulated by the cholinergic system, which shows early degeneration in Parkinson's disease with cognitive impairment, anomalies during this sleep stage might mirror dementia development. In this prospective study, we examined baseline electroencephalographic absolute spectral power across three states of consciousness (non-rapid eye movement sleep, rapid eye movement sleep, and wakefulness) in 68 non-demented patients with Parkinson's disease and 44 healthy controls. All participants underwent baseline polysomnographic recordings and a comprehensive neuropsychological assessment. Power spectral analyses were performed on standard frequency bands. Dominant occipital frequency during wakefulness and ratios of slow-to-fast frequencies during rapid eye movement sleep and wakefulness were also computed. At follow-up (an average 4.5 years after baseline), 18 patients with Parkinson's disease had developed dementia and 50 patients remained dementia-free. In rapid eye movement sleep, patients with Parkinson's disease who later developed dementia showed, at baseline, higher absolute power in delta and theta bands and a higher slowing ratio, especially in temporal, parietal, and occipital regions, compared to patients who remained dementia-free and controls. In non-rapid eye movement sleep, lower baseline sigma power in parietal cortical regions also predicted development of dementia. During wakefulness, patients with Parkinson's disease who later developed dementia showed lower dominant occipital frequency as well as higher delta and slowing ratio compared to patients who remained dementia-free and controls. At baseline, higher slowing ratios in temporo-occipital regions during rapid eye movement sleep were associated with poor performance on visuospatial tests in patients with Parkinson's disease. Using receiver operating characteristic curves, we found that best predictors of dementia in Parkinson's disease were rapid eye movement sleep slowing ratios in posterior regions, wakefulness slowing ratios in temporal areas, and lower dominant occipital frequency. These results suggest that electroencephalographic slowing during sleep is a new promising predictive biomarker for Parkinson's disease dementia, perhaps as a marker of cholinergic denervation.

Project description:Parkinson's disease is the second most prevalent neurodegenerative disorder in the Western world. It is estimated that the neuronal loss related to Parkinson's disease precedes the clinical diagnosis by more than 10 years (prodromal phase) which leads to a subtle decline that translates into non-specific clinical signs and symptoms. By leveraging diffusion magnetic resonance imaging brain (MRI) data evaluated longitudinally, at least at two different time points, we have the opportunity of detecting and measuring brain changes early on in the neurodegenerative process, thereby allowing early detection and monitoring that can enable development and testing of disease modifying therapies. In this study, we were able to define a longitudinal degenerative Parkinson's disease progression pattern using diffusion magnetic resonance imaging connectivity information. Such pattern was discovered using a de novo early Parkinson's disease cohort (n = 21), and a cohort of Controls (n = 30). Afterward, it was tested in a cohort at high risk of being in the Parkinson's disease prodromal phase (n = 16). This progression pattern was numerically quantified with a longitudinal brain connectome progression score. This score is generated by an interpretable machine learning (ML) algorithm trained, with cross-validation, on the longitudinal connectivity information of Parkinson's disease and Control groups computed on a nigrostriatal pathway-specific parcellation atlas. Experiments indicated that the longitudinal brain connectome progression score was able to discriminate between the progression of Parkinson's disease and Control groups with an area under the receiver operating curve of 0.89 [confidence interval (CI): 0.81-0.96] and discriminate the progression of the High Risk Prodromal and Control groups with an area under the curve of 0.76 [CI: 0.66-0.92]. In these same subjects, common motor and cognitive clinical scores used in Parkinson's disease research showed little or no discriminative ability when evaluated longitudinally. Results suggest that it is possible to quantify neurodegenerative patterns of progression in the prodromal phase with longitudinal diffusion magnetic resonance imaging connectivity data and use these image-based patterns as progression markers for neurodegeneration.

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Cognition and the course of prodromal Parkinson's disease.

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Cognition and the course of prodromal Parkinson's disease.

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