scholarly journals Cognitive Profiles in Parkinson’s Disease and Their Relation to Dementia: A Data-Driven Approach

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Inga Liepelt-Scarfone ◽  
Susanne Gräber ◽  
Monika Fruhmann Berger ◽  
Anne Feseker ◽  
Gülsüm Baysal ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Hierarchical Cluster ◽  
Individual Performance ◽  
Data Driven ◽  
Data Sets ◽  
Cognitive Profiles ◽  
Cluster Analyses ◽  
Data Driven Approach

Parkinson’s disease is characterized by a substantial cognitive heterogeneity, which is apparent in different profiles and levels of severity. To date, a distinct clinical profile for patients with a potential risk of developing dementia still has to be identified. We introduce a data-driven approach to detect different cognitive profiles and stages. Comprehensive neuropsychological data sets from a cohort of 121 Parkinson’s disease patients with and without dementia were explored by a factor analysis to characterize different cognitive domains. Based on the factor scores that represent individual performance in each domain, hierarchical cluster analyses determined whether subgroups of Parkinson’s disease patients show varying cognitive profiles. A six-factor solution accounting for 65.2% of total variance fitted best to our data and revealed high internal consistencies (Cronbach’s alpha coefficients>0.6). The cluster analyses suggested two independent patient clusters with different cognitive profiles. They differed only in severity of cognitive impairment and self-reported limitation of activities of daily living function but not in motor performance, disease duration, or dopaminergic medication. Based on a data-driven approach, divers cognitive profiles were identified, which separated early and more advanced stages of cognitive impairment in Parkinson’s disease without dementia. Importantly, these profiles were independent of motor progression.

scholarly journals Mapping Actuarial Criteria for Parkinson’s Disease-Mild Cognitive Impairment onto Data-Driven Cognitive Phenotypes

2021 ◽  
Vol 12 (1) ◽  
pp. 54
Author(s):  
Lauren E. Kenney ◽  
Adrianna M. Ratajska ◽  
Francesca V. Lopez ◽  
Catherine C. Price ◽  
Melissa J. Armstrong ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Neuropsychological Testing ◽  
Data Driven ◽  
Prevalence Rates ◽  
Cluster Analyses ◽  
Cluster Membership ◽  
Dementia Risk

Prevalence rates for mild cognitive impairment in Parkinson’s disease (PD-MCI) remain variable, obscuring the diagnosis’ predictive utility of greater dementia risk. A primary factor of this variability is inconsistent operationalization of normative cutoffs for cognitive impairment. We aimed to determine which cutoff was optimal for classifying individuals as PD-MCI by comparing classifications against data-driven PD cognitive phenotypes. Participants with idiopathic PD (n = 494; mean age 64.7 ± 9) completed comprehensive neuropsychological testing. Cluster analyses (K-means, Hierarchical) identified cognitive phenotypes using domain-specific composites. PD-MCI criteria were assessed using separate cutoffs (−1, −1.5, −2 SD) on ≥2 tests in a domain. Cutoffs were compared using PD-MCI prevalence rates, MCI subtype frequencies (single/multi-domain, executive function (EF)/non-EF impairment), and validity against the cluster-derived cognitive phenotypes (using chi-square tests/binary logistic regressions). Cluster analyses resulted in similar three-cluster solutions: Cognitively Average (n = 154), Low EF (n = 227), and Prominent EF/Memory Impairment (n = 113). The −1.5 SD cutoff produced the best model of cluster membership (PD-MCI classification accuracy = 87.9%) and resulted in the best alignment between PD-MCI classification and the empirical cognitive profile containing impairments associated with greater dementia risk. Similar to previous Alzheimer’s work, these findings highlight the utility of comparing empirical and actuarial approaches to establish concurrent validity of cognitive impairment in PD.

Motor patterns in Parkinson's disease: A data-driven approach

2009 ◽  
Vol 24 (7) ◽  
pp. 1042-1047 ◽  
Author(s):  
Stephanie M. van Rooden ◽  
Martine Visser ◽  
Dagmar Verbaan ◽  
Johan Marinus ◽  
Jacobus J. van Hilten
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Data Driven ◽  
Motor Patterns ◽  
Data Driven Approach

The spectrum of cognitive disorders in Parkinson's disease: A data-driven approach

2013 ◽  
Vol 28 (2) ◽  
pp. 183-189 ◽  
Author(s):  
Kathy Dujardin ◽  
Albert F.G. Leentjens ◽  
Carole Langlois ◽  
Anja J.H. Moonen ◽  
Annelien A. Duits ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Disorders ◽  
Data Driven ◽  
Data Driven Approach

scholarly journals Cluster analysis of cognitive performance in a sample of patients with Parkinson's disease

2016 ◽  
Vol 10 (4) ◽  
pp. 315-319 ◽  
Author(s):  
Carolina Pinto Souza ◽  
Guiomar Nascimento Oliveira ◽  
Maria Paula Foss ◽  
Vitor Tumas
Keyword(s):  
Parkinson’S Disease ◽  
Cluster Analysis ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Rating Scale ◽  
Digit Span ◽  
Depression Scale ◽  
Cognitive Profiles ◽  
Clinical Dementia Rating ◽  
Increased Risk

ABSTRACT Background: Cognitive impairment is a common feature of Parkinson's disease (PD). The diagnoses of mild cognitive impairment (MCI) in patients with PD implies an increased risk for later development of dementia, however, it is unclear whether a specific type of cognitive loss confers increased risk for faster cognitive decline. Objective: Determine whether it was possible to identify distinct cognitive phenotypes in a sample of patients with PD. Methods: A cross-sectional evaluation of 100 patients with PD recruited from a movement disorders clinic was conducted. The patients were evaluated using the simplified motor score of the UPDRS, the Hoehn and Yahr, Schwab and England, Geriatric Depression Scale, Pfeffer Functional Activities Questionnaire, Clinical Dementia Rating Scale, Mini-Mental State Examination, clock drawing test, digit span, word list battery of CERAD, Frontal Assessment Battery and verbal fluency test. We classified the patients as having normal cognition (PDNC), MCI (PDMCI) or dementia (PDD). Data were analyzed using the chi-square test, non-parametric statistics and cluster analysis. Results: There were 40 patients with PDD, 39 with PDMCI and 21 with PDNC. Patients with PDD were older, had longer disease duration, lower education and lower MMSE scores. Cluster analysis showed 3 general distinct cognitive profiles that represented a continuum from mild to severe impairment of cognition, without distinguishing specific cognitive profiles. Conclusion: Cognitive impairment in PD occurs progressively and heterogeneously in most patients. It is unclear whether the definition of the initial phenotype of cognitive loss can be used to establish the cognitive prognosis of patients.

scholarly journals Cognitive Impairment in Genetic Parkinson’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
A. Planas-Ballvé ◽  
D. Vilas
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Cognitive Function ◽  
Cognitive Decline ◽  
Autosomal Recessive ◽  
Autosomal Dominant ◽  
Cognitive Profiles ◽  
And Biological Markers ◽  
Gba Mutations

Cognitive impairment is common in idiopathic Parkinson’s disease (PD). Knowledge of the contribution of genetics to cognition in PD is increasing in the last decades. Monogenic forms of genetic PD show distinct cognitive profiles and rate of cognitive decline progression. Cognitive impairment is higher in GBA- and SNCA-associated PD, lower in Parkin- and PINK1-PD, and possibly milder in LRRK2-PD. In this review, we summarize data regarding cognitive function on clinical studies, neuroimaging, and biological markers of cognitive decline in autosomal dominant PD linked to mutations in LRRK2 and SNCA, autosomal recessive PD linked to Parkin and PINK1, and also PD linked to GBA mutations.

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