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Cognitive profiles in Parkinson's disease and their relation to dementia: a data-driven approach.

ABSTRACT: 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.

SUBMITTER: Liepelt-Scarfone I

PROVIDER: S-EPMC3483831 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Cognitive profiles in Parkinson's disease and their relation to dementia: a data-driven approach.

Liepelt-Scarfone Inga I Gräber Susanne S Fruhmann Berger Monika M Feseker Anne A Baysal Gülsüm G Csoti Ilona I Godau Jana J Gaenslen Alexandra A Huber Heiko H Srulijes Karin K Brockmann Kathrin K Berg Daniela D

International journal of Alzheimer's disease 20121018

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 an ...[more]

PMID: 23119224

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Project description:BackgroundDegeneration of the cholinergic system plays an important role in cognitive impairment in Parkinson's disease (PD). Positron emission tomography (PET) imaging using the presynaptic vesicular acetylcholine transporter (VAChT) tracer [18F]Fluoroethoxybenzovesamicol ([18F]FEOBV) allows for regional assessment of cholinergic innervation. The purpose of this study was to perform a data-driven analysis to identify co-varying cholinergic regions and to evaluate the relationship of these with cognitive functioning in PD.Materials and methodsA total of 87 non-demented PD patients (77% male, mean age 67.9 ± 7.6 years, disease duration 5.8 ± 4.6 years) and 27 healthy control (HC) subjects underwent [18F]FEOBV brain PET imaging and neuropsychological assessment. A volume-of-interest based factor analysis was performed for both groups to identify cholinergic principal components (PCs).ResultsSeven main PCs were identified for the PD group: (1) bilateral posterior cortex, (2) bilateral subcortical, (3) bilateral centro-cingulate, (4) bilateral frontal, (5) right-sided fronto-temporal, (6) cerebellum, and (7) predominantly left sided temporal regions. A complementary principal component analysis (PCA) analysis in the control group showed substantially different cholinergic covarying patterns. A multivariate linear regression analyses demonstrated PC3, PC5, and PC7, together with motor impairment score, as significant predictors for cognitive functioning in PD. PC3 showed most robust correlations with cognitive functioning (p < 0.001).ConclusionA data-driven approach identified covarying regions in the bilateral peri-central and cingulum cortex as a key determinant of cognitive impairment in PD. Cholinergic vulnerability of the centro-cingulate network appears to be disease-specific for PD rather than being age-related. The cholinergic system may be an important contributor to regional and large scale neural networks involved in cognitive functioning.

Project description:Individual differences in dopaminergic tone underlie tendencies to learn from reward versus punishment. These effects are well documented in Parkinson's patients, who vacillate between low and high tonic dopaminergic states as a function of medication. Yet very few studies have investigated the influence of higher-level cognitive states known to affect downstream dopaminergic learning in Parkinson's patients. A dopamine-dependent cognitive influence over learning would provide a candidate mechanism for declining cognitive integrity and motivation in Parkinson's patients. In this report we tested the influence of two high-level cognitive states (cost of conflict and value of volition) that have recently been shown to cause predictable learning biases in healthy young adults as a function of dopamine receptor subtype and dopaminergic challenge. It was hypothesized that Parkinson's patients OFF medication would have an enhanced cost of conflict and a decreased value of volition, and that these effects would be remediated or reversed ON medication. Participants included N = 28 Parkinson's disease patients who were each tested ON and OFF dopaminergic medication and 28 age- and sex-matched controls. The expected cost of conflict effect was observed in Parkinson's patients OFF versus ON medication, but only in those that were more recently diagnosed (<5 years). We found an unexpected effect in the value of volition task: medication compromised the ability to learn from difficult a-volitional (instructed) choices. This novel finding was also enhanced in recently diagnosed patients. The difference in learning biases ON versus OFF medication between these two tasks was strongly correlated, bolstering the idea that they tapped into a common underlying imbalance in dopaminergic tone that is particularly variable in earlier stage Parkinsonism. The finding that these decision biases are specific to earlier but not later stage disease may offer a chance for future studies to quantify phenotypic expressions of idiosyncratic disease progression.

Project description:Background and purposeThe aim of this study was to examine the risk factors, prodromal symptoms, non-motor symptoms (NMS), and motor symptoms (MS) in different Parkinson's disease (PD) non-motor subtypes, classified using newly established criteria and a data-driven approach.MethodsA total of 168 patients with idiopathic PD underwent comprehensive NMS and MS examinations. NMS were assessed by the Non-Motor Symptom Scale (NMSS), Montreal Cognitive Assessment (MoCA), Hamilton Depression Scale (HAM-D), Hamilton Anxiety Rating Scale (HAM-A), REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ), Epworth Sleepiness Scale (ESS), Starkstein Apathy Scale (SAS) and Fatigue Severity Scale (FSS). Motor subtypes were classified based on Stebbins' method. Patients were classified into groups of three NMS subtypes (cortical, limbic, and brainstem) based on the newly designed inclusion criteria. Further, data-driven clustering was performed as an alternative, statistical learning-based classification approach. The two classification approaches were compared for consistency.ResultsWe identified 38 (22.6%) patients with the cortical subtype, 48 (28.6%) with the limbic, and 82 (48.8%) patients with the brainstem NMS PD subtype. Using a data-driven approach, we identified five different clusters. Three corresponded to the cortical, limbic, and brainstem subtypes, while the two additional clusters may have represented patients with early and advanced PD. Pearson chi-square test of independence revealed that a priori classification and cluster membership were significantly related to one another with a large effect size (χ2(8) = 175.001, p < 0.001, Cramer's V = 0.722). The demographic and clinical profiles differed between NMS subtypes and clusters.ConclusionUsing the actuarial and clustering approach, marked differences between individual NMS subtypes were found. The newly established criteria have potential as a simplified tool for future clinical research of NMS subtypes of Parkinson's disease.

Dataset Information

Cognitive profiles in Parkinson's disease and their relation to dementia: a data-driven approach.

Publications

Cognitive profiles in Parkinson's disease and their relation to dementia: a data-driven approach.

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