Dataset Information

Parkinson's Disease Cognitive Phenotypes Show Unique Clock Drawing Features when Measured with Digital Technology.

ABSTRACT:

Background

A companion paper (Crowley et al., 2020) reports on the neuroimaging and neuropsychological profiles of statistically determined idiopathic non-dementia Parkinson's disease (PD).

Objective

The current investigation sought to further examine subtle behavioral clock drawing differences within the same PD cohort by comparing 1) PD to non-PD peers on digitally acquired clock drawing latency and graphomotor metrics, and 2) PD memory, executive, and cognitively well phenotypes on the same variables.

Methods

230 matched participants (115 PD, 115 non-PD) completed neuropsychological tests and dCDT. Statistically-derived PD cognitive phenotypes characterized PD participants as PD low executive (PDExe; n = 25), PD low memory (PDMem; n = 34), PD cognitively well (PDWell; n = 56). Using a Bayesian framework and based on apriori hypotheses, we compared groups on: total completion time (TCT), pre-first hand latency (PFHL), post-clock face latency (PCFL), total clock face area (TCFA), and total number of pen strokes.

Results

Fewer strokes and slower performance to command were associated with higher odds of PD diagnosis, while a larger clock face in the copy condition was associated with lower odds of PD diagnosis. Within PD cognitive phenotypes, slower performance (TCT, PCFL) and smaller clock face to command were associated with higher odds of being PDExe than PDWell, whereas larger clock faces associated with higher odds of being PDMem than PDWell. Longer disease duration, more pen strokes (command) and smaller clocks (command) associated with higher odds of being PDExe than PDWell.

Conclusion

Digitally-acquired clock drawing profiles differ between PD and non-PD peers, and distinguish PD cognitive phenotypes.

SUBMITTER: Dion C

PROVIDER: S-EPMC8108094 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCerebral microbleeds (CMB) increase the risk for Alzheimer disease. Current neuroimaging methods that are used to detect CMB are costly and not always accessible.ObjectiveThis study aimed to explore whether the digital clock-drawing test (DCT) may provide a behavioral indicator of CMB.MethodsIn this study, we analyzed data from participants in the Framingham Heart Study offspring cohort who underwent both brain magnetic resonance imaging scans (Siemens 1.5T, Siemens Healthcare Private Limited; T2*-GRE weighted sequences) for CMB diagnosis and the DCT as a predictor. Additionally, paper-based clock-drawing tests were also collected during the DCT. Individuals with a history of dementia or stroke were excluded. Robust multivariable linear regression models were used to examine the association between DCT facet scores with CMB prevalence, adjusting for relevant covariates. Receiver operating characteristic (ROC) curve analyses were used to evaluate DCT facet scores as predictors of CMB prevalence. Sensitivity analyses were conducted by further including participants with stroke and dementia.ResultsThe study sample consisted of 1020 (n=585, 57.35% female) individuals aged 45 years and older (mean 72, SD 7.9 years). Among them, 64 (6.27%) participants exhibited CMB, comprising 46 with lobar-only, 11 with deep-only, and 7 with mixed (lobar+deep) CMB. Individuals with CMB tended to be older and had a higher prevalence of mild cognitive impairment and higher white matter hyperintensities compared to those without CMB (P<.05). While CMB were not associated with the paper-based clock-drawing test, participants with CMB had a lower overall DCT score (CMB: mean 68, SD 23 vs non-CMB: mean 76, SD 20; P=.009) in the univariate comparison. In the robust multiple regression model adjusted for covariates, deep CMB were significantly associated with lower scores on the drawing efficiency (β=-0.65, 95% CI -1.15 to -0.15; P=.01) and simple motor (β=-0.86, 95% CI -1.43 to -0.30; P=.003) domains of the command DCT. In the ROC curve analysis, DCT facets discriminated between no CMB and the CMB subtypes. The area under the ROC curve was 0.76 (95% CI 0.69-0.83) for lobar CMB, 0.88 (95% CI 0.78-0.98) for deep CMB, and 0.98 (95% CI 0.96-1.00) for mixed CMB, where the area under the ROC curve value nearing 1 indicated an accurate model.ConclusionsThe study indicates a significant association between CMB, especially deep and mixed types, and reduced performance in drawing efficiency and motor skills as assessed by the DCT. This highlights the potential of the DCT for early detection of CMB and their subtypes, providing a reliable alternative for cognitive assessment and making it a valuable tool for primary care screening before neuroimaging referral.