Classifying Non-Dementia and Alzheimer’s Disease/Vascular Dementia Patients Using Kinematic, Time-Based, and Visuospatial Parameters: The Digital Clock Drawing Test

Article type: Research Article

Authors: Davoudi, Anis^a | Dion, Catherine^b | Amini, Shawna^b | Tighe, Patrick J.^d | Price, Catherine C.^{b; e; *} | Libon, David J.^{c; 1} | Rashidi, Parisa^{a; 1}

Affiliations: [a] Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA | [b] Clinical and Health Psychology, University of Florida, Gainesville, FL, USA | [c] Department of Geriatrics and Gerontology, New Jersey Institute for Successful Aging, School of Osteopathic Medicine, and the Department of Psychology, Rowan University, Glassboro, NJ, USA | [d] Department of Psychology, Rowan University, Glassboro, NJ, USA | [e] Department of Anesthesiology, University of Florida, Gainesville, FL, USA

Correspondence: [*] Correspondence to: Catherine Price, PhD, Associate Professor, Clinical and Health Psychology, University of Florida, Gainesville, FL 32610, USA. Tel.: +1 352 273 5929; E-mail: [email protected].

Note: [1] These authors contributed equally to this work.

Abstract: Background:Advantages of digital clock drawing metrics for dementia subtype classification needs examination. Objective:To assess how well kinematic, time-based, and visuospatial features extracted from the digital Clock Drawing Test (dCDT) can classify a combined group of Alzheimer’s disease/Vascular Dementia patients versus healthy controls (HC), and classify dementia patients with Alzheimer’s disease (AD) versus vascular dementia (VaD). Methods:Healthy, community-dwelling control participants (n = 175), patients diagnosed clinically with Alzheimer’s disease (n = 29), and vascular dementia (n = 27) completed the dCDT to command and copy clock drawing conditions. Thirty-seven dCDT command and 37 copy dCDT features were extracted and used with Random Forest classification models. Results:When HC participants were compared to participants with dementia, optimal area under the curve was achieved using models that combined both command and copy dCDT features (AUC = 91.52%). Similarly, when AD versus VaD participants were compared, optimal area under the curve was, achieved with models that combined both command and copy features (AUC = 76.94%). Subsequent follow-up analyses of a corpus of 10 variables of interest determined using a Gini Index found that groups could be dissociated based on kinematic, time-based, and visuospatial features. Conclusion:The dCDT is able to operationally define graphomotor output that cannot be measured using traditional paper and pencil test administration in older health controls and participants with dementia. These data suggest that kinematic, time-based, and visuospatial behavior obtained using the dCDT may provide additional neurocognitive biomarkers that may be able to identify and tract dementia syndromes.

Keywords: Alzheimer’s disease, Alzheimer’s disease/vascular spectrum dementia, digital clock drawing test, executive control, kinematics, vascular dementia

DOI: 10.3233/JAD-201129

Journal: Journal of Alzheimer's Disease, vol. 82, no. 1, pp. 47-57, 2021