The relevance of hippocampal subfield integrity and clock drawing test performance for the diagnosis of Alzheimer’s disease and mild cognitive impairment

OBJECTIVE: To evaluate apraxia in healthy elderly and in patients diagnosed with Alzheimer's disease (AD) and Mild cognitive impairment (MCI). METHODS: We evaluated 136 subjects with an average age of 75.74 years (minimum 60 years old, maximum 92 years old) and average schooling of 9 years (minimum of 7 and a maximum of 12 years), using the Mini-Mental State examination (MMSE), Cambridge Cognitive Examination (CAMCOG) and the Clock Drawing Test. For the analysis of the presence of apraxia, eight subitems from the CAMCOG were selected: the drawings of the pentagon, spiral, house, clock; and the tasks of putting a piece of paper in an envelope; the correct one hand waiving "Goodbye" movements; paper cutting using scissors; and brushing teeth. RESULTS: Elder controls had an average score of 11.51, compared to MCI (11.13), and AD patients, whose average apraxia test scores were the lowest (10.23). Apraxia scores proved able to differentiate the three groups studied (p=0.001). In addition, a negative correlation was observed between apraxia and MMSE scores. CONCLUSION: We conclude that testing for the presence of apraxia is important in the evaluation of patients with cognitive impairments and may help to differentiate elderly controls, MCI and AD.

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P2-316: Neural correlates of the clock drawing test performance in patients with Alzheimer's disease: A FDG-PET study

Alzheimer s & Dementia ◽

10.1016/j.jalz.2006.05.1156 ◽

2006 ◽

Vol 2 ◽

pp. S333-S333

Author(s):

Dong Y. Lee ◽

IL H. Choo ◽

Eun H. Seo ◽

Jong C. Youn ◽

Ki W. Kim ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Test Performance ◽

Fdg Pet ◽

Neural Correlates ◽

Clock Drawing Test ◽

Clock Drawing ◽

Drawing Test

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P3-151: White matter hyperintensities within cholinergic pathways in Alzheimer's disease affecting the clock drawing test performance

Alzheimer s & Dementia ◽

10.1016/j.jalz.2008.05.1717 ◽

2008 ◽

Vol 4 ◽

pp. T564-T564

Author(s):

Ae Young Lee ◽

Ye Sung Kim ◽

Byung Hee Choi ◽

Eun Hee Sohn ◽

Jae-Moon Kim

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

White Matter ◽

Test Performance ◽

White Matter Hyperintensities ◽

Clock Drawing Test ◽

Cholinergic Pathways ◽

Clock Drawing ◽

Drawing Test

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Machine Learning Analysis of Digital Clock Drawing Test Performance for Differential Classification of Mild Cognitive Impairment Subtypes Versus Alzheimer’s Disease

Journal of the International Neuropsychological Society ◽

10.1017/s1355617720000144 ◽

2020 ◽

Vol 26 (7) ◽

pp. 690-700

Author(s):

Russell Binaco ◽

Nicholas Calzaretto ◽

Jacob Epifano ◽

Sean McGuire ◽

Muhammad Umer ◽

...

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Group Classification ◽

Clock Drawing Test ◽

Digital Clock ◽

Clock Drawing ◽

Drawing Test

AbstractObjective:To determine how well machine learning algorithms can classify mild cognitive impairment (MCI) subtypes and Alzheimer’s disease (AD) using features obtained from the digital Clock Drawing Test (dCDT).Methods:dCDT protocols were administered to 163 patients diagnosed with AD(n = 59), amnestic MCI (aMCI; n = 26), combined mixed/dysexecutive MCI (mixed/dys MCI; n = 43), and patients without MCI (non-MCI; n = 35) using standard clock drawing command and copy procedures, that is, draw the face of the clock, put in all of the numbers, and set the hands for “10 after 11.” A digital pen and custom software recorded patient’s drawings. Three hundred and fifty features were evaluated for maximum information/minimum redundancy. The best subset of features was used to train classification models to determine diagnostic accuracy.Results:Neural network employing information theoretic feature selection approaches achieved the best 2-group classification results with 10-fold cross validation accuracies at or above 83%, that is, AD versus non-MCI = 91.42%; AD versus aMCI = 91.49%; AD versus mixed/dys MCI = 84.05%; aMCI versus mixed/dys MCI = 84.11%; aMCI versus non-MCI = 83.44%; and mixed/dys MCI versus non-MCI = 85.42%. A follow-up two-group non-MCI versus all MCI patients analysis yielded comparable results (83.69%). Two-group classification analyses were achieved with 25–125 dCDT features depending on group classification. Three- and four-group analyses yielded lower but still promising levels of classification accuracy.Conclusion:Early identification of emergent neurodegenerative illness is criterial for better disease management. Applying machine learning to standard neuropsychological tests promises to be an effective first line screening method for classification of non-MCI and MCI subtypes.

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Extended Application of Digital Clock Drawing Test in the Evaluation of Alzheimer’s Disease Based on Artificial intelligence and the Neural Basis

Current Alzheimer Research ◽

10.2174/1567205018666211210150808 ◽

2021 ◽

Vol 18 ◽

Author(s):

Xiaoran Zheng ◽

Xing Wang ◽

Wei Zhang ◽

Renren Li ◽

Meng Liu ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Cognitive Domain ◽

Neural Basis ◽

Cognitive Domains ◽

Clock Drawing Test ◽

Digital Clock ◽

Clock Drawing ◽

Drawing Test

Introduction: This study aimed to build the supervised learning model to predict the state of cognitive impairment, Alzheimer’s Disease (AD) and cognitive domains including memory, language, action, and visuospatial based on Digital Clock Drawing Test (dCDT) precisely. Methods: 207 normal controls, 242 Mild Cognitive Impairment (MCI) patients, 87 dementia patients, including 53 AD patients, were selected from Shanghai Tongji Hospital. The electromagnetic tablets were used to collect the trajectory points of dCDT. By combining dynamic process and static results, different types of features were extracted, and the prediction models were built based on the feature selection approaches and machine learning methods. Results: The optimal AUC of cognitive impairment’s screening, AD’s screening and differentiation are 0.782, 0.919 and 0.818, respectively. In addition, the cognitive state of the domains with the best prediction result based on the features of dCDT is action with the optimal AUC 0.794, while the other three cognitive domains got the prediction results between 0.744-0.755. Discussion: By extracting dCDT features, cognitive impairment and AD patients can be identified early. Through dCDT feature extraction, a prediction model of single cognitive domain damage can be established.

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

Journal of Alzheimer s Disease ◽

10.3233/jad-201129 ◽

2021 ◽

Vol 82 (1) ◽

pp. 47-57 ◽

Cited By ~ 1

Author(s):

Anis Davoudi ◽

Catherine Dion ◽

Shawna Amini ◽

Patrick J. Tighe ◽

Catherine C. Price ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Vascular Dementia ◽

Area Under The Curve ◽

Clock Drawing Test ◽

Digital Clock ◽

Clock Drawing ◽

Drawing Test ◽

Dementia Patients ◽

Optimal Area

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.

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