Diagnostic value of digital clock drawing test in comparison with CERAD neuropsychological battery total score for discrimination of patients in the early course of Alzheimer's disease from healthy individuals

Cerebral Microbleeds in Different Brain Regions and Their Associations With the Digital Clock-Drawing Test: Secondary Analysis of the Framingham Heart Study

BACKGROUND

Cerebral microbleeds (CMB) increase the risk for Alzheimer disease. Current neuroimaging methods that are used to detect CMB are costly and not always accessible.

OBJECTIVE

This study aimed to explore whether the digital clock-drawing test (DCT) may provide a behavioral indicator of CMB.

METHODS

In 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.

RESULTS

The 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.

CONCLUSIONS

The 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.

Detection of Mild Cognitive Impairment Through Hand Motor Function Under Digital Cognitive Test: Mixed Methods Study

BACKGROUND

Early detection of cognitive impairment or dementia is essential to reduce the incidence of severe neurodegenerative diseases. However, currently available diagnostic tools for detecting mild cognitive impairment (MCI) or dementia are time-consuming, expensive, or not widely accessible. Hence, exploring more effective methods to assist clinicians in detecting MCI is necessary.

OBJECTIVE

In this study, we aimed to explore the feasibility and efficiency of assessing MCI through movement kinetics under tablet-based "drawing and dragging" tasks.

METHODS

We iteratively designed "drawing and dragging" tasks by conducting symposiums, programming, and interviews with stakeholders (neurologists, nurses, engineers, patients with MCI, healthy older adults, and caregivers). Subsequently, stroke patterns and movement kinetics were evaluated in healthy control and MCI groups by comparing 5 categories of features related to hand motor function (ie, time, stroke, frequency, score, and sequence). Finally, user experience with the overall cognitive screening system was investigated using structured questionnaires and unstructured interviews, and their suggestions were recorded.

RESULTS

The "drawing and dragging" tasks can detect MCI effectively, with an average accuracy of 85% (SD 2%). Using statistical comparison of movement kinetics, we discovered that the time- and score-based features are the most effective among all the features. Specifically, compared with the healthy control group, the MCI group showed a significant increase in the time they took for the hand to switch from one stroke to the next, with longer drawing times, slow dragging, and lower scores. In addition, patients with MCI had poorer decision-making strategies and visual perception of drawing sequence features, as evidenced by adding auxiliary information and losing more local details in the drawing. Feedback from user experience indicates that our system is user-friendly and facilitates screening for deficits in self-perception.

CONCLUSIONS

The tablet-based MCI detection system quantitatively assesses hand motor function in older adults and further elucidates the cognitive and behavioral decline phenomenon in patients with MCI. This innovative approach serves to identify and measure digital biomarkers associated with MCI or Alzheimer dementia, enabling the monitoring of changes in patients' executive function and visual perceptual abilities as the disease advances.

A Network Analysis of Digital Clock Drawing for Command and Copy Conditions

Graphomotor and time-based variables from the digital Clock Drawing Test (dCDT) characterize cognitive functions. However, no prior publications have quantified the strength of the associations between digital clock variables as they are produced. We hypothesized that analysis of the production of clock features and their interrelationships, as suggested, will differ between the command and copy test conditions. Older adults aged 65+ completed a digital clock drawing to command and copy conditions. Using a Bayesian hill-climbing algorithm and bootstrapping (10,000 samples), we derived directed acyclic graphs (DAGs) to examine network structure for command and copy dCDT variables. Although the command condition showed moderate associations between variables (μ | β z | = 0.34) relative to the copy condition (μ | β z | = 0.25), the copy condition network had more connections (18/18 versus 15/18 command). Network connectivity across command and copy was most influenced by five of the 18 variables. The direction of dependencies followed the order of instructions better in the command condition network. Digitally acquired clock variables relate to one another but differ in network structure when derived from command or copy conditions. Continued analyses of clock drawing production should improve understanding of quintessential normal features to aid in early neurodegenerative disease detection.

Digital clock drawing test reflects visuospatial ability of older drivers

Objectives

To keep older drivers safe, it is necessary to assess their fitness to drive. We developed a touch screen-based digital Clock Drawing Test (dCDT) and examined the relationship between the dCDT scores and on-road driving performance of older drivers in a community-setting.

Methods

One hundred and forty-one community-dwelling older drivers (range; 64-88 years old) who participated in this study were included in the analysis. Participants completed the dCDT, the Mini-Mental State Examination-Japanese (MMSE-J), and an on-road driving assessment. We examined the relationship between dCDT scores using the method by Rouleau et al. (maximum 10 points) and the on-road driving performance based on a driving assessment system originally developed by Nagoya University.

Results

Multiple regression analyses showed that errors in the driving test were associated with dCDT score for the items "confirmation," "turning left" and "maintains driving lane position".

Discussion

This study confirmed the relationship between the dCDT score and driving errors, such as confirmation, turning left and maintaining driving lane position. The increase in these errors indicates a decline in visuospatial ability while driving. The dCDT score may reflect older drivers' visuospatial abilities while driving.

Clock Drawing Test Performance of Young Adults Based on a One-Shot Case Study

OBJECTIVE

The clock drawing test (CDT) is being used regularly by medical professionals in a variety of settings to aid in assessing cognitive functioning in adults of all ages. As our technological environment has changed significantly, because of the inception of this measure, the use of and exposure to the analog clock have diminished. We investigated whether young adults, who have grown up in a mainly digital world, can draw and tell time on an analog clock.

METHOD

Participants aged 18-30 years (N = 80, Mage = 24.2, SD = 3.93), who self-identified as having normal cognition, completed the CDT, as well as setting hands on a pre-drawn clock and identifying analog clock times.

RESULTS

About 25% of participants received a CDT score below the expected range. There was a moderate, positive correlation between analog clock hand setting and time identification in the group who scored below the expected range on the CDT only (rs(16) = 0.472, p = .048). Most participants reported not wearing an analog watch.

CONCLUSIONS

Based on these findings, the CDT should be used with caution to screen cognitive functioning in young adults (i.e., aged 18-30 years). Consideration of an alternative approach to screening cognition and modifying cognitive assessments in which the CDT is embedded is recommended for this population. These findings warrant further investigation into CDT performance in the young adult population.

A tablet-based multi-dimensional drawing system can effectively distinguish patients with amnestic MCI from healthy individuals

The population with dementia is expected to rise to 152 million in 2050 due to the aging population worldwide. Therefore, it is significant to identify and intervene in the early stage of dementia. The Rey-Osterreth complex figure (ROCF) test is a visuospatial test scale. Its scoring methods are numerous, time-consuming, and inconsistent, which is unsuitable for wide application as required by the high number of people at risk. Therefore, there is an urgent need for a rapid, objective, and sensitive digital scoring method to detect cognitive dysfunction in the early stage accurately. This study aims to clarify the organizational strategy of aMCI patients to draw complex figures through a multi-dimensional digital evaluation system. At the same time, a rapid, objective, and sensitive digital scoring method is established to replace traditional scoring. The data of 64 subjects (38 aMCI patients and 26 NC individuals) were analyzed in this study. All subjects completed the tablet's Geriatric Complex Figure (GCF) test, including copying, 3-min recall, and 20-min delayed recall, and also underwent a standardized neuropsychological test battery and classic ROCF test. Digital GCF (dGCF) variables and conventional GCF (cGCF) scores were input into the forward stepwise logistic regression model to construct classification models. Finally, ROC curves were made to visualize the difference in the diagnostic value of dGCF variables vs. cGCF scores in categorizing the diagnostic groups. In 20-min delayed recall, aMCI patients' time in air and pause time were longer than NC individuals. Patients with aMCI had more short strokes and poorer ability of detail integration (all p < 0.05). The diagnostic sensitivity of dGCF variables for aMCI patients was 89.47%, slightly higher than cGCF scores (sensitivity: 84.21%). The diagnostic accuracy of both was comparable (dGCF: 70.3%; cGCF: 73.4%). Moreover, combining dGCF variables and cGCF scores could significantly improve the diagnostic accuracy and specificity (accuracy: 78.1%, specificity: 84.62%). At the same time, we construct the regression equations of the two models. Our study shows that dGCF equipment can quantitatively evaluate drawing performance, and its performance is comparable to the time-consuming cGCF score. The regression equation of the model we constructed can well identify patients with aMCI in clinical application. We believe this new technique can be a highly effective screening tool for patients with MCI.

A Fully Automated Mini-Mental State Examination Assessment Model Using Computer Algorithms for Cognitive Screening

Background

Rapidly growing healthcare demand associated with global population aging has spurred the development of new digital tools for the assessment of cognitive performance in older adults.

Objective

To develop a fully automated Mini-Mental State Examination (MMSE) assessment model and validate the model's rating consistency.

Methods

The Automated Assessment Model for MMSE (AAM-MMSE) was an about 10-min computerized cognitive screening tool containing the same questions as the traditional paper-based Chinese MMSE. The validity of the AAM-MMSE was assessed in term of the consistency between the AAM-MMSE rating and physician rating.

Results

A total of 427 participants were recruited for this study. The average age of these participants was 60.6 years old (ranging from 19 to 104 years old). According to the intraclass correlation coefficient (ICC), the interrater reliability between physicians and the AAM-MMSE for the full MMSE scale AAM-MMSE was high [ICC (2,1)=0.952; with its 95% CI of (0.883,0.974)]. According to the weighted kappa coefficients results the interrater agreement level for audio-related items showed high, but for items "Reading and obey", "Three-stage command", and "Writing complete sentence" were slight to fair. The AAM-MMSE rating accuracy was 87%. A Bland-Altman plot showed that the bias between the two total scores was 1.48 points with the upper and lower limits of agreement equal to 6.23 points and -3.26 points.

Conclusions

Our work offers a promising fully automated MMSE assessment system for cognitive screening with pretty good accuracy.

A Study of Assisted Screening for Alzheimer's Disease Based on Handwriting and Gait Analysis

Background

Alzheimer's disease (AD) is a progressive neurodegenerative disease that is not easily detected in the early stage. Handwriting and walking have been shown to be potential indicators of cognitive decline and are often affected by AD.

Objective

This study proposes an assisted screening framework for AD based on multimodal analysis of handwriting and gait and explores whether using a combination of multiple modalities can improve the accuracy of single modality classification.

Methods

We recruited 90 participants (38 AD patients and 52 healthy controls). The handwriting data was collected under four handwriting tasks using dot-matrix digital pens, and the gait data was collected using an electronic trail. The two kinds of features were fused as inputs for several different machine learning models (Logistic Regression, SVM, XGBoost, Adaboost, LightGBM), and the model performance was compared.

Results

The accuracy of each model ranged from 71.95% to 96.17%. Among them, the model constructed by LightGBM had the best performance, with an accuracy of 96.17%, sensitivity of 95.32%, specificity of 96.78%, PPV of 95.94%, NPV of 96.74%, and AUC of 0.991. However, the highest accuracy of a single modality was 93.53%, which was achieved by XGBoost in gait features.

Conclusions

The research results show that the combination of handwriting features and gait features can achieve better classification results than a single modality. In addition, the assisted screening model proposed in this study can achieve effective classification of AD, which has development and application prospects.

Explainable deep learning approach for extracting cognitive features from hand-drawn images of intersecting pentagons

Hand drawing, which requires multiple neural systems for planning and controlling sequential movements, is a useful cognitive test for older adults. However, the conventional visual assessment of these drawings only captures limited attributes and overlooks subtle details that could help track cognitive states. Here, we utilized a deep-learning model, PentaMind, to examine cognition-related features from hand-drawn images of intersecting pentagons. PentaMind, trained on 13,777 images from 3111 participants in three aging cohorts, explained 23.3% of the variance in the global cognitive scores, 1.92 times more than the conventional rating. This accuracy improvement was due to capturing additional drawing features associated with motor impairments and cerebrovascular pathologies. By systematically modifying the input images, we discovered several important drawing attributes for cognition, including line waviness. Our results demonstrate that deep learning models can extract novel drawing metrics to improve the assessment and monitoring of cognitive decline and dementia in older adults.

Interpretable deep learning approach for extracting cognitive features from hand-drawn images of intersecting pentagons in older adults

Hand drawing involves multiple neural systems for planning and precise control of sequential movements, making it a valuable cognitive test for older adults. However, conventional visual assessment of drawings may not capture intricate nuances that could help track cognitive states. To address this issue, we utilized a deep-learning model, PentaMind, to examine cognition-related features from hand-drawn images of intersecting pentagons. PentaMind, trained on 13,777 images from 3,111 participants in three aging cohorts, explained 23.3% of the variance in global cognitive scores, a comprehensive hour-long cognitive battery. The model’s performance, which was 1.92 times more accurate than conventional visual assessment, significantly improved the detection of cognitive decline. The improvement in accuracy was due to capturing additional drawing features that we found to be associated with motor impairments and cerebrovascular pathologies. By systematically modifying the input images, we discovered several important drawing attributes for cognition, including line waviness. Our results demonstrate that hand-drawn images can provide rich cognitive information, enabling rapid assessment of cognitive decline and suggesting potential clinical implications in dementia.

Patterns of structure-function association in normal aging and in Alzheimer's disease: Screening for mild cognitive impairment and dementia with ML regression and classification models

Background

The combined analysis of imaging and functional modalities is supposed to improve diagnostics of neurodegenerative diseases with advanced data science techniques.

Objective

To get an insight into normal and accelerated brain aging by developing the machine learning models that predict individual performance in neuropsychological and cognitive tests from brain MRI. With these models we endeavor to look for patterns of brain structure-function association (SFA) indicative of mild cognitive impairment (MCI) and Alzheimer's dementia.

Materials and methods

We explored the age-related variability of cognitive and neuropsychological test scores in normal and accelerated aging and constructed regression models predicting functional performance in cognitive tests from brain radiomics data. The models were trained on the three study cohorts from ADNI dataset-cognitively normal individuals, patients with MCI or dementia-separately. We also looked for significant correlations between cortical parcellation volumes and test scores in the cohorts to investigate neuroanatomical differences in relation to cognitive status. Finally, we worked out an approach for the classification of the examinees according to the pattern of structure-function associations into the cohorts of the cognitively normal elderly and patients with MCI or dementia.

Results

In the healthy population, the global cognitive functioning slightly changes with age. It also remains stable across the disease course in the majority of cases. In healthy adults and patients with MCI or dementia, the trendlines of performance in digit symbol substitution test and trail making test converge at the approximated point of 100 years of age. According to the SFA pattern, we distinguish three cohorts: the cognitively normal elderly, patients with MCI, and dementia. The highest accuracy is achieved with the model trained to predict the mini-mental state examination score from voxel-based morphometry data. The application of the majority voting technique to models predicting results in cognitive tests improved the classification performance up to 91.95% true positive rate for healthy participants, 86.21%-for MCI and 80.18%-for dementia cases.

Conclusion

The machine learning model, when trained on the cases of this of that group, describes a disease-specific SFA pattern. The pattern serves as a "stamp" of the disease reflected by the model.

Dissociating Statistically Determined Normal Cognitive Abilities and Mild Cognitive Impairment Subtypes with DCTclock

OBJECTIVE

To determine whether the DCTclock can detect differences across groups of patients seen in the memory clinic for suspected dementia.

METHOD

Patients (n = 123) were classified into the following groups: cognitively normal (CN), subtle cognitive impairment (SbCI), amnestic cognitive impairment (aMCI), and mixed/dysexecutive cognitive impairment (mx/dysMCI). Nine outcome variables included a combined command/copy total score and four command and four copy indices measuring drawing efficiency, simple/complex motor operations, information processing speed, and spatial reasoning.

RESULTS

Total combined command/copy score distinguished between groups in all comparisons with medium to large effects. The mx/dysMCI group had the lowest total combined command/copy scores out of all groups. The mx/dysMCI group scored lower than the CN group on all command indices (p < .050, all analyses); and lower than the SbCI group on drawing efficiency (p = .011). The aMCI group scored lower than the CN group on spatial reasoning (p = .019). Smaller effect sizes were obtained for the four copy indices.

CONCLUSIONS

These results suggest that DCTclock command/copy parameters can dissociate CN, SbCI, and MCI subtypes. The larger effect sizes for command clock indices suggest these metrics are sensitive in detecting early cognitive decline. Additional research with a larger sample is warranted.

Synergy through integration of digital cognitive tests and wearable devices for mild cognitive impairment screening

Introduction

Advances in mobile computing platforms and the rapid development of wearable devices have made possible the continuous monitoring of patients with mild cognitive impairment (MCI) and their daily activities. Such rich data can reveal more subtle changes in patients' behavioral and physiological characteristics, providing new ways to detect MCI anytime, anywhere. Therefore, we aimed to investigate the feasibility and validity of digital cognitive tests and physiological sensors applied to MCI assessment.

Methods

We collected photoplethysmography (PPG), electrodermal activity (EDA) and electroencephalogram (EEG) signals from 120 participants (61 MCI patients, 59 healthy controls) during rest and cognitive testing. The features extracted from these physiological signals involved the time domain, frequency domain, time-frequency domain and statistics. Time and score features during the cognitive test are automatically recorded by the system. In addition, selected features of all modalities were classified by tenfold cross-validation using five different classifiers.

Results

The experimental results showed that the weighted soft voting strategy combining five classifiers achieved the highest classification accuracy (88.9%), precision (89.9%), recall (88.2%), and F1 score (89.0%). Compared to healthy controls, the MCI group typically took longer to recall, draw, and drag. Moreover, during cognitive testing, MCI patients showed lower heart rate variability, higher electrodermal activity values, and stronger brain activity in the alpha and beta bands.

Discussion

It was found that patients' classification performance improved when combining features from multiple modalities compared to using only tablet parameters or physiological features, indicating that our scheme could reveal MCI-related discriminative information. Furthermore, the best classification results on the digital span test across all tasks suggest that MCI patients may have deficits in attention and short-term memory that came to the fore earlier. Finally, integrating tablet cognitive tests and wearable sensors would provide a new direction for creating an easy-to-use and at-home self-check MCI screening tool.

Differentiation of patients with mild cognitive impairment and healthy controls based on computer assisted hand movement analysis: a proof-of-concept study

Mild cognitive impairment (MCI) is the prodromal phase of dementia, and it is highly underdiagnosed in the community. We aimed to develop an automated, rapid (< 5 min), electronic screening tool for the recognition of MCI based on hand movement analysis. Sixty-eight individuals participated in our study, 46 healthy controls and 22 patients with clinically defined MCI. All participants underwent a detailed medical assessment including neuropsychology and brain MRI. Significant differences were found between controls and MCI groups in mouse movement characteristics. Patients showed higher level of entropy for both the left (F = 5.24; p = 0.001) and the right hand (F = 8.46; p < 0.001). Longer time was required in MCI to perform the fine motor task (p < 0.005). Furthermore, we also found significant correlations between mouse movement parameters and neuropsychological test scores. Correlation was the strongest between motor parameters and Clinical Dementia Rating scale (CDR) score (average r: - 0.36, all p's < 0.001). Importantly, motor parameters were not influenced by age, gender, or anxiety effect (all p's > 0.05). Our study draws attention to the utility of hand movement analysis, especially to the estimation of entropy in the early recognition of MCI. It also suggests that our system might provide a promising tool for the cognitive screening of large populations.

Characteristics of Drawing Process Differentiate Alzheimer’s Disease and Dementia with Lewy Bodies

Background: Early differential diagnosis of Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) is important for treatment and disease management, but it remains challenging. Although computer-based drawing analysis may help differentiate AD and DLB, it has not been extensively studied. Objective: We aimed to identify the differences in features characterizing the drawing process between AD, DLB, and cognitively normal (CN) individuals, and to evaluate the validity of using these features to identify and differentiate AD and DLB. Methods: We collected drawing data with a digitizing tablet and pen from 123 community-dwelling older adults in three clinical diagnostic groups of mild cognitive impairment or dementia due to AD (n = 47) or Lewy body disease (LBD; n = 27), and CN (n = 49), matched for their age, sex, and years of education. We then investigated drawing features in terms of the drawing speed, pressure, and pauses. Results: Reduced speed and reduced smoothness in speed and pressure were observed particularly in the LBD group, while increased pauses and total durations were observed in both the AD and LBD groups. Machine-learning models using these features achieved an area under the receiver operating characteristic curve (AUC) of 0.80 for AD versus CN, 0.88 for LBD versus CN, and 0.77 for AD versus LBD. Conclusion: Our results indicate how different types of drawing features were particularly discriminative between the diagnostic groups, and how the combination of these features can facilitate the identification and differentiation of AD and DLB.

Digital Cognitive Biomarker for Mild Cognitive Impairments and Dementia: A Systematic Review

The dementia population is increasing as the world's population is growing older. The current systematic review aims to identify digital cognitive biomarkers from computerized tests for detecting dementia and its risk state of mild cognitive impairment (MCI), and to evaluate the diagnostic performance of digital cognitive biomarkers. A literature search was performed in three databases, and supplemented by a Google search for names of previously identified computerized tests. Computerized tests were categorized into five types, including memory tests, test batteries, other single/multiple cognitive tests, handwriting/drawing tests, and daily living tasks and serious games. Results showed that 78 studies were eligible. Around 90% of the included studies were rated as high quality based on the Newcastle-Ottawa Scale (NOS). Most of the digital cognitive biomarkers achieved comparable or even better diagnostic performance than traditional paper-and-pencil tests. Moderate to large group differences were consistently observed in cognitive outcomes related to memory and executive functions, as well as some novel outcomes measured by handwriting/drawing tests, daily living tasks, and serious games. These outcomes have the potential to be sensitive digital cognitive biomarkers for MCI and dementia. Therefore, digital cognitive biomarkers can be a sensitive and promising clinical tool for detecting MCI and dementia.

The Boston Process Approach and Digital Neuropsychological Assessment: Past Research and Future Directions

Neuropsychological assessment using the Boston Process Approach (BPA) suggests that an analysis of the strategy or the process by which tasks and neuropsychological tests are completed, and the errors made during test completion convey much information regarding underlying brain and cognition and are as important as overall summary scores. Research over the last several decades employing an analysis of process and errors has been able to dissociate between dementia patients diagnosed with Alzheimer's disease, vascular dementia associated with MRI-determined white matter alterations, and Parkinson's disease; and between mild cognitive impairment subtypes. Nonetheless, BPA methods can be labor intensive to deploy. However, the recent availability of digital platforms for neuropsychological test administration and scoring now enables reliable, rapid, and objective data collection. Further, digital technology can quantify highly nuanced data previously unobtainable to define neurocognitive constructs with high accuracy. In this paper, a brief review of the BPA is provided. Studies that demonstrate how digital technology translates BPA into specific neurocognitive constructs using the Clock Drawing Test, Backward Digit Span Test, and a Digital Pointing Span Test are described. Implications for using data driven artificial intelligence-supported analytic approaches enabling the creation of more sensitive and specific detection/diagnostic algorithms for putative neurodegenerative illness are also discussed.

Automated Early Detection of Alzheimer's Disease by Capturing Impairments in Multiple Cognitive Domains with Multiple Drawing Tasks

BACKGROUND

Automatic analysis of the drawing process using a digital tablet and pen has been applied to successfully detect Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, most studies focused on analyzing individual drawing tasks separately, and the question of how a combination of drawing tasks could improve the detection performance thus remains unexplored.

OBJECTIVE

We aimed to investigate whether analysis of the drawing process in multiple drawing tasks could capture different, complementary aspects of cognitive impairments, with a view toward combining multiple tasks to effectively improve the detection capability.

METHODS

We collected drawing data from 144 community-dwelling older adults (27 AD, 65 MCI, and 52 cognitively normal, or CN) who performed five drawing tasks. We then extracted motion- and pause-related drawing features for each task and investigated the statistical associations of the features with the participants' diagnostic statuses and cognitive measures.

RESULTS

The drawing features showed gradual changes from CN to MCI and then to AD, and the changes in the features for each task were statistically associated with cognitive impairments in different domains. For classification into the three diagnostic categories, a machine learning model using the features from all five tasks achieved a classification accuracy of 75.2%, an improvement by 7.8% over that of the best single-task model.

CONCLUSION

Our results demonstrate that a common set of drawing features from multiple drawing tasks can capture different, complementary aspects of cognitive impairments, which may lead to a scalable way to improve the automated detection of AD and MCI.

Automated Analysis of Drawing Process to Estimate Global Cognition in Older Adults: Preliminary International Validation on the US and Japan Data Sets

BACKGROUND

With the aging of populations worldwide, early detection of cognitive impairments has become a research and clinical priority, particularly to enable preventive intervention for dementia. Automated analysis of the drawing process has been studied as a promising means for lightweight, self-administered cognitive assessment. However, this approach has not been sufficiently tested for its applicability across populations.

OBJECTIVE

The aim of this study was to evaluate the applicability of automated analysis of the drawing process for estimating global cognition in community-dwelling older adults across populations in different nations.

METHODS

We collected drawing data with a digital tablet, along with Montreal Cognitive Assessment (MoCA) scores for assessment of global cognition, from 92 community-dwelling older adults in the United States and Japan. We automatically extracted 6 drawing features that characterize the drawing process in terms of the drawing speed, pauses between drawings, pen pressure, and pen inclinations. We then investigated the association between the drawing features and MoCA scores through correlation and machine learning-based regression analyses.

RESULTS

We found that, with low MoCA scores, there tended to be higher variability in the drawing speed, a higher pause:drawing duration ratio, and lower variability in the pen's horizontal inclination in both the US and Japan data sets. A machine learning model that used drawing features to estimate MoCA scores demonstrated its capability to generalize from the US dataset to the Japan dataset (R2=0.35; permutation test, P<.001).

CONCLUSIONS

This study presents initial empirical evidence of the capability of automated analysis of the drawing process as an estimator of global cognition that is applicable across populations. Our results suggest that such automated analysis may enable the development of a practical tool for international use in self-administered, automated cognitive assessment.

Comparing drawing under instructions with image copying for mild cognitive impairment (MCI) or dementia screening: a meta-analysis of 92 diagnostic studies

OBJECTIVES

Drawing is a major component of cognitive screening for dementia. It can be performed without language restriction. Drawing pictures under instructions and copying images are different screening approaches. The objective of this study was to compare the diagnostic performance between drawing under instructions and image copying for MCI and dementia screening.

METHOD

A literature search was carried out in the OVID databases with keywords related to drawing for cognitive screening. Study quality and risk of bias were assessed by QUADAS-2. The level of diagnostic accuracy across different drawing tests was pooled by bivariate analysis in a random effects model. The area under the hierarchical summary receiver-operating characteristic curve (AUC) was constructed to summarize the diagnostic performance.

RESULTS

Ninety-two studies with sample size of 22,085 were included. The pooled results for drawing under instructions showed a sensitivity of 79% (95% CI: 76 - 83%) and a specificity of 80% (95% CI: 77 - 83%) with AUC of 0.87 (95% CI: 0.83 - 0.89). The pooled results for image copying showed a sensitivity of 71% (95% CI: 62 - 79%) and a specificity of 83% (95% CI: 72 - 90%) with AUC of 0.83 (95% CI: 0.80 - 0.86). Clock-drawing test was the screening test used in the majority of studies.

CONCLUSION

Drawing under instructions showed a similar diagnostic performance when compared with image copying for cognitive screening and the administration of image copying is relatively simpler. Self-screening for dementia is feasible to be done at home in the near future.

Associations Between the Digital Clock Drawing Test and Brain Volume: Large Community-Based Prospective Cohort (Framingham Heart Study)

BACKGROUND

The digital Clock Drawing Test (dCDT) has been recently used as a more objective tool to assess cognition. However, the association between digitally obtained clock drawing features and structural neuroimaging measures has not been assessed in large population-based studies.

OBJECTIVE

We aimed to investigate the association between dCDT features and brain volume.

METHODS

This study included participants from the Framingham Heart Study who had both a dCDT and magnetic resonance imaging (MRI) scan, and were free of dementia or stroke. Linear regression models were used to assess the association between 18 dCDT composite scores (derived from 105 dCDT raw features) and brain MRI measures, including total cerebral brain volume (TCBV), cerebral white matter volume, cerebral gray matter volume, hippocampal volume, and white matter hyperintensity (WMH) volume. Classification models were also built from clinical risk factors, dCDT composite scores, and MRI measures to distinguish people with mild cognitive impairment (MCI) from those whose cognition was intact.

RESULTS

A total of 1656 participants were included in this study (mean age 61 years, SD 13 years; 50.9% women), with 23 participants diagnosed with MCI. All dCDT composite scores were associated with TCBV after adjusting for multiple testing (P value <.05/18). Eleven dCDT composite scores were associated with cerebral white matter volume, but only 1 dCDT composite score was associated with cerebral gray matter volume. None of the dCDT composite scores was associated with hippocampal volume or WMH volume. The classification model for differentiating MCI and normal cognition participants, which incorporated age, sex, education, MRI measures, and dCDT composite scores, showed an area under the curve of 0.897.

CONCLUSIONS

dCDT composite scores were significantly associated with multiple brain MRI measures in a large community-based cohort. The dCDT has the potential to be used as a cognitive assessment tool in the clinical diagnosis of MCI.

Diagnostic performance and neural basis of the combination of free- and pre-drawn Clock Drawing Test

OBJECTIVES

This study aimed to clarify the diagnostic performance and neural basis of the Clock Drawing Test (CDT) combining free- and pre-drawn methods.

METHODS

This retrospective study included 165 participants (91 with Alzheimer disease [AD], 52 with amnestic mild cognitive impairment [aMCI], and 22 healthy controls [HC]), who were divided into four groups according to their free- and pre-drawn CDT scores: group 1, could do both; group 2, impaired in both; group 3, impaired in pre-drawn CDT; and group 4, impaired in free-drawn CDT. The diagnostic performances of the free-drawn, pre-drawn, and combination methods were compared using receiver operating characteristics analysis; in voxel-based morphometry analysis, the gray matter (GM) volume of groups 2-4 were compared with that of group 1.

RESULTS

The area under the curve of the combination method was greater than that of the free- or pre-drawn method alone when comparing AD with HC or aMCI. Group 2 had a significantly smaller GM volume in the bilateral temporal lobes than group 1. Group 3 had a trend toward smaller GM volumes in the right temporal lobe when a liberal threshold was applied. Group 4 had significantly smaller GM volumes in the left temporal lobe than group 1.

CONCLUSIONS

This study suggests that the combination method may be able to screen for a wider range of brain dysfunction. Combined use of free- and pre-drawn CDT may be useful for screening for AD and its early detection and treatment.

Digital clock drawing test metrics in older patients before and after endoscopy with sedation: An exploratory analysis

BACKGROUND

In the postoperative period, clinically feasible instruments to monitor elderly patients' neurocognitive recovery and discharge-readiness, especially after short-stay procedures, are limited. Cognitive monitoring may be improved by a novel digital clock drawing test (dCDT). We screened for cognitive impairment with the 4 A Test (4AT) and then administered the dCDT pre and post short-stay procedure (endoscopy). The primary aim was to investigate whether the dCDT was sensitive to a change in cognitive status postendoscopy. We also investigated if preoperative cognitive status impacted postendoscopy dCDT variables.

METHODS

We recruited 100 patients ≥65 years presenting for endoscopy day procedures at a single metropolitan hospital. Participants were assessed after admission and immediately before discharge from the hospital. We administered the 4AT, followed by both command and copy clock conditions of the dCDT. We analysed the total drawing time (dCDT time), as well as scored the drawn clock against the established Montreal Cognitive Assessment (MoCA) criteria both before and after endoscopy.

RESULTS

Linear regression showed higher 4AT test scores (poorer performance) were associated with longer postoperative dCDT time (β = 5.6, p = 0.012) for the command condition after adjusting for preoperative baseline dCDT metrics, sex, age, and years of education.

CONCLUSION

Postoperative dCDT time-based variables slowed in those with baseline cognitive impairment detected by the 4AT, but not for those without cognitive impairment. Our results suggest the dCDT, using the command mode, may help detect cognitive impairment in patients aged >65 years after elective endoscopy.

Diagnostic performance of digital cognitive tests for the identification of MCI and dementia: A systematic review

BACKGROUND

The use of digital cognitive tests is getting common nowadays. Older adults or their family members may use online tests for self-screening of dementia. However, the diagnostic performance across different digital tests is still to clarify. The objective of this study was to evaluate the diagnostic performance of digital cognitive tests for MCI and dementia in older adults.

METHODS

Literature searches were systematically performed in the OVID databases. Validation studies that reported the diagnostic performance of a digital cognitive test for MCI or dementia were included. The main outcome was the diagnostic performance of the digital test for the detection of MCI or dementia.

RESULTS

A total of 56 studies with 46 digital cognitive tests were included in this study. Most of the digital cognitive tests were shown to have comparable diagnostic performances with the paper-and-pencil tests. Twenty-two digital cognitive tests showed a good diagnostic performance for dementia, with a sensitivity and a specificity over 0.80, such as the Computerized Visuo-Spatial Memory test and Self-Administered Tasks Uncovering Risk of Neurodegeneration. Eleven digital cognitive tests showed a good diagnostic performance for MCI such as the Brain Health Assessment. However, all the digital tests only had a few validation studies to verify their performance.

CONCLUSIONS

Digital cognitive tests showed good performances for MCI and dementia. The digital test can collect digital data that is far beyond the traditional ways of cognitive tests. Future research is suggested on these new forms of cognitive data for the early detection of MCI and dementia.

A Paradox in Digital Memory Assessment: Increased Sensitivity With Reduced Difficulty

The One Card Learning Test (OCL80) from the Cogstate Brief Battery-a digital cognitive test used both in-person and remotely in clinical trials and in healthcare contexts to inform health decisions-has shown high sensitivity to changes in memory in early Alzheimer's disease (AD). However, recent studies suggest that OCL sensitivity to memory impairment in symptomatic AD is not as strong as that for other standardized assessments of memory. This study aimed to improve the sensitivity of the OCL80 to AD-related memory impairment by reducing the test difficultly (i.e., OCL48). Experiment 1 showed performance in healthy adults improved on the OCL48 while the pattern separation operations that constrain performance on the OCL80 were retained. Experiment 2 showed repeated administration of the OCL48 at short retest intervals did not induce ceiling or practice effects. Experiment 3 showed that the sensitivity of the OCL48 to AD-related memory impairment (Glass's Δ = 3.11) was much greater than the sensitivity of the OCL80 (Glass's Δ = 1.94). Experiment 4 used data from a large group of cognitively normal older adults to calibrate performance scores between the OCL80 and OCL48 using equipercentile equating. Together these results showed the OCL48 to be a valid and reliable test of learning with greater sensitivity to memory impairment in AD than the OCL80.

Evaluation of Digital Drawing Tests and Paper-and-Pencil Drawing Tests for the Screening of Mild Cognitive Impairment and Dementia: A Systematic Review and Meta-analysis of Diagnostic Studies

Digital drawing tests have been proposed for cognitive screening over the past decade. However, the diagnostic performance is still to clarify. The objective of this study was to evaluate the diagnostic performance among different types of digital and paper-and-pencil drawing tests in the screening of mild cognitive impairment (MCI) and dementia. Diagnostic studies evaluating digital or paper-and-pencil drawing tests for the screening of MCI or dementia were identified from OVID databases, included Embase, MEDLINE, CINAHL, and PsycINFO. Studies evaluated any type of drawing tests for the screening of MCI or dementia and compared with healthy controls. This study was performed according to PRISMA and the guidelines proposed by the Cochrane Diagnostic Test Accuracy Working Group. A bivariate random-effects model was used to compare the diagnostic performance of these drawing tests and presented with a summary receiver-operating characteristic curve. The primary outcome was the diagnostic performance of clock drawing test (CDT). Other types of drawing tests were the secondary outcomes. A total of 90 studies with 22,567 participants were included. In the screening of MCI, the pooled sensitivity and specificity of the digital CDT was 0.86 (95% CI = 0.75 to 0.92) and 0.92 (95% CI = 0.69 to 0.98), respectively. For the paper-and-pencil CDT, the pooled sensitivity and specificity of brief scoring method was 0.63 (95% CI = 0.49 to 0.75) and 0.77 (95% CI = 0.68 to 0.84), and detailed scoring method was 0.63 (95% CI = 0.56 to 0.71) and 0.72 (95% CI = 0.65 to 0.78). In the screening of dementia, the pooled sensitivity and specificity of the digital CDT was 0.83 (95% CI = 0.72 to 0.90) and 0.87 (95% CI = 0.79 to 0.92). The performances of the digital and paper-and-pencil pentagon drawing tests were comparable in the screening of dementia. The digital CDT demonstrated better diagnostic performance than paper-and-pencil CDT for MCI. Other types of digital drawing tests showed comparable performance with paper-and-pencil formats. Therefore, digital drawing tests can be used as an alternative tool for the screening of MCI and dementia.

Overview of the Complex Figure Test and Its Clinical Application in Neuropsychiatric Disorders, Including Copying and Recall

The Rey–Osterrieth Complex Figure (ROCF) test is a commonly used neuropsychological assessment tool. It is widely used to assess the visuo-constructional ability and visual memory of neuropsychiatric disorders, including copying and recall tests. By drawing the complex figure, the functional decline of a patient in multiple cognitive dimensions can be assessed, including attention and concentration, fine-motor coordination, visuospatial perception, non-verbal memory, planning and organization, and spatial orientation. This review first describes the different versions and scoring methods of ROCF. It then reviews the application of ROCF in the assessment of visuo-constructional ability in patients with dementia, other brain diseases, and psychiatric disorders. Finally, based on the scoring method of the digital system, future research hopes to develop a new digital ROCF scoring method combined with machine learning algorithms to standardize clinical practice and explore the characteristic neuropsychological structure information of different disorders.

Automated scoring for a Tablet-based Rey Figure copy task differentiates constructional, organisational, and motor abilities

Accuracy in copying a figure is one of the most sensitive measures of visuo-constructional ability. However, drawing tasks also involve other cognitive and motor abilities, which may influence the final graphic produced. Nevertheless, these aspects are not taken into account in conventional scoring methodologies. In this study, we have implemented a novel Tablet-based assessment, acquiring data and information for the entire execution of the Rey Complex Figure copy task (T-RCF). This system extracts 12 indices capturing various dimensions of drawing abilities. We have also analysed the structure of relationships between these indices and provided insights into the constructs that they capture. 102 healthy adults completed the T-RCF. A subgroup of 35 participants also completed a paper-and-pencil drawing battery from which constructional, procedural, and motor measures were obtained. Principal component analysis of the T-RCF indices was performed, identifying spatial, procedural and kinematic components as distinct dimensions of drawing execution. Accordingly, a composite score for each dimension was determined. Correlational analyses provided indications of their validity by showing that spatial, procedural, and kinematic scores were associated with constructional, organisational and motor measures of drawing, respectively. Importantly, final copy accuracy was found to be associated with all of these aspects of drawing. In conclusion, copying complex figures entails an interplay of multiple functions. T-RCF provides a unique opportunity to analyse the entire drawing process and to extract scores for three critical dimensions of drawing execution.

Assessment of dementia in a clinical sample of persons with intellectual disability

BACKGROUND

Assessment of age-associated disorders has become increasingly important.

METHODS

In a clinical setting, people with intellectual disability with and without dementia were assessed retrospectively using the Neuropsychological Test Battery (NTB) and the Dementia Questionnaire for People with Learning Disabilities (DLD) at two different times to analyse neuropsychological changes and diagnostic validity. One group (n = 44) was assessed with both instruments, while the DLD was applied in 71 patients.

RESULTS

In the NTB (n = 44), only patients with dementia (n = 26) showed a decline in the NTB total score and three subscales. Receiver operating characteristic analysis revealed a diagnostic sensitivity of .67, a specificity of .81, and an area under the curve (AUC) of .767. In the DLD group (n = 71), only those with dementia displayed a decrease in the cognitive and social scale; diagnostic sensitivity and specificity values were low (.61/.63) and the AUC was .704.

CONCLUSIONS

Neuropsychological assessment was sensitive to detect cognitive changes over time. Sensitivity values of both instruments suggest a reassessment at a later time point.

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

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.

Association Between the Digital Clock Drawing Test and Neuropsychological Test Performance: Large Community-Based Prospective Cohort (Framingham Heart Study)

BACKGROUND

The Clock Drawing Test (CDT) has been widely used in clinic for cognitive assessment. Recently, a digital Clock Drawing Text (dCDT) that is able to capture the entire sequence of clock drawing behaviors was introduced. While a variety of domain-specific features can be derived from the dCDT, it has not yet been evaluated in a large community-based population whether the features derived from the dCDT correlate with cognitive function.

OBJECTIVE

We aimed to investigate the association between dCDT features and cognitive performance across multiple domains.

METHODS

Participants from the Framingham Heart Study, a large community-based cohort with longitudinal cognitive surveillance, who did not have dementia were included. Participants were administered both the dCDT and a standard protocol of neuropsychological tests that measured a wide range of cognitive functions. A total of 105 features were derived from the dCDT, and their associations with 18 neuropsychological tests were assessed with linear regression models adjusted for age and sex. Associations between a composite score from dCDT features were also assessed for associations with each neuropsychological test and cognitive status (clinically diagnosed mild cognitive impairment compared to normal cognition).

RESULTS

The study included 2062 participants (age: mean 62, SD 13 years, 51.6% women), among whom 36 were diagnosed with mild cognitive impairment. Each neuropsychological test was associated with an average of 50 dCDT features. The composite scores derived from dCDT features were significantly associated with both neuropsychological tests and mild cognitive impairment.

CONCLUSIONS

The dCDT can potentially be used as a tool for cognitive assessment in large community-based populations.

Use of the Clock Drawing Test and the Rey-Osterrieth Complex Figure Test-copy with convolutional neural networks to predict cognitive impairment

BACKGROUND

The Clock Drawing Test (CDT) and Rey-Osterrieth Complex Figure Test (RCFT) are widely used as a part of neuropsychological test batteries to assess cognitive function. Our objective was to confirm the prediction accuracies of the RCFT-copy and CDT for cognitive impairment (CI) using convolutional neural network algorithms as a screening tool.

METHODS

The CDT and RCFT-copy data were obtained from patients aged 60-80 years who had more than 6 years of education. In total, 747 CDT and 980 RCFT-copy figures were utilized. Convolutional neural network algorithms using TensorFlow (ver. 2.3.0) on the Colab cloud platform ( www.colab.

RESEARCH

google.com ) were used for preprocessing and modeling. We measured the prediction accuracy of each drawing test 10 times using this dataset with the following classes: normal cognition (NC) vs. mildly impaired cognition (MI), NC vs. severely impaired cognition (SI), and NC vs. CI (MI + SI).

RESULTS

The accuracy of the CDT was better for differentiating MI (CDT, 78.04 ± 2.75; RCFT-copy, not being trained) and SI from NC (CDT, 91.45 ± 0.83; RCFT-copy, 90.27 ± 1.52); however, the RCFT-copy was better at predicting CI (CDT, 77.37 ± 1.77; RCFT, 83.52 ± 1.41). The accuracy for a 3-way classification (NC vs. MI vs. SI) was approximately 71% for both tests; no significant difference was found between them.

CONCLUSIONS

The two drawing tests showed good performance for predicting severe impairment of cognition; however, a drawing test alone is not enough to predict overall CI. There are some limitations to our study: the sample size was small, all the participants did not perform both the CDT and RCFT-copy, and only the copy condition of the RCFT was used. Algorithms involving memory performance and longitudinal changes are worth future exploration. These results may contribute to improved home-based healthcare delivery.

A novel digital clock drawing test as a screening tool for perioperative neurocognitive disorders: A feasibility study

BACKGROUND

We developed a digital clock drawing test (dCDT), an adaptation of the original pen and paper clock test, that may be advantageous over previous dCDTs in the perioperative environment. We trialed our dCDT on a tablet device in the preoperative period to determine the feasibility of administration in this setting. To assess the clinical utility of this test, we examined the relationship between the performance on the test and compared derived digital clock measures with the 4 A's Test (4AT), a delirium and cognition screening tool.

METHODS

We recruited a sample of 102 adults aged 65 years and over presenting for elective surgery in a single tertiary hospital. Participants completed the 4AT, followed by both command and copy clock conditions of the dCDT. We recorded time-based clock-drawing metrics, alongside clock replications scored using the Montreal Cognitive Assessment (MoCA) clock scoring criteria.

RESULTS

The dCDT had an acceptance rate of 99%. After controlling for demographic variables and prior tablet use, regression analyses showed higher 4AT scores were associated with greater dCDT time (seconds) for both command (β = 8.2, P = .020) and copy clocks (β = 12, P = .005) and lower MoCA-based clock scores in both command (OR = 0.19, P = .001) and copy conditions (OR = 0.14, P = .012).

CONCLUSION

The digital clock drawing test is feasible to administer and is highly acceptable to older adults in a preoperative setting. We demonstrated a significant association between both the dCDT time and clock score metrics, with the established 4AT. Our results provide convergent validity of the dCDT in the preoperative setting.

Association of Digital Clock Drawing With PET Amyloid and Tau Pathology in Normal Older Adults

OBJECTIVE

To determine whether a digital clock-drawing test, DCTclock, improves upon standard cognitive assessments for discriminating diagnostic groups and for detecting biomarker evidence of amyloid and tau pathology in clinically normal older adults (CN).

METHODS

Participants from the Harvard Aging Brain Study and the PET laboratory at Massachusetts General Hospital were recruited to undergo the DCTclock, standard neuropsychological assessments including the Preclinical Alzheimer Cognitive Composite (PACC), and amyloid/tau PET imaging. Receiver operating curve analyses were used to assess diagnostic and biomarker discriminability. Logistic regression and partial correlations were used to assess DCTclock performance in relation to PACC and PET biomarkers.

RESULTS

A total of 300 participants were studied. Among the 264 CN participants, 143 had amyloid and tau PET imaging (Clinical Dementia Rating [CDR] 0, Mini-Mental State Examination [MMSE] 28.9 ± 1.2). An additional 36 participants with a diagnosis of mild cognitive impairment or early Alzheimer dementia (CDR 0.5, MMSE 25.2 ± 3.9) were added to assess diagnostic discriminability. DCTclock showed excellent discrimination between diagnostic groups (area under the receiver operating characteristic curve 0.86). Among CN participants with biomarkers, the DCTclock summary score and spatial reasoning subscores were associated with greater amyloid and tau burden and showed better discrimination (Cohen d = 0.76) between Aβ± groups than the PACC (d = 0.30).

CONCLUSION

DCTclock discriminates between diagnostic groups and improves upon traditional cognitive tests for detecting biomarkers of amyloid and tau pathology in CN older adults. The validation of such digitized measures has the potential of providing an efficient tool for detecting early cognitive changes along the AD trajectory.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that DCTclock results were associated with amyloid and tau burden in CN older adults.

Combining Multimodal Behavioral Data of Gait, Speech, and Drawing for Classification of Alzheimer's Disease and Mild Cognitive Impairment

BACKGROUND

Gait, speech, and drawing behaviors have been shown to be sensitive to the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, previous studies focused on only analyzing individual behavioral modalities, although these studies suggested that each of these modalities may capture different profiles of cognitive impairments associated with AD.

OBJECTIVE

We aimed to investigate if combining behavioral data of gait, speech, and drawing can improve classification performance compared with the use of individual modality and if each of these behavioral data can be associated with different cognitive and clinical measures for the diagnosis of AD and MCI.

METHODS

Behavioral data of gait, speech, and drawing were acquired from 118 AD, MCI, and cognitively normal (CN) participants.

RESULTS

Combining all three behavioral modalities achieved 93.0% accuracy for classifying AD, MCI, and CN, and only 81.9% when using the best individual behavioral modality. Each of these behavioral modalities was statistically significantly associated with different cognitive and clinical measures for diagnosing AD and MCI.

CONCLUSION

Our findings indicate that these behaviors provide different and complementary information about cognitive impairments such that classification of AD and MCI is superior to using either in isolation.

Normative References for Graphomotor and Latency Digital Clock Drawing Metrics for Adults Age 55 and Older: Operationalizing the Production of a Normal Appearing Clock

BACKGROUND

Relative to the abundance of publications on dementia and clock drawing, there is limited literature operationalizing 'normal' clock production.

OBJECTIVE

To operationalize subtle behavioral patterns seen in normal digital clock drawing to command and copy conditions.

METHODS

From two research cohorts of cognitively-well participants age 55 plus who completed digital clock drawing to command and copy conditions (n = 430), we examined variables operationalizing clock face construction, digit placement, clock hand construction, and a variety of time-based, latency measures. Data are stratified by age, education, handedness, and number anchoring.

RESULTS

Normative data are provided in supplementary tables. Typical errors reported in clock research with dementia were largely absent. Adults age 55 plus produce symmetric clock faces with one stroke, with minimal overshoot and digit misplacement, and hands with expected hour hand to minute hand ratio. Data suggest digitally acquired graphomotor and latency differences based on handedness, age, education, and anchoring.

CONCLUSION

Data provide useful benchmarks from which to assess digital clock drawing performance in Alzheimer's disease and related dementias.

Reaction time and response inhibition in autosomal dominant Alzheimer's disease

OBJECTIVE

Subtle deficits in several cognitive domains characterize the neuropsychological profile of preclinical Alzheimer's disease (AD). Assessment of preclinical individuals with genes causing autosomal dominant AD (ADAD) provides a model for prodromal disease. We sought to sensitively evaluate attention and working memory using a computerized battery in non-demented persons carrying ADAD mutations.

METHOD

A total of 71 non-demented Latinos at-risk for ADAD mutations were recruited [40 mutation carriers (MCs), 31 non-mutation carriers (NCs)] and completed a Spanish language chronometric battery of speeded decision and working memory tasks.

RESULTS

On two complex reaction time (RT) tasks involving decision-making and response inhibition, MCs exhibited slower RTs than NCs as they approached their anticipated age of dementia diagnosis. Education moderated these effects, but only in younger MCs. APOE ε4 status was not associated with age-related slowing among NCs or MCs on any of the tests.

CONCLUSIONS

Our findings indicate MCs respond more slowly as they approach the age of dementia onset on tasks with greater demands on executive function. Our results also suggest these effects were not explained by APOE ε4 status independently of ADAD mutation status. Computerized reaction time tests can provide sensitive measures of the earliest cognitive changes in AD.

Detection of Cognitive Dysfunction in Elderly with a Low Educational Level Using a Reaction-Time Attention Task

Background: Scales for cognitive deterioration usually depend on education level. Objective: We aimed to study the clinical utility of a culture-free Go/No-Go task in a multi-ethnic cohort with low education level. Methods: Sixty-four participants with less than 4 years of formal education were included and divided on the basis of their Clinical-Dementia-Rate scores (CDR) into cognitively unimpaired (CDR = 0), mild cognitive impairment (MCI; CDR = 0.5), and early Alzheimer’s disease (AD, CDR = 1). All underwent a 90-s Continuous Visual Attention Test. This test consisted of a 90-s Go/No-go task with 72 (80%) targets and 18 (20%) non-targets. For each participant, reaction times and intraindividual variability of reaction times of all correct target responses, as well as the number of omission and commission errors were evaluated. Coefficient of variability was calculated for each participant by dividing the standard deviation of the reaction times by the mean reaction time. A MANCOVA was performed to examine between-group differences using age and sex as covariates. Discriminate analysis was performed to find the most reliable test-variable to discriminate the three groups. Results: Commission error, intraindividual variability of reaction time, and coefficient of variability progressively worsened with increasing CDR level. Discriminant analysis demonstrated that coefficient of variability was the best discriminant factor, followed by intraindividual variability of reaction time and commission error. Conclusion: The Go/No-Go task was able to discriminate people with MCI or early AD from controls in the setting of illiteracy.

Phenotyping Cognitive Impairment using Graphomotor and Latency Features in Digital Clock Drawing Test

The Clock Drawing Test, where the participant is asked to draw a clock from memory and copy a model clock, is widely used for screening of cognitive impairment. The digital version of the clock test, the digital clock drawing test (dCDT), employs accelerometer and pressure sensors of a digital pen to capture time and pressure information from a participant's performance in a granular digital format. While visual features of the clock drawing test have previously been studied, little is known about the relationship between demographic and cognitive impairment characteristics with dCDT latency and graphomotor features. Here, we examine dCDT feature clusters with respect to sociodemographic and cognitive impairment outcomes. Our results show that the clusters are not significantly different in terms of age and gender, but did significantly differ in terms of education, Mini-Mental State Exam scores, and cognitive impairment diagnoses.This study shows that features extracted from digital clock drawings can provide important information regarding cognitive reserve and cognitive impairments.

A Comprehensive Evaluation of the Process of Copying a Complex Figure in Early- and Late-Onset Alzheimer Disease: A Quantitative Analysis of Digital Pen Data

Background

The Rey-Osterrieth Complex Figure Test (RCFT) is a neuropsychological test that is widely used to assess visual memory and visuoconstructional deficits in patients with cognitive impairment, including Alzheimer disease (AD). Patients with AD have an increased tendency for exhibiting extraordinary behaviors in the RCFT for selecting the drawing area, organizing the figure, and deciding the order of images, among other activities. However, the conventional scoring system based on pen and paper has a limited ability to reflect these detailed behaviors.

Objective

This study aims to establish a scoring system that addresses not only the spatial arrangement of the finished drawing but also the drawing process of patients with AD by using digital pen data.

Methods

A digital pen and tablet were used to copy complex figures. The stroke patterns and kinetics of normal controls (NCs) and patients with early-onset AD (EOAD) and late-onset AD (LOAD) were analyzed by comparing the pen tip trajectory, spatial arrangement, and similarity of the finished drawings.

Results

Patients with AD copied the figure in a more fragmented way with a longer pause than NCs (EOAD: P=.045; LOAD: P=.01). Patients with AD showed an increased tendency to draw the figures closer toward the target image in comparison with the NCs (EOAD: P=.005; LOAD: P=.01) Patients with AD showed the lower accuracy than NCs (EOAD: P=.004; LOAD: P=.002). Patients with EOAD and LOAD showed similar but slightly different drawing behaviors, especially in space use and in the initial stage of drawing.

Conclusions

The digitalized complex figure test evaluated copying performance quantitatively and further elucidated the patients’ ongoing process during copying. We believe that this novel approach can be used as a digital biomarker of AD. In addition, the repeatability of the test will delineate the process of executive functions and constructional organization abilities with disease progression.

Clock-drawing test in vascular mild cognitive impairment: Validity of quantitative and qualitative analyses

INTRODUCTION

The clock-drawing test (CDT) has been used as a screening tool to identify cognitive deficit in patients with dementia. However, it has not been extensively evaluated for categorizing patients with vascular mild cognitive impairment (vMCI). This study aimed to examine the discrimination of vMCI using various CDT scoring methods.

METHOD

A total of 120 vMCI patients and 119 normal control (NC) subjects were tested using three CDT quantitative scoring systems: the one from the Montreal Cognitive Assessment (MoCA) (CDT3) and the systems of Rouleau (CDT10) and Babins (CDT18). We used a revised scoring method to evaluate the effectiveness in differentiating vMCI patients from NC subjects, which combined the CDT10 quantitative score and three qualitative errors with a significantly higher prevalence in vMCI group (called hereinafter CDTcomb, including CDTcomb13 and CDTcomb16 based on different weights of the three error types). The sensitivity and specificity of the CDT methods were determined by the receiver operating characteristic (ROC) curve. The results of the scoring systems were compared with those of the Mini-Mental State Examination (MMSE).

RESULTS

The results of the ROC analyses with the CDT3, CDT10, and CDT18 systems produced a sensitivity of 71.1%, 81.8%, and 60.3%, and a specificity of 66.12%, 58.68%, and 73.55%, respectively, for the diagnosis of vMCI. Compared with the separate MMSE score, the combination of MMSE with the CDT3, CDT10 and CDT18 scores did not increase the sensitivity and specificity. When three qualitative errors were incorporated into the CDT10 quantitative score, CDTcomb13 and CDTcomb16 provided a sensitivity of 87.6% and 86.78%, and a specificity of 74.79% and 80.67%, respectively, in differentiating vMCI patients from the NC group.

CONCLUSION

Our findings suggest that the combination of CDT quantitative score with qualitative observations of the clock-drawing errors can provide a better discrimination between vMCI patients and cognitively normal subjects.

Automatic, Qualitative Scoring of the Interlocking Pentagon Drawing Test (PDT) based on U-Net and Mobile Sensor Data

We implemented a mobile phone application of the pentagon drawing test (PDT), called mPDT, with a novel, automatic, and qualitative scoring method for the application based on U-Net (a convolutional network for biomedical image segmentation) coupled with mobile sensor data obtained with the mPDT. For the scoring protocol, the U-Net was trained with 199 PDT hand-drawn images of 512 × 512 resolution obtained via the mPDT in order to generate a trained model, Deep5, for segmenting a drawn right or left pentagon. The U-Net was also trained with 199 images of 512 × 512 resolution to attain the trained model, DeepLock, for segmenting an interlocking figure. Here, the epochs were iterated until the accuracy was greater than 98% and saturated. The mobile senor data primarily consisted of x and y coordinates, timestamps, and touch-events of all the samples with a 20 ms sampling period. The velocities were then calculated using the primary sensor data. With Deep5, DeepLock, and the sensor data, four parameters were extracted. These included the number of angles (0–4 points), distance/intersection between the two drawn figures (0–4 points), closure/opening of the drawn figure contours (0–2 points), and tremors detected (0–1 points). The parameters gave a scaling of 11 points in total. The performance evaluation for the mPDT included 230 images from subjects and their associated sensor data. The results of the performance test indicated, respectively, a sensitivity, specificity, accuracy, and precision of 97.53%, 92.62%, 94.35%, and 87.78% for the number of angles parameter; 93.10%, 97.90%, 96.09%, and 96.43% for the distance/intersection parameter; 94.03%, 90.63%, 92.61%, and 93.33% for the closure/opening parameter; and 100.00%, 100.00%, 100.00%, and 100.00% for the detected tremor parameter. These results suggest that the mPDT is very robust in differentiating dementia disease subtypes and is able to contribute to clinical practice and field studies.

Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data

Background

Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data.

Methods

Multi-center data were obtained from 14,926 formal neuropsychological assessments (Seoul Neuropsychological Screening Battery), which were classified into normal cognition (NC), mild cognitive impairment (MCI) and Alzheimer’s disease dementia (ADD). We trained a machine learning model with artificial neural network algorithm using TensorFlow (https://www.tensorflow.org) to distinguish cognitive state with the 46-variable data and measured prediction accuracies from 10 randomly selected datasets. The features of the NPT were listed in order of their contribution to the outcome using Recursive Feature Elimination.

Results

The ten times mean accuracies of identifying CI (MCI and ADD) achieved by 96.66 ± 0.52% of the balanced dataset and 97.23 ± 0.32% of the clinic-based dataset, and the accuracies for predicting cognitive states (NC, MCI or ADD) were 95.49 ± 0.53 and 96.34 ± 1.03%. The sensitivity to the detection CI and MCI in the balanced dataset were 96.0 and 96.0%, and the specificity were 96.8 and 97.4%, respectively. The ‘time orientation’ and ‘3-word recall’ score of MMSE were highly ranked features in predicting CI and cognitive state. The twelve features reduced from 46 variable of NPTs with age and education had contributed to more than 90% accuracy in predicting cognitive impairment.

Conclusions

The machine learning algorithm for NPTs has suggested potential use as a reference in differentiating cognitive impairment in the clinical setting.

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Studying the effects of neurodegeneration on handwriting has emerged as an interdisciplinary research topic and has attracted considerable interest from psychologists to neuroscientists and from physicians to computer scientists. The complexity of handwriting, in fact, appears to be sensitive to age-related impairments in cognitive functioning; thus, analyzing handwriting in elderly people may facilitate the diagnosis and monitoring of these impairments. A large body of knowledge has been collected in the last thirty years thanks to the advent of new technologies which allow researchers to investigate not only the static characteristics of handwriting but also especially the dynamic aspects of the handwriting process. The present paper aims at providing an overview of the most relevant literature investigating the application of dynamic handwriting analysis in neurodegenerative disease assessment. The focus, in particular, is on Parkinon’s disease (PD) and Alzheimer’s disease (AD), as the two most widespread neurodegenerative disorders. More specifically, the studies taken into account are grouped in accordance with three main research questions: disease insight, disease monitoring, and disease diagnosis. The net result is that dynamic handwriting analysis is a powerful, noninvasive, and low-cost tool for real-time diagnosis and follow-up of PD and AD. In conclusion of the paper, open issues still demanding further research are highlighted.