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

Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis

BACKGROUND

With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes and assesses the key contributions and development scale of AI in dementia biomarker research.

OBJECTIVE

The aim of this study was to comprehensively evaluate the current state, hot topics, and future trends of AI in dementia biomarker research globally.

METHODS

This study thoroughly analyzed the literature in the application of AI to dementia biomarkers across various dimensions, such as publication volume, authors, institutions, journals, and countries, based on the Web of Science Core Collection. In addition, scales, trends, and potential connections between AI and biomarkers were extracted and deeply analyzed through multiple expert panels.

RESULTS

To date, the field includes 1070 publications across 362 journals, involving 74 countries and 1793 major research institutions, with a total of 6455 researchers. Notably, 69.41% (994/1432) of the researchers ceased their studies before 2019. The most prevalent algorithms used are support vector machines, random forests, and neural networks. Current research frequently focuses on biomarkers such as imaging biomarkers, cerebrospinal fluid biomarkers, genetic biomarkers, and blood biomarkers. Recent advances have highlighted significant discoveries in biomarkers related to imaging, genetics, and blood, with growth in studies on digital and ophthalmic biomarkers.

CONCLUSIONS

The field is currently in a phase of stable development, receiving widespread attention from numerous countries, institutions, and researchers worldwide. Despite this, stable clusters of collaborative research have yet to be established, and there is a pressing need to enhance interdisciplinary collaboration. Algorithm development has shown prominence, especially the application of support vector machines and neural networks in imaging studies. Looking forward, newly discovered biomarkers are expected to undergo further validation, and new types, such as digital biomarkers, will garner increased research interest and attention.

Digital assessment of cognition in neurodegenerative disease: a data driven approach leveraging artificial intelligence

Introduction

A rapid and reliable neuropsychological protocol is essential for the efficient assessment of neurocognitive constructs related to emergent neurodegenerative diseases. We developed an AI-assisted, digitally administered/scored neuropsychological protocol that can be remotely administered in ~10 min. This protocol assesses the requisite neurocognitive constructs associated with emergent neurodegenerative illnesses.

Methods

The protocol was administered to 77 ambulatory care/memory clinic patients (56.40% women; 88.50% Caucasian). The protocol includes a 6-word version of the Philadelphia (repeatable) Verbal Learning Test [P(r)VLT], three trials of 5 digits backward from the Backwards Digit Span Test (BDST), and the "animal" fluency test. The protocol provides a comprehensive set of traditional "core" measures that are typically obtained through paper-and-pencil tests (i.e., serial list learning, immediate and delayed free recall, recognition hits, percent correct serial order backward digit span, and "animal" fluency output). Additionally, the protocol includes variables that quantify errors and detail the processes used in administering the tests. It also features two separate, norm-referenced summary scores specifically designed to measure executive control and memory.

Results

Using four core measures, we used cluster analysis to classify participants into four groups: cognitively unimpaired (CU; n = 23), amnestic mild cognitive impairment (MCI; n = 17), dysexecutive MCI (n = 23), and dementia (n = 14). Subsequent analyses of error and process variables operationally defined key features of amnesia (i.e., rapid forgetting, extra-list intrusions, profligate responding to recognition foils); key features underlying reduced executive abilities (i.e., BDST items and dysexecutive errors); and the strength of the semantic association between successive responses on the "animal" fluency test. Executive and memory index scores effectively distinguished between all four groups. There was over 90% agreement between how cluster analysis of digitally obtained measures classified patients compared to classification using a traditional comprehensive neuropsychological protocol. The correlations between digitally obtained outcome variables and analogous paper/pencil measures were robust.

Discussion

The digitally administered protocol demonstrated a capacity to identify patterns of impaired performance and classification similar to those observed with standard paper/pencil neuropsychological tests. The inclusion of both core measures and detailed error/process variables suggests that this protocol can detect subtle, nuanced signs of early emergent neurodegenerative illness efficiently and comprehensively.

Dysexecutive difficulty and subtle everyday functional disabilities: the digital Trail Making Test

Background

Digital neuropsychological tests reliably capture real-time, process-based behavior that traditional paper/pencil tests cannot detect, enabling earlier detection of neurodegenerative illness. We assessed relations between informant-based subtle and mild functional decline and process-based features extracted from the digital Trail Making Test-Part B (dTMT-B).

Methods

A total of 321 community-dwelling participants (56.0% female) were assessed with the Functional Activities Questionnaire (FAQ) and the dTMT-B. Three FAQ groups were constructed: FAQ = 0 (unimpaired); FAQ = 1-4 (subtle impairment); FAQ = 5-8 (mild impairment).

Results

Compared to the FAQ-unimpaired group, other groups required longer pauses inside target circles (p < 0.050) and produced more total pen strokes to complete the test (p < 0.016). FAQ-subtle participants required more time to complete the entire test (p < 0.002) and drew individual lines connecting successive target circles slower (p < 0.001) than FAQ-unimpaired participants. Lines connecting successive circle targets were less straight among FAQ-mild, compared to FAQ-unimpaired participants (p < 0.044). Using stepwise nominal regression (reference group = FAQ-unimpaired), pauses inside target circles classified other participants into their respective groups (p < 0.015, respectively). Factor analysis using six dTMT-B variables (oblique rotation) yielded a two-factor solution related to impaired motor/cognitive operations (48.96% variance explained) and faster more efficient motor/cognitive operations (28.88% variance explained).

Conclusion

Digital assessment technology elegantly quantifies occult, nuanced behavior not previously appreciated, operationally defines critical underlying neurocognitive constructs related to functional abilities, and yields selected process-based scores that outperform traditional paper/pencil test scores for participant classification. When brought to scale, the dTMT-B test could be a sensitive tool to detect subtle-to-mild functional deficits in emergent neurodegenerative illnesses.

Investigating the associations between upper limb motor function and cognitive impairment: a scoping review

Upper limb motor function is a potential new biomarker of cognitive impairment and may aid discrimination from healthy ageing. However, it remains unclear which assessments to use. This study aimed to explore what methods have been used and to describe associations between upper limb function and cognitive impairment. A scoping review was conducted using PubMed, CINAHL and Web of Science. A systematic search was undertaken, including synonyms for key concepts 'upper limb', 'motor function' and 'cognitive impairment'. Selection criteria included tests of upper limb motor function and impaired cognition in adults. Analysis was by narrative synthesis. Sixty papers published between 1998 and 2022, comprising 41,800 participants, were included. The most common assessment tasks were finger tapping, Purdue Pegboard Test and functional tasks such as writing. Protocols were diverse in terms of equipment used and recording duration. Most participants were recruited from clinical settings. Alzheimer's Disease was the most common cause of cognitive impairment. Results were mixed but, generally, slower speed, more errors, and greater variability in upper limb movement variables was associated with cognitive impairment. This review maps the upper limb motor function assessments used and summarises the available evidence on how these associate with cognitive impairment. It identifies research gaps and may help guide protocols for future research. There is potential for upper limb motor function to be used in assessments of cognitive impairment.

Attentive pairwise interaction network for AI-assisted clock drawing test assessment of early visuospatial deficits

Dementia is a debilitating neurological condition which impairs the cognitive function and the ability to take care of oneself. The Clock Drawing Test (CDT) is widely used to detect dementia, but differentiating normal from borderline cases requires years of clinical experience. Misclassifying mild abnormal as normal will delay the chance to investigate for potential reversible causes or slow down the progression. To help address this issue, we propose an automatic CDT scoring system that adopts Attentive Pairwise Interaction Network (API-Net), a fine-grained deep learning model that is designed to distinguish visually similar images. Inspired by how humans often learn to recognize different objects by looking at two images side-by-side, API-Net is optimized using image pairs in a contrastive manner, as opposed to standard supervised learning, which optimizes a model using individual images. In this study, we extend API-Net to infer Shulman CDT scores from a dataset of 3108 subjects. We compare the performance of API-Net to that of convolutional neural networks: VGG16, ResNet-152, and DenseNet-121. The best API-Net achieves an F1-score of 0.79, which is a 3% absolute improvement over ResNet-152's F1-score of 0.76. The code for API-Net and the dataset used have been made available at https://github.com/cccnlab/CDT-API-Network .

Adherence type impacts completion rates of frequent mobile cognitive assessments among older adults with and without cognitive impairment

Background

Prior to a diagnosis of Alzheimer's disease, many individuals experience cognitive and behavioral fluctuations that are not detected during a single session of traditional neuropsychological assessment. Mobile applications now enable high-frequency cognitive data to be collected remotely, introducing new opportunities and challenges. Emerging evidence suggests cognitively impaired older adults are capable of completing mobile assessments frequently, but no study has observed whether completion rates vary by assessment frequency or adherence type.

Methods

Thirty-three older adults were recruited from the Boston University Alzheimer's Disease Research Center (mean age = 73.5 years; 27.3% cognitively impaired; 57.6% female; 81.8% White, 18.2% Black). Participants remotely downloaded and completed the DANA Brain Vital application on their own mobile devices throughout the study. The study schedule included seventeen assessments to be completed over the course of a year. Specific periods during which assessments were expected to be completed were defined as subsegments, while segments consisted of multiple subsegments. The first segment included three subsegments to be completed within one week, the second segment included weekly subsegments and spanned three weeks, and the third and fourth segments included monthly subsegments spanning five and six months, respectively. Three distinct adherence types - subsegment adherence, segment adherence, and cumulative adherence - were examined to determine how completion rates varied depending on assessment frequency and adherence type.

Results

Adherence type significantly impacted whether the completion rates declined. When utilizing subsegment adherence, the completion rate significantly declined (p = 0.05) during the fourth segment. However, when considering completion rates from the perspective of segment adherence, a decline in completion rate was not observed. Overall adherence rates increased as adherence parameters were broadened from subsegment adherence (60.6%) to segment adherence (78.8%), to cumulative adherence (90.9%).

Conclusions

Older adults, including those with cognitive impairment, are able to complete remote cognitive assessments at a high-frequency, but may not necessarily adhere to prescribed schedules.

Teleneuropsychology for vascular cognitive impairment: Which tools do we have?

The halt of clinical activities imposed during the COVID-19 pandemic forced clinicians to find alternative strategies to provide continuity of care and services, and led to a renewed interest in use of teleneuropsychology (TNP) to remotely assess patients. Recent TNP guidelines recommend maximizing the reproduction of standard in-person assessment, particularly through videoconferences. However, consistency of the adaptations of usual cognitive tests to videoconference needs further elucidation. This review aims at critical reviewing which cognitive tests could be recommended for a remote evaluation of patients with vascular cognitive impairment (VCI) among those widely recognized as reference standards. Current evidence supports the use of global cognitive efficiency (MMSE and MoCA), verbal memory (Revised Hopkins Verbal Learning Test), and language tests (phonemic and semantic verbal fluencies, Boston Naming Test), while there is a lack of strong validity support for measures of visuospatial functions (Rey-Osterreith Complex Figure), and executive functioning and processing speed (Trail making Test, and Digit symbol or Symbol digit tests). This represents a major limitation in the evaluation of VCI because its cognitive profile in often characterized by attention and executive deficits. At present, a videoconference TNP visit appears useful for a brief evaluation of global cognitive efficiency, and to 'triage' patients towards a second level in person evaluation. In future, hybrid models of TNP based on data collected across multiple modalities, incorporating both adaptation of usual cognitive tools and new computerized tools in the supervised videoconference setting, are likely to become the best option for a comprehensive remote cognitive assessment.

Explainable semi-supervised deep learning shows that dementia is associated with small, avocado-shaped clocks with irregularly placed hands

The clock drawing test is a simple and inexpensive method to screen for cognitive frailties, including dementia. In this study, we used the relevance factor variational autoencoder (RF-VAE), a deep generative neural network, to represent digitized clock drawings from multiple institutions using an optimal number of disentangled latent factors. The model identified unique constructional features of clock drawings in a completely unsupervised manner. These factors were examined by domain experts to be novel and not extensively examined in prior research. The features were informative, as they distinguished dementia from non-dementia patients with an area under receiver operating characteristic (AUC) of 0.86 singly, and 0.96 when combined with participants' demographics. The correlation network of the features depicted the "typical dementia clock" as having a small size, a non-circular or "avocado-like" shape, and incorrectly placed hands. In summary, we report a RF-VAE network whose latent space encoded novel constructional features of clocks that classify dementia from non-dementia patients with high performance.

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.

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.

Variational autoencoder provides proof of concept that compressing CDT to extremely low-dimensional space retains its ability of distinguishing dementia

The clock drawing test (CDT) is an inexpensive tool to screen for dementia. In this study, we examined if a variational autoencoder (VAE) with only two latent variables can capture and encode clock drawing anomalies from a large dataset of unannotated CDTs (n = 13,580) using self-supervised pre-training and use them to classify dementia CDTs (n = 18) from non-dementia CDTs (n = 20). The model was independently validated using a larger cohort consisting of 41 dementia and 50 non-dementia clocks. The classification model built with the parsimonious VAE latent space adequately classified dementia from non-dementia (0.78 area under receiver operating characteristics (AUROC) in the original test dataset and 0.77 AUROC in the secondary validation dataset). The VAE-identified atypical clock features were then reviewed by domain experts and compared with existing literature on clock drawing errors. This study shows that a very small number of latent variables are sufficient to encode important clock drawing anomalies that are predictive of dementia.

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.

Modeling Users' Cognitive Performance Using Digital Pen Features

Digital pen features model characteristics of sketches and user behavior, and can be used for various supervised machine learning (ML) applications, such as multi-stroke sketch recognition and user modeling. In this work, we use a state-of-the-art set of more than 170 digital pen features, which we implement and make publicly available. The feature set is evaluated in the use case of analyzing paper-pencil-based neurocognitive assessments in the medical domain. Most cognitive assessments, for dementia screening for example, are conducted with a pen on normal paper. We record these tests with a digital pen as part of a new interactive cognitive assessment tool with automatic analysis of pen input. The physician can, first, observe the sketching process in real-time on a mobile tablet, e.g., in telemedicine settings or to follow Covid-19 distancing regulations. Second, the results of an automatic test analysis are presented to the physician in real-time, thereby reducing manual scoring effort and producing objective reports. As part of our evaluation we examine how accurately different feature-based, supervised ML models can automatically score cognitive tests, with and without semantic content analysis. A series of ML-based sketch recognition experiments is conducted, evaluating 10 modern off-the-shelf ML classifiers (i.e., SVMs, Deep Learning, etc.) on a sketch data set which we recorded with 40 subjects from a geriatrics daycare clinic. In addition, an automated ML approach (AutoML) is explored for fine-tuning and optimizing classification performance on the data set, achieving superior recognition accuracies. Using standard ML techniques our feature set outperforms all previous approaches on the cognitive tests considered, i.e., the Clock Drawing Test, the Rey-Osterrieth Complex Figure Test, and the Trail Making Test, by automatically scoring cognitive tests with up to 87.5% accuracy in a binary classification task.

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.

Digitally generated Trail Making Test data: Analysis using hidden Markov modeling

The Trail Making Test (TMT) is a neuropsychological test used to assess cognitive dysfunction. The TMT consists of two parts: TMT-A requires connecting numbers 1 to 25 sequentially; TMT-B requires connecting numbers 1 to 12 and letters A to L sequentially, alternating between numbers and letters. We propose using a digitally recorded version of TMT to capture cognitive or physical functions underlying test performance. We analyzed digital versions of TMT-A and -B to derive time metrics and used Bayesian hidden Markov models to extract additional metrics. We correlated these derived metrics with cognitive and physical function scores using regression. On both TMT-A and -B, digital metrics associated with graphomotor processing test scores and gait speed. Digital metrics on TMT-B were additionally associated with episodic memory test scores and grip strength. These metrics provide additional information of cognitive state and can differentiate cognitive and physical factors affecting test performance.

The Correlation between a New Online Cognitive Test (the Brain Assessment) and Widely Used In-Person Neuropsychological Tests

INTRODUCTION

There are several problems with standard in-person neuropsychological assessments, such as habituation, necessity of human resources, and difficulty of in-person assessment under societal conditions during the outbreak of coronavirus disease 2019. Thus, we developed an online cognitive test (the Brain Assessment [BA]). In this study, we investigated the correlation between the results of the BA and those of established neuropsychological tests.

PARTICIPANTS AND METHODS

Seventy-seven elderly persons (mean 71.3 ± 5.1 years old; range 65-86; male:female = 45:32) were recruited through the internet. Correlations were evaluated between the BA and the following widely used neuropsychological tests: the mini-mental state examination (MMSE), the Raven's colored progressive matrices (RCPM), the logical memory I and II of the Rivermead Behavioral Memory Test, the word fluency (WF) test, and the Trail-Making TestA/B.

RESULTS

We found moderate correlations between the total cognitive score of the BA and the total score of the MMSE (r = 0.433, p < 0.001), as well as between the total BA score and the total RCPM score (r = 0.582, p < 0.001) and time to complete the RCPM (r = 0.455, p < 0.001). Moderate correlations were also observed between the cognitive score of the memory of words BA subtest and the LM-I (r = 0.518, p < 0.001), the mental rotation subtest and figure drawing (r = 0.404, p < 0.001), the logical reasoning subtest and total RCPM score (r = 0.491, p < 0.001), and the memory of numbers and words subtests and WF (memory of numbers and total WF: r = 0.456, p < 0.001; memory of words and total WF: r = 0.571, p < 0.001).

DISCUSSION

We found that the BA showed moderate correlations between established neuropsychological tests for intellect, memory, visuospatial function, and frontal function. The MMSE and the RCPM reflect Spearman's s-factor and g-factor, respectively, and thus the BA also covered both factors.

CONCLUSION

The BA is a useful tool for assessing the cognitive function of generally healthy elderly persons.

The New Frontier of Perioperative Cognitive Medicine for Alzheimer's Disease and Related Dementias

This is a review of preoperative cognitive assessment and other healthcare gaps in the care of older adults at risk for Alzheimer's disease and related dementias (ADRD) who have elected surgery with anesthesia. It summarizes concerns regarding ADRD perioperative healthcare, perioperative cognitive, and neuronal domains of vulnerability. It also offers a plan for phased preoperative cognitive screening and perioperative cognitive intervention opportunities. An argument is made for why medical professionals in the perioperative setting need fundamental training in cognitive-behavioral principles, an understanding of neurodegenerative diseases of aging, and an appreciation of the immediate and long-term medical risks for such patients undergoing anesthesia. The author's goal is to encourage readers to consider perioperative cognitive medicine as a new frontier for generating evidence-based care approaches for at-risk older adults with neurodegenerative disorders who require procedures with anesthesia.

Digital Neuropsychological Assessment: New Technology for Measuring Subtle Neuropsychological Behavior

Technology has transformed the science and practice of medicine. In this special mini-forum, data using digital neuropsychological technology are reported. All of these papers demonstrate how coupling digital technology with standard paper and pencil neuropsychological tests are able to extract behavior not otherwise obtainable. As digital assessment methods mature, early identification of persons with emergent neurodegenerative and other neurological illness may be possible.