The MemTrax Test Compared to the Montreal Cognitive Assessment Estimation of Mild Cognitive Impairment

Hoek, Marjanne D. van der; Nieuwenhuizen, Arie G.; Keijer, Jaap; Ashford, J. Wesson

doi:10.3233/jad-181003

Cited by 28 publications

(35 citation statements)

References 49 publications

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“…Within this progression, the data from this study suggest that this highly precise measure of EM function, could detect very early changes associated with AD potentially 10 years before the diagnosis of dementia, and possibly even much earlier, particularly with multiple testing repeated over an extended period. AD is a condition that develops across the full life-span of an individual, consistent with its predominant genetic basis [44,48], and it is important to consider the long preclinical AD condition [60,61] in trying to determine the earliest cognitive markers of early neurodegeneration [62].…”

Section: Screening For Memory Impairment Dementia and Admentioning

confidence: 99%

A Computerized Continuous-Recognition Task for Measurement of Episodic Memory

Ashford

Tarpin-Bernard²,

Ashford³

et al. 2019

JAD

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Based on clinical observations of severe episodic memory (EM) impairment in dementia of Alzheimer's disease (AD), a brief, computerized EM test was developed for AD patient evaluation. A continuous recognition task (CRT) was chosen because of its extensive use in EM research. Initial experience with this computerized CRT (CCRT) showed patients were very engaged in the test, but AD patients had marked failure in recognizing repeated images. Subsequently, the test was administered to audiences, and then a two-minute online version was implemented (http://www.memtrax.com). The online CCRT shows 50 images, 25 unique and 25 repeats, which subjects respectively either try to remember or indicate recognition as quickly as possible. The pictures contain 5 sets of 5 images of scenes or objects (e.g., mountains, clothing, vehicles, etc.). A French company (HAPPYneuron, SAS) provided the test for 2 years, with these results. Of 18,477 individuals, who indicated sex and age 21-99 years and took the test for the first time, 18,007 individuals performed better than chance. In this group, age explained 1.5% of the variance in incorrect responses and 3.5% of recognition time variance, indicating considerable population variability. However, when averaging for specific year of age, age explained 58% of percent incorrect variance and 78% of recognition time variance, showing substantial population variability but a major age effect. There were no apparent sex effects. Further studies are indicated to determine the value of this CCRT as an AD screening test and validity as a measure of EM impairment in other clinical conditions.

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Section: Screening For Memory Impairment Dementia and Admentioning

confidence: 99%

A Computerized Continuous-Recognition Task for Measurement of Episodic Memory

Ashford

Tarpin-Bernard²,

Ashford³

et al. 2019

JAD

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“…Of course, we also recognize that a more definitive clinical diagnosis or assessment of cognitive dysfunction to train the learners would improve predictive model performance and practical clinical utility of MemTrax. Notably, however, a comparison of Mem-Trax to the recognized and widely utilized Montreal Cognitive Assessment Estimation of mild cognitive impairment underscored the power and potential of this new online tool and approach in evaluating shortterm memory in diagnostic support for cognitive screening and assessment with a variety of clinical conditions and impairments including dementia [20]. There is a corresponding urgent need to have quantifiable insight for individuals across the continuum from normal through mild cognitive impairment [7,21,22].…”

Section: Discussionmentioning

confidence: 99%

Episodic-Memory Performance in Machine Learning Modeling for Predicting Cognitive Health Status Classification

Bergeron

Landset

Tarpin-Bernard³

et al. 2019

JAD

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Background: Memory dysfunction is characteristic of aging and often attributed to Alzheimer's disease (AD). An easily administered tool for preliminary assessment of memory function and early AD detection would be integral in improving patient management. Objective: Our primary aim was to utilize machine learning in determining initial viable models to serve as complementary instruments in demonstrating efficacy of the MemTrax online Continuous Recognition Tasks (M-CRT) test for episodicmemory screening and assessing cognitive impairment. Methods: We used an existing dataset subset (n = 18,395) of demographic information, general health screening questions (addressing memory, sleep quality, medications, and medical conditions affecting thinking), and test results from a convenience sample of adults who took the M-CRT test. M-CRT performance and participant features were used as independent attributes: true positive/negative, percent responses/correct, response time, age, sex, and recent alcohol consumption. For predictive modeling, we used demographic information and test scores to predict binary classification of the health-related questions (yes/no) and general health status (healthy/unhealthy), based on the screening questions. Results: ANOVA revealed significant differences among HealthQScore groups for response time true positive (p = 0.000) and true positive (p = 0.020), but none for true negative (p = 0.0551). Both %responses and %correct had significant differences (p = 0.026 and p = 0.037, respectively). Logistic regression was generally the top-performing learner with moderately robust prediction performance (AUC) for HealthQScore (0.648-0.680) and selected general health questions (0.713-0.769). Conclusion: Our novel application of supervised machine learning and predictive modeling helps to demonstrate and validate cross-sectional utility of MemTrax in assessing early-stage cognitive impairment and general screening for AD.

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“…The implementation and demonstrated utility of MemTrax and these models in China, where the language and culture are drastically different from other regions of established utility (e.g., France, Netherlands, and United States) [7,8,27], further underscores the potential for widespread global acceptance and clinical value of a MemTrax-based platform. This is a demonstrable example in striving toward data harmonization and developing practical international norms and modeling resources for cognitive screening that are standardized and easily adapted for use worldwide.…”

Section: Key Points In Clinical Practicementioning

confidence: 99%

“…Two additional metrics, MoCA aggregate score and MoCA aggregate score adjusted for years of education [12], were used separately to develop dependent classification labels, thus creating two distinct modeling schemes to be applied to our combined dataset. For each version (adjusted and unadjusted) of the MoCA score, the data were again separately modeled for binary classification using two different criterion thresholds-the initially recommended one [12] and an alternate value used and promoted by others [8,15]. In the alternate threshold classification scheme,…”

Section: Moca Classification Modelingmentioning

confidence: 99%

Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment

Bergeron

Landset

Zhou

et al. 2020

JAD

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Background: The widespread incidence and prevalence of Alzheimer’s disease and mild cognitive impairment (MCI) has prompted an urgent call for research to validate early detection cognitive screening and assessment. Objective: Our primary research aim was to determine if selected MemTrax performance metrics and relevant demographics and health profile characteristics can be effectively utilized in predictive models developed with machine learning to classify cognitive health (normal versus MCI), as would be indicated by the Montreal Cognitive Assessment (MoCA). Methods: We conducted a cross-sectional study on 259 neurology, memory clinic, and internal medicine adult patients recruited from two hospitals in China. Each patient was given the Chinese-language MoCA and self-administered the continuous recognition MemTrax online episodic memory test on the same day. Predictive classification models were built using machine learning with 10-fold cross validation, and model performance was measured using Area Under the Receiver Operating Characteristic Curve (AUC). Models were built using two MemTrax performance metrics (percent correct, response time), along with the eight common demographic and personal history features. Results: Comparing the learners across selected combinations of MoCA scores and thresholds, Naïve Bayes was generally the top-performing learner with an overall classification performance of 0.9093. Further, among the top three learners, MemTrax-based classification performance overall was superior using just the top-ranked four features (0.9119) compared to using all 10 common features (0.8999). Conclusion: MemTrax performance can be effectively utilized in a machine learning classification predictive model screening application for detecting early stage cognitive impairment.

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The MemTrax Test Compared to the Montreal Cognitive Assessment Estimation of Mild Cognitive Impairment

Cited by 28 publications

References 49 publications

A Computerized Continuous-Recognition Task for Measurement of Episodic Memory

A Computerized Continuous-Recognition Task for Measurement of Episodic Memory

Episodic-Memory Performance in Machine Learning Modeling for Predicting Cognitive Health Status Classification

Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment

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