Validity of the MemTrax Memory Test Compared to the Montreal Cognitive Assessment in the Detection of Mild Cognitive Impairment and Dementia due to Alzheimer’s Disease in a Chinese Cohort

2021 ◽  
pp. 1-11
Author(s):  
Xiaolei Liu ◽  
Xinjie Chen ◽  
Xianbo Zhou ◽  
Yajun Shang ◽  
Fan Xu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Assessment ◽  
Area Under The Curve ◽  
Memory Test ◽  
Montreal Cognitive Assessment ◽  
Chinese Cohort ◽  
Moca Score

Background: A valid, reliable, accessible, engaging, and affordable digital cognitive screen instrument for clinical use is in urgent demand. Objective: To assess the clinical utility of the MemTrax memory test for early detection of cognitive impairment in a Chinese cohort. Methods: The 2.5-minute MemTrax and the Montreal Cognitive Assessment (MoCA) were performed by 50 clinically diagnosed cognitively normal (CON), 50 mild cognitive impairment due to AD (MCI-AD), and 50 Alzheimer’s disease (AD) volunteer participants. The percentage of correct responses (MTx-% C), the mean response time (MTx-RT), and the composite scores (MTx-Cp) of MemTrax and the MoCA scores were comparatively analyzed and receiver operating characteristic (ROC) curves generated. Results: Multivariate linear regression analyses indicated MTx-% C, MTx-Cp, and the MoCA score were significantly lower in MCI-AD versus CON and in AD versus MCI-AD groups (all with p≤0.001). For the differentiation of MCI-AD from CON, an optimized MTx-% C cutoff of 81% had 72% sensitivity and 84% specificity with an area under the curve (AUC) of 0.839, whereas the MoCA score of 23 had 54% sensitivity and 86% specificity with an AUC of 0.740. For the differentiation of AD from MCI-AD, MTx-Cp of 43.0 had 70% sensitivity and 82% specificity with an AUC of 0.799, whereas the MoCA score of 20 had 84% sensitivity and 62% specificity with an AUC of 0.767. Conclusion: MemTrax can effectively detect both clinically diagnosed MCI and AD with better accuracy as compared to the MoCA based on AUCs in a Chinese cohort.

Is the Montreal Cognitive Assessment (MoCA) screening superior to the Mini-Mental State Examination (MMSE) in the detection of mild cognitive impairment (MCI) and Alzheimer’s Disease (AD) in the elderly?

2018 ◽  
Vol 31 (04) ◽  
pp. 491-504 ◽  
Author(s):  
Tiago C. C. Pinto ◽  
Leonardo Machado ◽  
Tatiana M. Bulgacov ◽  
Antônio L. Rodrigues-Júnior ◽  
Maria L. G. Costa ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Mental State ◽  
Cognitive Assessment ◽  
Mini Mental State Examination ◽  
Montreal Cognitive Assessment ◽  
Cochrane Library ◽  
State Examination

ABSTRACTObjective:To compare the accuracy of Mini-Mental State Examination (MMSE) and of the Montreal Cognitive Assessment (MoCA) in tracking mild cognitive impairment (MCI) and Alzheimer’s Disease (AD).Method:A Systematic review of the PubMed, Bireme, Science Direct, Cochrane Library, and PsycInfo databases was conducted. Using inclusion and exclusion criteria and staring with 1,629 articles, 34 articles were selected. The quality of the selected research was evaluated through the Quality Assessment of Diagnostic Accuracy Studies 2 tool (QUADAS-2).Result:More than 80% of the articles showed MoCA to be superior to MMSE in discriminating between individuals with mild cognitive impairment and no cognitive impairment. The area under the curve varied from 0.71 to 0.99 for MoCA, and 0.43 to 0.94 for MMSE, when evaluating the ability to discriminate MCI in the cognitively healthy elderly individuals, and 0.87 to 0.99 and 0.67 to 0.99, respectively, when evaluating the detection of AD. The AUC mean value for MoCA was significantly larger compared to the MMSE in discriminating MCI from control [0.883 (CI 95% 0.855-0.912) vs MMSE 0.780 (CI 95% 0.740-0.820) p < 0.001].Conclusion:The screening tool MoCA is superior to MMSE in the identification of MCI, and both tests were found to be accurate in the detection of AD.

scholarly journals Errors on the MoCA's animal-naming: findings from Parkinson's disease patients

2017 ◽  
Vol 29 (7) ◽  
pp. 1227-1228
Author(s):  
Yassar Alamri ◽  
Tim Anderson ◽  
John Dalrymple-Alford ◽  
Michael Macaskill
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Assessment ◽  
Amnestic Mild Cognitive Impairment ◽  
Original Version ◽  
Montreal Cognitive Assessment ◽  
Healthy Controls

We read the findings by Cecato et al. (2016) with great interest. In their study, naming the rhinoceros discriminated between patients with amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) but not healthy controls (HC). Of note, HC participants were significantly younger than aMCI and AD patients. All participants were administered the original version of the Montreal Cognitive Assessment (MoCA) instrument.

A subtest analysis of the Montreal cognitive assessment (MoCA): which subtests can best discriminate between healthy controls, mild cognitive impairment and Alzheimer's disease?

2015 ◽  
Vol 28 (5) ◽  
pp. 825-832 ◽  
Author(s):  
Juliana Francisco Cecato ◽  
José Eduardo Martinelli ◽  
Rafael Izbicki ◽  
Mônica Sanches Yassuda ◽  
Ivan Aprahamian
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Assessment ◽  
Depression Scale ◽  
Montreal Cognitive Assessment ◽  
Word Recall ◽  
Screening Tests ◽  
Healthy Controls

ABSTRACTBackground:It is necessary to continue to explore the psychometric characteristics of key cognitive screening tests such as the Montreal Cognitive Assessment (MoCA) to diagnose cognitive decline as early as possible and to attend to the growing need of clinical trials involving mild cognitive impairment (MCI) participants. The main aim of this study was to assess which MoCA subtests could best discriminate between healthy controls (HC), participants with MCI, and Alzheimer's disease (AD).Methods:Cross-sectional analysis of 136 elderly with more than four years of education. All participants were submitted to detailed clinical, laboratory, and neuroimaging evaluation. The MoCA, Mini-Mental State Examination (MMSE), the Cambridge Cognitive Examination (CAMCOG), Geriatric Depression Scale (GDS), and Functional Activities Questionnaire (FAQ) were applied to all participants. The MoCA test was not used in the diagnostic procedure.Results:Median MoCA total scores were 27, 23 and 18 for HC, MCI, and AD, respectively (p < 0.001). Word repetition, inverse digits, serial 7, phrases, verbal fluency, abstraction, and word recall discriminated between MCI and HC participants (p < 0.001). The clock drawing, the rhino naming, delayed recall of five words and orientation discriminated between patients with MCI and AD (p < 0.001). A reduced version of the MoCA with only these items did not improve accuracy between MCI and HC (p = 0.076) or MCI and AD (p = 0.119).Conclusions:Not all MoCA subtests might be fundamental to clinical diagnosis of MCI. The reduced versions of MoCA did not add diagnostic accuracy.

Use of the Montreal Cognitive Assessment Thai Version to Discriminate Amnestic Mild Cognitive Impairment from Alzheimer’s Disease and Healthy Controls: Machine Learning Results

2021 ◽  
pp. 1-12
Author(s):  
Solaphat Hemrungrojn ◽  
Sookjaroen Tangwongchai ◽  
Thammanard Charoenboon ◽  
Muthita Panasawat ◽  
Thitiporn Supasitthumrong ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Roc Curve ◽  
Cognitive Assessment ◽  
Assessment Tool ◽  
Amnestic Mild Cognitive Impairment ◽  
Montreal Cognitive Assessment ◽  
Healthy Controls

<b><i>Background:</i></b> The Montreal Cognitive Assessment (MoCA) is an effective and applicable screening instrument to confirm the diagnosis of amnestic mild cognitive impairment (aMCI) from patients with Alzheimer’s disease (AD) and healthy controls (HCs). <b><i>Objectives:</i></b> This study aimed to determine the reliability and validity of the following: (a) Thai translation of the MoCA (MoCA-Thai) and (b) delineate the key features of aMCI based on the MoCA subdomains. <b><i>Methods:</i></b> This study included 60 HCs, 61 aMCI patients, and 60 AD patients. The MoCA-Thai shows adequate psychometric properties including internal consistency, concurrent validity, test-retest validity, and inter-rater reliability. <b><i>Results:</i></b> The MoCA-Thai may be employed as a diagnostic criterion to make the diagnosis of aMCI, whereby aMCI patients are discriminated from HC with an area under the receiver-operating characteristic (AUC-ROC) curve of 0.813 and from AD patients with an AUC-ROC curve of 0.938. The best cutoff scores of the MoCA-Thai to discriminate aMCI from HC is ≤24 and from AD &#x3e; 16. Neural network analysis showed that (a) aberrations in recall was the most important feature of aMCI versus HC with impairments in language and orientation being the second and third most important features and (b) aberrations in visuospatial skills and executive functions were the most important features of AD versus aMCI and that impairments in recall, language, and orientation but not attention, concentration, and working memory, further discriminated AD from aMCI. <b><i>Conclusions:</i></b> The MoCA-Thai is an appropriate cognitive assessment tool to be used in the Thai population for the diagnosis of aMCI and AD.

scholarly journals 12-year prediction of mild cognitive impairment aided by Alzheimer’s brain signatures at mean age 56

2021 ◽  
Author(s):  
McKenna E Williams ◽  
Jeremy A Elman ◽  
Linda K McEvoy ◽  
Ole A Andreassen ◽  
Anders M Dale ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Mean Diffusivity ◽  
Area Under The Curve ◽  
Polygenic Risk Score ◽  
Age Difference ◽  
Cognitively Normal ◽  
Brain Age

Abstract Neuroimaging signatures based on composite scores of cortical thickness and hippocampal volume predict progression from mild cognitive impairment to Alzheimer’s disease. However, little is known about the ability of these signatures among cognitively normal adults to predict progression to mild cognitive impairment. Toward that end, a signature sensitive to microstructural changes that may predate macrostructural atrophy should be useful. We hypothesized that: 1) a validated MRI-derived Alzheimer’s disease signature based on cortical thickness and hippocampal volume in cognitively normal middle-aged adults would predict progression to mild cognitive impairment; and 2) a novel gray matter mean diffusivity signature would be a better predictor than the thickness/volume signature. This cohort study was part of the Vietnam Era Twin Study of Aging. Concurrent analyses compared cognitively normal and mild cognitive impairment groups at each of three study waves (ns = 246–367). Predictive analyses included 169 cognitively normal men at baseline (age = 56.1, range = 51–60). Our previously published thickness/volume signature derived from independent data, a novel mean diffusivity signature using the same regions and weights as the thickness/volume signature, age, and an Alzheimer’s disease polygenic risk score were used to predict incident mild cognitive impairment an average of 12 years after baseline (follow-up age = 67.2, range = 61–71). Additional analyses adjusted for predicted brain age difference scores (chronological age minus predicted brain age) to determine if signatures were Alzheimer-related and not simply aging-related. In concurrent analyses, individuals with mild cognitive impairment had higher (worse) mean diffusivity signature scores than cognitively normal participants, but thickness/volume signature scores did not differ between groups. In predictive analyses, age and polygenic risk score yielded an area under the curve of 0.74 (sensitivity = 80.00%; specificity = 65.10%). Prediction was significantly improved with addition of the mean diffusivity signature (area under the curve = 0.83; sensitivity = 85.00%; specificity = 77.85%; P=0.007), but not with addition of the thickness/volume signature. A model including both signatures did not improve prediction over a model with only the mean diffusivity signature. Results held up after adjusting for predicted brain age difference scores. The novel mean diffusivity signature was limited by being yoked to the thickness/volume signature weightings. An independently-derived mean diffusivity signature may thus provide even stronger prediction. The young age of the sample at baseline is particularly notable. Given that the brain signatures were examined when participants were only in their 50 s, our results suggest a promising step toward improving very early identification of Alzheimer’s disease risk and the potential value of mean diffusivity and/or multimodal brain signatures.

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