Clinical Utility of Two- and Three-Dimensional Visuoconstructional Tasks in Mild Cognitive Impairment and Early Alzheimer's Disease.

2019 ◽  
Author(s):  
Rafaela Martins-Rodrigues ◽  
Égina Karoline Gonçalves da Fonsêca ◽  
Silvia Sanaly Lucena-Alves ◽  
Israel Contador ◽  
Luigi Trojano ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Block Design ◽  
Three Dimensional ◽  
Youden Index ◽  
Characteristic Analysis ◽  
Clock Drawing Test ◽  
J 51

Abstract Objective The aim of this study was to investigate whether different types of visuoconstructional abilities are useful to distinguish individuals with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) from healthy controls (HCs). Method We selected 20 patients with MCI and 14 with AD diagnosis based on standard criteria. The neuropsychological performance of MCI and AD groups were compared with that of a group of 11 HCs using a standard neuropsychological battery and visuoconstructional tasks that differed difficulty and type of implicated skills (graphomotor vs. non-graphomotor): two-dimensional (Clock Drawing Test, CDT; Block Design, BD; and Visual Puzzles, VP) and three-dimensional Block Construction (TBC). Results AD group scored significantly lower than HCs in BD, VP and TBC tasks, but no significant differences were found between HCs and MCI. CDT (copy condition) scores did not differ significantly among the groups. The receiver operating characteristic analysis showed that BD [sensitivity (se) = .85, specificity (sp) = .90, Youden index (J) = .76], VP (se = .78 and sp = .72, J = .51) and TBC (se = .71, sp = 100, J = .71) were accurate tasks to discriminate between AD and HCs. Moreover, BD tasks (se = .85, sp = .70, J = .55) and TBC (se = .71, sp = .80, J = .51) showed fair accuracy to differentiate between MCI and AD groups. Conclusions These findings indicate that non-graphomotor visuoconstructional tasks are already impaired in the early stages of AD, but are preserved in MCI individuals when compared with HCs.

Does the Combination of the MMSE and Clock Drawing Test (Mini-Clock) Improve the Detection of Mild Alzheimer's Disease and Mild Cognitive Impairment?

2010 ◽  
Vol 22 (3) ◽  
pp. 889-896 ◽  
Author(s):  
Jesús Cacho ◽  
Julián Benito-León ◽  
Ricardo García-García ◽  
Bernardino Fernández-Calvo ◽  
José Luis Vicente-Villardón ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Clock Drawing Test ◽  
Clock Drawing ◽  
Drawing Test

scholarly journals Assessment for apraxia in Mild Cognitive Impairment and Alzheimer's disease

2015 ◽  
Vol 9 (1) ◽  
pp. 71-75 ◽  
Author(s):  
Mirela Ward ◽  
Juliana F. Cecato ◽  
Ivan Aprahamian ◽  
José Eduardo Martinelli
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Mini Mental State Examination ◽  
Average Score ◽  
Clock Drawing Test ◽  
Healthy Elderly ◽  
Drawing Test ◽  
State Examination

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

Characteristics of Mild Cognitive Impairment Using the Thai Version of the Consortium to Establish a Registry for Alzheimer’s Disease Tests: A Multivariate and Machine Learning Study

2018 ◽  
Vol 45 (1-2) ◽  
pp. 38-48 ◽  
Author(s):  
Chavit Tunvirachaisakul ◽  
Thitiporn Supasitthumrong ◽  
Sookjareon Tangwongchai ◽  
Solaphat Hemrunroj ◽  
Phenphichcha Chuchuen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Support Vector ◽  
Test Machine ◽  
Characteristic Analysis ◽  
Adaptive Boosting ◽  
Normal Controls

Background: The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) developed a neuropsychological battery (CERAD-NP) to screen patients with Alzheimer’s dementia. Mild cognitive impairment (MCI) has received attention as a pre-dementia stage. Objectives: To delineate the CERAD-NP features of MCI and their clinical utility to externally validate MCI diagnosis. Methods: The study included 60 patients with MCI, diagnosed using the Clinical Dementia Rating, and 63 normal controls. Data were analysed employing receiver operating characteristic analysis, Linear Support Vector Machine, Random Forest, Adaptive Boosting, Neural Network models, and t-distributed stochastic neighbour embedding (t-SNE). Results: MCI patients were best discriminated from normal controls using a combination of Wordlist Recall, Wordlist Memory, and Verbal Fluency Test. Machine learning showed that the CERAD features learned from MCI patients and controls were not strongly predictive of the diagnosis (maximal cross-validation 77.2%), whilst t-SNE showed that there is a considerable overlap between MCI and controls. Conclusions: The most important features of the CERAD-NP differentiating MCI from normal controls indicate impairments in episodic and semantic memory and recall. While these features significantly discriminate MCI patients from normal controls, the tests are not predictive of MCI.

Models for predicting risk of dementia: a systematic review

2018 ◽  
Vol 90 (4) ◽  
pp. 373-379 ◽  
Author(s):  
Xiao-He Hou ◽  
Lei Feng ◽  
Can Zhang ◽  
Xi-Peng Cao ◽  
Lan Tan ◽  
...  
Keyword(s):  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Predictive Ability ◽  
Disease Assessment ◽  
Characteristic Analysis ◽  
Risk Models ◽  
Dementia Risk

BackgroundInformation from well-established dementia risk models can guide targeted intervention to prevent dementia, in addition to the main purpose of quantifying the probability of developing dementia in the future.MethodsWe conducted a systematic review of published studies on existing dementia risk models. The models were assessed by sensitivity, specificity and area under the curve (AUC) from receiver operating characteristic analysis.ResultsOf 8462 studies reviewed, 61 articles describing dementia risk models were identified, with the majority of the articles modelling late life risk (n=39), followed by those modelling prediction of mild cognitive impairment to Alzheimer’s disease (n=15), mid-life risk (n=4) and patients with diabetes (n=3). Age, sex, education, Mini Mental State Examination, the Consortium to Establish a Registry for Alzheimer’s Disease neuropsychological assessment battery, Alzheimer’s Disease Assessment Scale-cognitive subscale, body mass index, alcohol intake and genetic variables are the most common predictors included in the models. Most risk models had moderate-to-high predictive ability (AUC>0.70). The highest AUC value (0.932) was produced from a risk model developed for patients with mild cognitive impairment.ConclusionThe predictive ability of existing dementia risk models is acceptable. Population-specific dementia risk models are necessary for populations and subpopulations with different characteristics.

Multimodal Classification of Alzheimer’s Disease and Amnestic Mild Cognitive Impairment: Integrated 18F-FDG PET and DTI Study

2021 ◽  
pp. 1-13
Author(s):  
Weihua Li ◽  
Zhilian Zhao ◽  
Min Liu ◽  
Shaozhen Yan ◽  
Yanhong An ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Fdg Pet ◽  
Classification Performance ◽  
Amnestic Mild Cognitive Impairment ◽  
Healthy Controls ◽  
Characteristic Analysis ◽  
18F Fdg

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and memory impairment. Amnestic mild cognitive impairment (aMCI) is the intermediate stage between normal cognitive aging and early dementia caused by AD. It can be challenging to differentiate aMCI patients from healthy controls (HC) and mild AD patients. Objective: To validate whether the combination of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) and diffusion tensor imaging (DTI) will improve classification performance compared with that based on a single modality. Methods: A total of thirty patients with AD, sixty patients with aMCI, and fifty healthy controls were included. AD was diagnosed according to the National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable. aMCI diagnosis was based on Petersen’s criteria. The 18F-FDG PET and DTI measures were each used separately or in combination to evaluate sensitivity, specificity, and accuracy for differentiating HC, aMCI, and AD using receiver operating characteristic analysis together with binary logistic regression. The rate of accuracy was based on the area under the curve (AUC). Results: For classifying AD from HC, we achieve an AUC of 0.96 when combining two modalities of biomarkers and 0.93 when using 18F-FDG PET individually. For classifying aMCI from HC, we achieve an AUC of 0.79 and 0.76 using the best individual modality of biomarkers. Conclusion: Our results show that the combination of two modalities improves classification performance, compared with that using any individual modality.

scholarly journals Discriminative capacity and construct validity of the Clock Drawing Test in Mild Cognitive Impairment and Alzheimer’s disease

2018 ◽  
Vol 33 (7) ◽  
pp. 1159-1174 ◽  
Author(s):  
Diana Duro ◽  
Sandra Freitas ◽  
Miguel Tábuas-Pereira ◽  
Beatriz Santiago ◽  
Maria Amália Botelho ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Construct Validity ◽  
Clock Drawing Test ◽  
Clock Drawing ◽  
Drawing Test
Sign in / Sign up

Export Citation Format

Share Document