scholarly journals The Chinese version of story recall: a useful screening tool for mild cognitive impairment and Alzheimer’s disease in the elderly

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
Jing Shi ◽  
Mingqing Wei ◽  
Jinzhou Tian ◽  
Julie Snowden ◽  
Xuekai Zhang ◽  
...  
2018 ◽  
Vol 15 (2) ◽  
pp. 104-110 ◽  
Author(s):  
Shohei Kato ◽  
Akira Homma ◽  
Takuto Sakuma

Objective: This study presents a novel approach for early detection of cognitive impairment in the elderly. The approach incorporates the use of speech sound analysis, multivariate statistics, and data-mining techniques. We have developed a speech prosody-based cognitive impairment rating (SPCIR) that can distinguish between cognitively normal controls and elderly people with mild Alzheimer's disease (mAD) or mild cognitive impairment (MCI) using prosodic signals extracted from elderly speech while administering a questionnaire. Two hundred and seventy-three Japanese subjects (73 males and 200 females between the ages of 65 and 96) participated in this study. The authors collected speech sounds from segments of dialogue during a revised Hasegawa's dementia scale (HDS-R) examination and talking about topics related to hometown, childhood, and school. The segments correspond to speech sounds from answers to questions regarding birthdate (T1), the name of the subject's elementary school (T2), time orientation (Q2), and repetition of three-digit numbers backward (Q6). As many prosodic features as possible were extracted from each of the speech sounds, including fundamental frequency, formant, and intensity features and mel-frequency cepstral coefficients. They were refined using principal component analysis and/or feature selection. The authors calculated an SPCIR using multiple linear regression analysis. Conclusion: In addition, this study proposes a binary discrimination model of SPCIR using multivariate logistic regression and model selection with receiver operating characteristic curve analysis and reports on the sensitivity and specificity of SPCIR for diagnosis (control vs. MCI/mAD). The study also reports discriminative performances well, thereby suggesting that the proposed approach might be an effective tool for screening the elderly for mAD and MCI.


2011 ◽  
Vol 18 (2) ◽  
pp. 214-229 ◽  
Author(s):  
Min J. Baek ◽  
Hyun J. Kim ◽  
Hui J. Ryu ◽  
Seoung H. Lee ◽  
Seol H. Han ◽  
...  

2009 ◽  
Vol 15 (2) ◽  
pp. 258-267 ◽  
Author(s):  
MEGAN G. SHEROD ◽  
H. RANDALL GRIFFITH ◽  
JACQUELYNN COPELAND ◽  
KATHERINE BELUE ◽  
SARA KRZYWANSKI ◽  
...  

AbstractFinancial capacity is a complex instrumental activity of daily living critical to independent functioning of older adults and sensitive to impairment in patients with amnestic mild cognitive impairment (MCI) and Alzheimer’s disease (AD). However, little is known about the neurocognitive basis of financial impairment in dementia. We developed cognitive models of financial capacity in cognitively healthy older adults (n = 85) and patients with MCI (n = 113) and mild AD (n = 43). All participants were administered the Financial Capacity Instrument (FCI) and a neuropsychological test battery. Univariate correlation and multiple regression procedures were used to develop cognitive models of overall FCI performance across groups. The control model (R2 = .38) comprised (in order of entry) written arithmetic skills, delayed story recall, and simple visuomotor sequencing. The MCI model (R2 = .69) comprised written arithmetic skills, visuomotor sequencing and set alternation, and race. The AD model (R2 = .65) comprised written arithmetic skills, simple visuomotor sequencing, and immediate story recall. Written arithmetic skills (WRAT-3 Arithmetic) was the primary predictor across models, accounting for 27% (control model), 46% (AD model), and 55% (MCI model) of variance. Executive function and verbal memory were secondary model predictors. The results offer insight into the cognitive basis of financial capacity across the dementia spectrum of cognitive aging, MCI, and AD. (JINS, 2009, 15, 258–267.)


2020 ◽  
Author(s):  
Francesco Iodice ◽  
Valeria Cassano ◽  
Paolo Maria Rossini

Abstract This article reviews the main articles that have been published so far about the direct and indirect effects of the COVID-19 pandemic on a particularly fragile population represented by the healthy elderly people as well as those with Mild Cognitive Impairment and Alzheimer's disease. Such populations have been among the most affected in the early stages of the pandemic due to the direct effects of the virus and numerous indirect effects now emerge and will have to be carefully assessed over time. The pandemic associated to COVID-19 has shifted most of the health resources to the emergency area and has consequently left the three main medical areas that dealing with the elderly population (oncology, time-dependent diseases and degenerative disease) temporarily “uncovered”. In the phase following the emergency, it will be crucial to guarantee to each area the economic and organizational resources to quickly return to the level of support of the pre-pandemic state. The emergency phase represented an important moment of discussion on the possibilities of telemedicine which will inevitably become increasingly important but all the limits of its use in the elderly population have to be considered. In the post-lockdown recovery phase, alongside the classic medical evaluation, the psychological evaluation must become even more important for doctors caring about people with cognitive decline.


Author(s):  
Pedro Miguel Rodrigues ◽  
Diamantino Rui Freitas ◽  
João Paulo Teixeira ◽  
Dílio Alves ◽  
Carolina Garrett

The World's health systems are now facing a global problem known as Alzheimer's disease (AD) that mainly affects the elderly. The goal of this work is to perform a classification methodology skilled with Artificial Neural Networks (ANN) to improve the discrimination accuracy amongst patients at AD different stages comparatively to the state-of-art. For that, several study features that characterized the Electroencephalogram (EEG) signals “slow-down” were extracted and presented to the ANN entries in order to classify the dataset. The classification results achieved in the present work are promising concerning AD early diagnosis and they show that EEG can be a good tool for AD detection (Controls (C) vs AD: accuracy 95%; C vs Mild-cognitive Impairment (MCI): accuracy 77%; MCI vs AD: accuracy 83%; All vs All: accuracy 90%).


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