P4-376: Behavioral performance as a predictor of cognitive decline: Detecting the transition from healthy aging to mild cognitive impairment

2012 ◽  
Vol 8 (4S_Part_21) ◽  
pp. S791-S791 ◽  
Author(s):  
Stuart Zola ◽  
Cecelia Manzanares ◽  
Paul Clopton ◽  
James Lah ◽  
Allan Levey
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Decline ◽  
Healthy Aging ◽  
Behavioral Performance

scholarly journals Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jaime Gómez-Ramírez ◽  
Marina Ávila-Villanueva ◽  
Miguel Ángel Fernández-Blázquez
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Random Forest ◽  
Cognitive Decline ◽  
Social Life ◽  
Healthy Aging ◽  
Personal Information ◽  
Disease Onset ◽  
Subjective Cognitive Decline ◽  
Comorbid Condition

AbstractAlzheimer’s Disease is a complex, multifactorial, and comorbid condition. The asymptomatic behavior in the early stages makes the identification of the disease onset particularly challenging. Mild cognitive impairment (MCI) is an intermediary stage between the expected decline of normal aging and the pathological decline associated with dementia. The identification of risk factors for MCI is thus sorely needed. Self-reported personal information such as age, education, income level, sleep, diet, physical exercise, etc. is called to play a key role not only in the early identification of MCI but also in the design of personalized interventions and the promotion of patients empowerment. In this study, we leverage a large longitudinal study on healthy aging in Spain, to identify the most important self-reported features for future conversion to MCI. Using machine learning (random forest) and permutation-based methods we select the set of most important self-reported variables for MCI conversion which includes among others, subjective cognitive decline, educational level, working experience, social life, and diet. Subjective cognitive decline stands as the most important feature for future conversion to MCI across different feature selection techniques.

scholarly journals Selecting the most important self-assessed features for predicting conversion to Mild Cognitive Impairment with Random Forest and Permutation-based methods

2019 ◽  
Author(s):  
Jaime Gómez-Ramírez ◽  
Marina Ávila-Villanueva ◽  
Miguel Ángel Fernández-Blázquez
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Random Forest ◽  
Cognitive Decline ◽  
Social Life ◽  
Healthy Aging ◽  
Personal Information ◽  
Disease Onset ◽  
Subjective Cognitive Decline ◽  
Comorbid Condition

ABSTRACTAlzheimer’s Disease (AD) is a complex, multifactorial and comorbid condition. The asymptomatic behavior in the early stages makes the identification of the disease onset particularly challenging. Mild cognitive impairment (MCI) is an intermediary stage between the expected decline of normal aging and the pathological decline associated with dementia. The identification of risk factors for MCI is thus sorely needed. Self-reported personal information such as age, education, income level, sleep, diet, physical exercise, etc. are called to play a key role not only in the early identification of MCI but also in the design of personalized interventions and the promotion of patients empowerment. In this study we leverage on The Vallecas Project, a large longitudinal study on healthy aging in Spain, to identify the most important self-reported features for future conversion to MCI. Using machine learning (random forest) and permutation-based methods we select the set of most important self-reported variables for MCI conversion which includes among others, subjective cognitive decline, educational level, working experience, social life, and diet. Subjective cognitive decline stands as the most important feature for future conversion to MCI across different feature selection techniques.

The NIH Toolbox Cognition Battery App in healthy aging and cognitive decline due to Alzheimer’s Disease and Mild Cognitive Impairment

2020 ◽  
Vol 30 (Supplement_2) ◽  
Author(s):  
C Marques-Costa ◽  
M S Pinho ◽  
M R Simões ◽  
G Prieto
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Decline ◽  
Language Processing ◽  
Healthy Aging ◽  
Older Individuals ◽  
Average Life Expectancy

Abstract Introduction There has been a significant increase in average life expectancy. This increase brought more focus on aging with more health, autonomy and independence. Among current public health concerns, the detection of cognitive decline in older individuals stands out, namely in Alzheimer’s disease (AD) and Mild Cognitive Impairment (MCI). Accurate, brief, practical and automated measures are needed to assess cognitive function throughout the life-span. Since 2015, there is the NIH Toolbox Cognition Battery (NIHTB-CB) app that meets these requirements and assesses the neurocognitive subdomains of attention, episodic memory, executive function, language, processing speed, and working memory. The European Portuguese app, developed by our team, will be validated for the Portuguese older adults. Objectives The aim is presenting a literature review of the use of NIHTB-CB in healthy aging and cognitive decline in MCI and AD. Methodology Advanced search in the databases of Web of Science and Google Scholar for studies published between 2016-2019, including articles and meeting abstracts with the words: NIH Toolbox Cognition Battery, AD, MCI, Elder or Senior or Older. Results According to the studies reviewed, NIHTB-CB may be useful in memory clinics (e.g.Hackett et al, 2018; Mayeda et al. 2018); clinical trials (e.g.Buckley et al., 2017; Parsey, Bagger & Hanson, 2019); and healthy aging (e.g.Scott, Sorell, & Benitez, 2019). Preliminary results of the ARMADA study (Weintraub et al, 2019) with people with more than 85 years old became available showing that generally, NIHTB-CB is well accepted, also in MCI patients. No difficulties were found in the use of the iPad with older adults. Conclusion NIHTB-CB measures provide a valid assessment of neurocognitive domains that are important in healthy aging, MCI and AD. As the studies are still scarce, more research is needed.

Subjective Spatial Navigation Complaints - A Frequent Symptom Reported by Patients with Subjective Cognitive Decline, Mild Cognitive Impairment and Alzheimer's Disease

2018 ◽  
Vol 15 (3) ◽  
pp. 219-228 ◽  
Author(s):  
Jiri Cerman ◽  
Ross Andel ◽  
Jan Laczo ◽  
Martin Vyhnalek ◽  
Zuzana Nedelska ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Decline ◽  
Spatial Navigation ◽  
Subjective Cognitive Decline ◽  
Great Effort ◽  
Frequent Symptom ◽  
Normal Controls

Background: Great effort has been put into developing simple and feasible tools capable to detect Alzheimer's disease (AD) in its early clinical stage. Spatial navigation impairment occurs very early in AD and is detectable even in the stage of mild cognitive impairment (MCI). Objective: The aim was to describe the frequency of self-reported spatial navigation complaints in patients with subjective cognitive decline (SCD), amnestic and non-amnestic MCI (aMCI, naMCI) and AD dementia and to assess whether a simple questionnaire based on these complaints may be used to detect early AD. Method: In total 184 subjects: patients with aMCI (n=61), naMCI (n=27), SCD (n=63), dementia due to AD (n=20) and normal controls (n=13) were recruited. The subjects underwent neuropsychological examination and were administered a questionnaire addressing spatial navigation complaints. Responses to the 15 items questionnaire were scaled into four categories (no, minor, moderate and major complaints). Results: 55% of patients with aMCI, 64% with naMCI, 68% with SCD and 72% with AD complained about their spatial navigation. 38-61% of these complaints were moderate or major. Only 33% normal controls expressed complaints and none was ranked as moderate or major. The SCD, aMCI and AD dementia patients were more likely to express complaints than normal controls (p's<0.050) after adjusting for age, education, sex, depressive symptoms (OR for SCD=4.00, aMCI=3.90, AD dementia=7.02) or anxiety (OR for SCD=3.59, aMCI=3.64, AD dementia=6.41). Conclusion: Spatial navigation complaints are a frequent symptom not only in AD, but also in SCD and aMCI and can potentially be detected by a simple and inexpensive questionnaire.

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