Daily stressors and emotional reactivity in individuals with mild cognitive impairment and cognitively healthy controls.

2015 ◽  
Vol 30 (2) ◽  
pp. 420-431 ◽  
Author(s):  
Elizabeth Hahn Rickenbach ◽  
Kristen L. Condeelis ◽  
William E. Haley
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Emotional Reactivity ◽  
Healthy Controls ◽  
Daily Stressors

scholarly journals Assessing visuo-constructive functions in patients with subjective cognitive decline, mild cognitive impairment and Alzheimer’s disease with the Vienna Visuo-Constructional Test 3.0 (VVT 3.0)

2021 ◽  
Author(s):  
Noel Valencia ◽  
Johann Lehrner
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Decline ◽  
Multinomial Logistic Regression ◽  
Subjective Cognitive Decline ◽  
Healthy Controls ◽  
Considerable Potential ◽  
Multinomial Logistic Regression Analysis

Summary Background Visuo-Constructive functions have considerable potential for the early diagnosis and monitoring of disease progression in Alzheimer’s disease. Objectives Using the Vienna Visuo-Constructional Test 3.0 (VVT 3.0), we measured visuo-constructive functions in subjective cognitive decline (SCD), mild cognitive impairment (MCI), Alzheimer’s disease (AD), and healthy controls to determine whether VVT performance can be used to distinguish these groups. Materials and methods Data of 671 participants was analyzed comparing scores across diagnostic groups and exploring associations with relevant clinical variables. Predictive validity was assessed using Receiver Operator Characteristic curves and multinomial logistic regression analysis. Results We found significant differences between AD and the other groups. Identification of cases suffering from visuo-constructive impairment was possible using VVT scores, but these did not permit classification into diagnostic subgroups. Conclusions In summary, VVT scores are useful indicators for visuo-constructive impairment but face challenges when attempting to discriminate between several diagnostic groups.

scholarly journals Attention and inhibition in Mild Cognitive Impairment and Alzheimer's Disease

2013 ◽  
Vol 6 (3) ◽  
pp. 43-50
Author(s):  
Clara Zancada-Menéndez ◽  
Patricia Sampedro-Piquero ◽  
Azucena Begega ◽  
Laudino López ◽  
Jorge Luis Arias
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Reaction Time ◽  
Alzheimer Disease ◽  
Neuropsychological Test ◽  
Reaction Times ◽  
Stop Signal Task ◽  
Healthy Controls

Mild cognitive impairment is understood as a cognitive deficit of insufficient severity to fulfil the criteria for Alzheimer’s disease. Many studies have attempted to identify which cognitive functions are most affected by this type of impairment and which is the most sensitive neuropsychological test for early detection. This study investigated sustained and selective attention, processing speed, and the inhibition process using a sample of people divided into three groups mild cognitive impairment, Alzheimer disease and cognitively healthy controls selected and grouped based on their scores in the Mini Mental State Examination and Cambridge Cognitive Examination-revised. Three tests from the Cambridge Neuropsychological Test Automated Battery (Motor Screening Task, Stop Signal Task and Reaction time) were used as well as the d2 attention test. The results show that that participants with mild cognitive impairment and Alzheimer disease showed lower levels of concentration compared with the cognitively healthy controls group in the d2 test and longer reaction times in the Cambridge Neuropsychological Test Automated Battery, although the differences were not marked in the latter test. The impairments in basic cognitive processes, such as reaction time and sustained attention, indicate the need to take these functions into account in the test protocols when discriminating between normal aging and early and preclinical dementia processes.

scholarly journals How can automated linguistic analysis help to discern functional cognitive disorder from healthy controls and mild cognitive impairment?

BJPsych Open ◽  
2021 ◽  
Vol 7 (S1) ◽  
pp. S7-S7
Author(s):  
Lizzie Beavis ◽  
Ronan O'Malley ◽  
Bahman Mirheidari ◽  
Heidi Christensen ◽  
Daniel Blackburn
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Delayed Diagnosis ◽  
Age Of Acquisition ◽  
Linguistic Analysis ◽  
Cross Sectional Study ◽  
Cognitive Disorder ◽  
Remote Memory ◽  
Healthy Controls ◽  
Cross Sectional

AimsThe disease burden of cognitive impairment is significant and increasing. The aetiology of cognitive impairment can be structural, such as in mild cognitive impairment (MCI) due to early Alzheimer's disease (AD), or in functional cognitive disorder (FCD), where there is no structural pathology. Many people with FCD receive a delayed diagnosis following invasive or costly investigations. Accurate, timely diagnosis improves outcomes across all patients with cognitive impairment. Research suggests that analysis of linguistic features of speech may provide a non-invasive diagnostic tool. This study aimed to investigate the linguistic differences in conversations between people with early signs of cognitive impairment with and without structural pathology, with a view to developing a screening tool using linguistic analysis of conversations.MethodIn this explorative, cross-sectional study, we recruited 25 people with MCI considered likely due to AD, (diagnosed according to Petersen's criteria and referred to as PwMCI), 25 healthy controls (HCs) and 15 people with FCD (PwFCD). Participants’ responses to a standard questionnaire asked by an interactional virtual agent (Digital Doctor) were quantified using previously identified parameters. This paper presents statistical analyses of the responses and a discussion of the results.ResultPwMCI produced fewer words than PwFCD and HCs. The ratio of pauses to speech was generally lower for PwMCI and PwFCD than for HCs. PwMCI showed a greater pause to speech ratio for recent questions (such as ‘what did you do at the weekend?’) compared with the HCs. Those with FCD showed the greatest pause to speech ratio in remote memory questions (such as ‘what was your first job?’). The average age of acquisition of answers for verbal fluency questions was lower in the MCI group than HCs.ConclusionThe results and qualitative observations support the relative preservation of remote memory compared to recent memory in MCI due to AD and decreased spontaneous elaboration in MCI compared with healthy controls and patients with FCD. Word count, age of acquisition and pause to speech ratio could form part of a diagnostic toolkit in identifying those with structural and functional causes of cognitive impairment. Further investigation is required using a large sample.

scholarly journals LINGUISTIC MEASURES OF SPOKEN UTTERANCES FOR DETECTING MILD COGNITIVE IMPAIRMENT

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S224-S225
Author(s):  
Meysam Asgari ◽  
Jeffrey Kaye ◽  
Hiroko Dodge
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Stimulation ◽  
Support Vector ◽  
Svm Classifier ◽  
Healthy Controls ◽  
Document Frequency ◽  
Trained Interviewer ◽  
Validation Procedure ◽  
Characteristic Area

Abstract Studies have shown that speech characteristics can aid in early-identification of those with mild cognitive impairment (MCI). We performed a linguistic analysis on spoken utterances of 41 participants (15 MCI, 26 healthy controls) from conversations with a trained interviewer using the Term Frequency-Inverse Document Frequency (TF-IDF) method. Data came from a randomized controlled behavioral clinical trial (ClinicalTrials.gov: NCT01571427) to examine effects of conversation-based cognitive stimulation on cognitive functions among older adults with normal cognition or MCI, which served as a pilot study for I-CONECT. From the collected spoken utterances we first constructed a fixed-dimensional feature vector using TF-IDF. Next, to distinguish between MCI and healthy controls, we trained a support vector machine (SVM) classifier on per-subject feature vectors according to 5-fold cross-validation procedure. Our results verify the effectiveness of TF-IDF features in this classification task with Receiver Operating Characteristic Area Under Curve of 81%, well above chance at 65%.

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