Automated assessment of speech production and prediction of MCI in older adults

A preliminary investigation on core lexicon analysis in dementia of the Alzheimer's type

BACKGROUND

Core lexicon (CL) analysis is a time efficient and possibly reliable measure that captures discourse production abilities. For people with aphasia, CL scores have demonstrated correlations with aphasia severity, as well as other discourse and linguistic measures. It was also found to be clinician-friendly and clinically sensitive enough to capture longitudinal changes in aphasia. To our knowledge, CL has never been investigated in individuals with neurologically progressive disease.

AIMS

As a preliminary investigation, we sought to investigate (1) whether CL scores correlate with dementia severity, (2) whether CL scores correlate with measures of discourse quality, and (3) whether CL scores correlate with other measures of lexical/semantic access.

METHODS & PROCEDURES

Twelve participants with a cognitive impairment associated with dementia of the Alzheimer's type (DAT) completed several measures of language and cognitive ability, as well as provide a language sample from the wordless picture book, Picnic.

RESULTS & CONCLUSION

Results are informative, as they provide insight into characteristics of CL and provide support for potential use of CL in individuals with neurologically progressive disease. The results indicated that CL scores do correlate with dementia severity and several measures of language ability, indicating they may provide a useful measure of language abilities in DAT, but more research is needed.

WHAT THIS PAPER ADDS

What is already known on the subject Core lexicon (CL) analysis is an assessment measure of discourse ability, most closely related to informativeness or productivity, used in aphasiology that is easier to use and less time consuming than previous measures of informativeness, such as correct information units or type-token ratio (TTR). For people with aphasia, CL analysis correlates with aphasia severity, measures of informativeness, as well as other measures of discourse quality. It has also been shown to be faster and more reliable between scorers than other informativeness measures. What this study adds Core lexicon analysis is a new simple and online method for assessing the informativeness of a discourse sample without the need to record or transcribe the language sample. CL is receiving a lot of attention in aphasia, correlating with everything from aphasia severity to measures of productivity and lexical access, as well as measures of informativeness. Unfortunately, no one has investigated CL analysis in dementia. The study demonstrates the first evidence that CL analysis may be a useful measure for determining dementia severity and language quality in people with dementia. What are the clinical implications of this work? Core lexicon analysis may provide clinicians and researchers with an easy method for assessing the discourse of people with a cognitive impairment associated with dementia of the Alzheimer's type. This will improve initial assessment, as well as improve ongoing language assessment that may provide clues into their functional ability to communicate effectively.

Sociodemographic Factors Predict Incident Mild Cognitive Impairment: A Brief Review and Empirical Study

OBJECTIVES

Mild cognitive impairment (MCI) is a transitional stage between normal cognitive aging and dementia that increases the risk of progressive cognitive decline. Early prediction of MCI could be beneficial for identifying vulnerable individuals in the community and planning primary and secondary prevention to reduce the incidence of MCI.

DESIGN

A narrative review and cohort study.

SETTING AND PARTICIPANTS

We review the MCI prediction based on the assessment of sociodemographic factors. We included participants from 3 surveys: 8915 from wave 2011/2012 of the China Health and Retirement Longitudinal Study (CHARLS), 9765 from the 2011 Chinese Longitudinal Healthy Longevity Survey (CLHLS), and 1823 from the 2014 Rugao Longevity and Ageing Study (RuLAS).

METHODS

We searched in PubMed, Embase, and Web of Science Core Collection between January 1, 2019, and December 30, 2022. To construct the composite risk score, a multivariate Cox proportional hazards regression model was used. The performance of the score was assessed using receiver operating characteristic (ROC) curves. Furthermore, the composite risk score was validated in 2 longitudinal cohorts, CLHLS and RuLAS.

RESULTS

We concluded on 20 articles from 892 available. The results suggested that the previous models suffered from several defects, including overreliance on cross-sectional data, low predictive utility, inconvenient measurement, and inapplicability to developing countries. Our empirical work suggested that the area under the curve for a 5-year MCI prediction was 0.861 in CHARLS, 0.797 in CLHLS, and 0.823 in RuLAS. We designed a publicly available online tool for this composite risk score.

CONCLUSIONS AND IMPLICATIONS

Attention to these sociodemographic factors related to the incidence of MCI can be beneficially incorporated into the current work, which will set the stage for better early prediction of MCI before its incidence and for reducing the burden of the disease.

Lexical Speech Features of Spontaneous Speech in Older Persons With and Without Cognitive Impairment: Reliability Analysis

Background

Speech analysis data are promising digital biomarkers for the early detection of Alzheimer disease. However, despite its importance, very few studies in this area have examined whether older adults produce spontaneous speech with characteristics that are sufficiently consistent to be used as proxy markers of cognitive status.

Objective

This preliminary study seeks to investigate consistency across lexical characteristics of speech in older adults with and without cognitive impairment.

Methods

A total of 39 older adults from a larger, ongoing study (age: mean 81.1, SD 5.9 years) were included. Participants completed neuropsychological testing and both picture description tasks and expository tasks to elicit speech. Participants with T-scores of ≤40 on ≥2 cognitive tests were categorized as having mild cognitive impairment (MCI). Speech features were computed automatically by using Python and the Natural Language Toolkit.

Results

Reliability indices based on mean correlations for picture description tasks and expository tasks were similar in persons with and without MCI (with r ranging from 0.49 to 0.65 within tasks). Intraindividual variability was generally preserved across lexical speech features. Speech rate and filler rate were the most consistent indices for the cognitively intact group, and speech rate was the most consistent for the MCI group.

Conclusions

Our findings suggest that automatically calculated lexical properties of speech are consistent in older adults with varying levels of cognitive impairment. These findings encourage further investigation of the utility of speech analysis and other digital biomarkers for monitoring cognitive status over time.