Using Automatic Assessment of Speech Production to Predict Current and Future Cognitive Function 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.

Distinguishable features of spontaneous speech in Alzheimer's clinical syndrome and healthy controls

There is growing evidence that subtle changes in spontaneous speech may reflect early pathological changes in cognitive function. Recent work has found that lexical-semantic features of spontaneous speech predict cognitive dysfunction in individuals with mild cognitive impairment (MCI). The current study assessed whether Ostrand and Gunstad's (OG) lexical-semantic features extend to predicting cognitive status in a sample of individuals with Alzheimer's clinical syndrome (ACS) and healthy controls. Four additional (New) speech indices shown to be important in language processing research were also explored in this sample to extend prior work. Speech transcripts of the Cookie Theft Task from 81 individuals with ACS (Mage = 72.7 years, SD = 8.80, 70.4% female) and 61 healthy controls (HC) (Mage = 63.9 years, SD = 8.52, 62.3% female) from Dementia Bank were analyzed. Random forest and logistic machine learning techniques examined whether subject-level lexical-semantic features could be used to accurately discriminate those with ACS from HC. Results showed that logistic models with the New lexical-semantic features obtained good classification accuracy (78.4%), but the OG features had wider success across machine learning model types. In terms of sensitivity and specificity, the random forest model trained on the OG features was the most balanced. Findings from the current study suggest that features of spontaneous speech used to predict MCI may also distinguish between individuals with ACS and healthy controls. Future work should evaluate these lexical-semantic features in pre-clinical persons to further explore their potential to assist with early detection through speech analysis.

Language switching and speaking a nondominant language challenge executive control: Preliminary data for novel behavioral markers of Alzheimer's risk in Spanish-English bilinguals

OBJECTIVE

The present study explored psycholinguistic analysis of spoken responses produced in a structured interview and cued linguistic and nonlinguistic task switching as possible novel markers of Alzheimer's disease (AD) risk in Spanish-English bilinguals.

METHOD

Nineteen Spanish-English bilinguals completed an Oral Proficiency Interview (OPI) in both languages, cued-switching tasks, and a battery of traditional neuropsychological tests (in a separate testing session). All were cognitively healthy at the time of testing, but eight decliners were later diagnosed with AD (on average 4.5 years after testing; SD = 2.3), while 11 controls remained cognitively healthy.

RESULTS

Past studies showed picture naming was more sensitive to AD in the dominant than in the nondominant language, but we found the opposite for a composite measure of spoken utterances produced in the OPI that included revisions, repetitions, and filled pauses (RRFPs), which were especially sensitive to AD risk in the nondominant language. Errors produced on language switch trials best discriminated decliners from controls (in receiver operating characteristic curves), and though the nonlinguistic switching task was also sensitive to AD risk, it elicited more errors overall and was also negatively affected by increased age and low education level.

CONCLUSIONS

Speaking a nondominant language and errors in cued language switching provided sensitive and specific markers of pending cognitive decline and AD risk in bilinguals. These measures may reflect early decline in executive control abilities that are needed to plan and monitor the production of connected speech and to manage competition for selection between languages. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Design and Feasibility Analysis of a Smartphone-Based Digital Cognitive Assessment Study in the Framingham Heart Study

BACKGROUND

Smartphone-based digital technology is increasingly being recognized as a cost-effective, scalable, and noninvasive method of collecting longitudinal cognitive and behavioral data. Accordingly, a state-of-the-art 3-year longitudinal project focused on collecting multimodal digital data for early detection of cognitive impairment was developed.

METHODS AND RESULTS

A smartphone application collected 2 modalities of cognitive data, digital voice and screen-based behaviors, from the FHS (Framingham Heart Study) multigenerational Generation 2 (Gen 2) and Generation 3 (Gen 3) cohorts. To understand the feasibility of conducting a smartphone-based study, participants completed a series of questions about their smartphone and app use, as well as sensory and environmental factors that they encountered while completing the tasks on the app. Baseline data collected to date were from 537 participants (mean age=66.6 years, SD=7.0; 58.47% female). Across the younger participants from the Gen 3 cohort (n=455; mean age=60.8 years, SD=8.2; 59.12% female) and older participants from the Gen 2 cohort (n=82; mean age=74.2 years, SD=5.8; 54.88% female), an average of 76% participants agreed or strongly agreed that they felt confident about using the app, 77% on average agreed or strongly agreed that they were able to use the app on their own, and 81% on average rated the app as easy to use.

CONCLUSIONS

Based on participant ratings, the study findings are promising. At baseline, the majority of participants are able to complete the app-related tasks, follow the instructions, and encounter minimal barriers to completing the tasks independently. These data provide evidence that designing and collecting smartphone application data in an unsupervised, remote, and naturalistic setting in a large, community-based population is feasible.

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.

AI-based assessments of speech and language impairments in dementia

Recent advancements in the artificial intelligence (AI) domain have revolutionized the early detection of cognitive impairments associated with dementia. This has motivated clinicians to use AI-powered dementia detection systems, particularly systems developed based on individuals' and patients' speech and language, for a quick and accurate identification of patients with dementia. This paper reviews articles about developing assessment tools using machine learning and deep learning algorithms trained by vocal and textual datasets.

An explainable machine learning model of cognitive decline derived from speech

INTRODUCTION

Traditional Alzheimer's disease (AD) and mild cognitive impairment (MCI) screening lacks the sensitivity and timeliness required to detect subtle indicators of cognitive decline. Multimodal artificial intelligence technologies using only speech data promise improved detection of neurodegenerative disorders.

METHODS

Speech collected over the telephone from 91 older participants who were cognitively healthy (n = 29) or had diagnoses of AD (n = 30) or amnestic MCI (aMCI; n = 32) was analyzed with multimodal natural language and speech processing methods. An explainable ensemble decision tree classifier for the multiclass prediction of cognitive decline was created.

RESULTS

This approach was 75% accurate overall-an improvement over traditional speech-based screening tools and a unimodal language-based model. We include a dashboard for the examination of the results, allowing for novel ways of interpreting such data.

DISCUSSION

This work provides a foundation for a meaningful change in medicine as clinical translation, scalability, and user friendliness were core to the methodologies.

Highlights

Remote assessments and artificial intelligence (AI) models allow greater access to cognitive decline screening.Speech impairments differ significantly between mild AD, amnestic mild cognitive impairment (aMCI), and healthy controls.AI predictions of cognitive decline are more accurate than experts and standard tools.The AI model was 75% accurate in classifying mild AD, aMCI, and healthy controls.

Development of digital voice biomarkers and associations with cognition, cerebrospinal biomarkers, and neural representation in early Alzheimer's disease

Introduction

Advances in natural language processing (NLP), speech recognition, and machine learning (ML) allow the exploration of linguistic and acoustic changes previously difficult to measure. We developed processes for deriving lexical-semantic and acoustic measures as Alzheimer's disease (AD) digital voice biomarkers.

Methods

We collected connected speech, neuropsychological, neuroimaging, and cerebrospinal fluid (CSF) AD biomarker data from 92 cognitively unimpaired (40 Aβ+) and 114 impaired (63 Aβ+) participants. Acoustic and lexical-semantic features were derived from audio recordings using ML approaches.

Results

Lexical-semantic (area under the curve [AUC] = 0.80) and acoustic (AUC = 0.77) scores demonstrated higher diagnostic performance for detecting MCI compared to Boston Naming Test (AUC = 0.66). Only lexical-semantic scores detected amyloid-β status (p = 0.0003). Acoustic scores associated with hippocampal volume (p = 0.017) while lexical-semantic scores associated with CSF amyloid-β (p = 0.007). Both measures were significantly associated with 2-year disease progression.

Discussion

These preliminary findings suggest that derived digital biomarkers may identify cognitive impairment in preclinical and prodromal AD, and may predict disease progression.

Highlights

This study derived lexical-semantic and acoustics features as Alzheimer's disease (AD) digital biomarkers.These features were derived from audio recordings using machine learning approaches.Voice biomarkers detected cognitive impairment and amyloid-β status in early stages of AD.Voice biomarkers may predict Alzheimer's disease progression.These markers significantly mapped to functional connectivity in AD-susceptible brain regions.

An Analysis of Right Hemisphere Stroke Discourse in the Modern Cookie Theft Picture

PURPOSE

Adults with right hemisphere damage demonstrate differences in connected speech compared to controls, but systematic, quantitative methods to capture these differences are lacking. The current study aimed to (a) investigate if measures using the Modern Cookie Theft picture description would identify discourse differences in acute right hemisphere stroke, and (b) examine if discourse differences were associated with documented cognitive impairment.

METHOD

Eighty-four participants completed the Modern Cookie Theft picture description within 5 days of right hemisphere stroke. Descriptions were analyzed for multiple microlinguistic characteristics. Medical charts were retrospectively reviewed for documented presence of cognitive impairment.

RESULTS

Individuals with acute right hemisphere stroke produced fewer content units, total syllables, and lower left-right content unit ratios compared to controls, indicating a paucity of informativeness. Presence of cognitive impairment was associated with fewer content units produced.

CONCLUSIONS

Multiple measures of microlinguistic discourse characteristics differentiated adults with right hemisphere stroke from controls, highlighting variations in both the quantity and quality of connected speech. Findings continue to underscore the contribution and correlation between cognitive skills and discourse performance. Future work is needed to assess the relationship between particular cognitive domains and discourse production as well as to investigate longitudinal changes to discourse production during stroke recovery.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.20778541.

Language performance as a prognostic factor for developing Alzheimer's clinical syndrome and mild cognitive impairment: Results from the population-based HELIAD cohort

OBJECTIVES

There is limited research on the prognostic value of language tasks regarding mild cognitive impairment (MCI) and Alzheimer's clinical syndrome (ACS) development in the cognitively normal (CN) elderly, as well as MCI to ACS conversion.

METHODS

Participants were drawn from the population-based Hellenic Longitudinal Investigation of Aging and Diet (HELIAD) cohort. Language performance was evaluated via verbal fluency [semantic (SVF) and phonemic (PVF)], confrontation naming [Boston Naming Test short form (BNTsf)], verbal comprehension, and repetition tasks. An additional language index was estimated using both verbal fluency tasks: SVF-PVF discrepancy. Cox proportional hazards analyses adjusted for important sociodemographic parameters (age, sex, education, main occupation, and socioeconomic status) and global cognitive status [Mini Mental State Examination score (MMSE)] were performed.

RESULTS

A total of 959 CN and 118 MCI older (>64 years) individuals had follow-up investigations after a mean of ∼3 years. Regarding the CN group, each standard deviation increase in the composite language score reduced the risk of ACS and MCI by 49% (8-72%) and 32% (8-50%), respectively; better SVF and BNTsf performance were also independently associated with reduced risk of ACS and MCI. On the other hand, using the smaller MCI participant set, no language measurement was related to the risk of MCI to ACS conversion.

CONCLUSIONS

Impaired language performance is associated with elevated risk of ACS and MCI development. Better SVF and BNTsf performance are associated with reduced risk of ACS and MCI in CN individuals, independent of age, sex, education, main occupation, socioeconomic status, and MMSE scores at baseline.

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

The population of older adults is growing dramatically and, with it comes increased prevalence of neurological disorders, including Alzheimer's disease (AD). Though existing cognitive screening tests can aid early detection of cognitive decline, these methods are limited in their sensitivity and require trained administrators. The current study sought to determine whether it is possible to identify persons with mild cognitive impairment (MCI) using automated analysis of spontaneous speech. Participants completed a brief neuropsychological test battery and a spontaneous speech task. MCI was classified using established research criteria, and lexical-semantic features were calculated from spontaneous speech. Logistic regression analyses compared the predictive ability of a commonly-used cognitive screening instrument (the Modified Mini Mental Status Exam, 3MS) and speech indices for MCI classification. Testing against constant-only logistic regression models showed that both the 3MS [χ2(1) = 6.18, p = .013; AIC = 41.46] and speech indices [χ2(16) = 32.42, p = .009; AIC = 108.41] were able to predict MCI status. Follow-up testing revealed the full speech model better predicted MCI status than did 3MS (p = .049). In combination, the current findings suggest that spontaneous speech may have value as a potential screening measure for the identification of cognitive deficits, though confirmation is needed in larger, prospective studies.

Breaking the flow of thought: Increase of empty pauses in the connected speech of people with mild and moderate Alzheimer's disease

INTRODUCTION

The profile of spontaneous speech in Alzheimer's disease (AD) includes increased pausing as a window into cognitive decline. We here aimed to further characterize the pausing profile of AD by linking pauses to the syntactic positions in which they appear and disease progression.

METHODS

Speech was obtained through a picture description task, thus minimizing demands on episodic memory (EM), from a group of mild (N = 21) and moderate AD (N = 19), and healthy elderly controls (N = 40). Pauses were sub-indexed according to whether they occurred within-clauses, clause-initially, or utterance-initially, and whether they preceded nouns, verbs, or adjectives/adverbs, when occurring within-clauses. Additionally, relations to verbal fluency (VF) measures at the single-word level were explored.

RESULTS

Pause rate but not duration distinguished controls from both AD groups, while fillers did not distinguish any groups. The analysis by syntactic position revealed a highly differentiated picture, with largest effect sizes of significant group differences seen in the utterance-initial pause rate. The two AD groups patterned differently when compared to controls, while none of the measures differentiated the AD groups. Specifically, moderate but not mild AD differed from controls in clause-initial pauses, while mild but not moderate AD differed from controls in within-clause positions. At the within-clause level, the effect dividing controls from mild-AD was specifically driven by pauses ahead of nouns. A significant negative correlation emerged between pausing rate in spontaneous speech and VF measures in the mild-AD group only.

CONCLUSIONS

Increased empty (non-filled) pauses in AD are not confined to pauses in within-clause positions, which are most directly related to problems in the retrieval of words. Even in early disease stages, where these within-clause pause effects are seen, they are confined to nouns, revealing a grammatically specific problem possibly related to the referencing of objects. At all disease stages, pauses increase in utterance-sized units of structure, indicating progressive problems in the creative configuration of complete thoughts.