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Language Impairment in Alzheimer's Disease-Robust and Explainable Evidence for AD-Related Deterioration of Spontaneous Speech Through Multilingual Machine Learning.

ABSTRACT: Alzheimer's disease (AD) is a pervasive neurodegenerative disease that affects millions worldwide and is most prominently associated with broad cognitive decline, including language impairment. Picture description tasks are routinely used to monitor language impairment in AD. Due to the high amount of manual resources needed for an in-depth analysis of thereby-produced spontaneous speech, advanced natural language processing (NLP) combined with machine learning (ML) represents a promising opportunity. In this applied research field though, NLP and ML methodology do not necessarily ensure robust clinically actionable insights into cognitive language impairment in AD and additional precautions must be taken to ensure clinical-validity and generalizability of results. In this study, we add generalizability through multilingual feature statistics to computational approaches for the detection of language impairment in AD. We include 154 participants (78 healthy subjects, 76 patients with AD) from two different languages (106 English speaking and 47 French speaking). Each participant completed a picture description task, in addition to a battery of neuropsychological tests. Each response was recorded and manually transcribed. From this, task-specific, semantic, syntactic and paralinguistic features are extracted using NLP resources. Using inferential statistics, we determined language features, excluding task specific features, that are significant in both languages and therefore represent "generalizable" signs for cognitive language impairment in AD. In a second step, we evaluated all features as well as the generalizable ones for English, French and both languages in a binary discrimination ML scenario (AD vs. healthy) using a variety of classifiers. The generalizable language feature set outperforms the all language feature set in English, French and the multilingual scenarios. Semantic features are the most generalizable while paralinguistic features show no overlap between languages. The multilingual model shows an equal distribution of error in both English and French. By leveraging multilingual statistics combined with a theory-driven approach, we identify AD-related language impairment that generalizes beyond a single corpus or language to model language impairment as a clinically-relevant cognitive symptom. We find a primary impairment in semantics in addition to mild syntactic impairment, possibly confounded by additional impaired cognitive functions.

SUBMITTER: Lindsay H

PROVIDER: S-EPMC8170097 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundLanguage impairment is an important marker of neurodegenerative disorders. Despite this, there is no universal system of terminology used to describe these impairments and large inter-rater variability can exist between clinicians assessing language. The use of natural language processing (NLP) and automated speech analysis (ASA) is emerging as a novel and potentially more objective method to assess language in individuals with mild cognitive impairment (MCI) and Alzheimer's dementia (AD). No studies have analyzed how variables extracted through NLP and ASA might also be correlated to language impairments identified by a clinician.MethodsAudio recordings (n=30) from participants with AD, MCI, and controls were rated by clinicians for word-finding difficulty, incoherence, perseveration, and errors in speech. Speech recordings were also transcribed, and linguistic and acoustic variables were extracted through NLP and ASA. Correlations between clinician-rated speech characteristics and the variables were compared using Spearman's correlation. Exploratory factor analysis was applied to find common factors between variables for each speech characteristic.ResultsClinician agreement was high in three of the four speech characteristics: word-finding difficulty (ICC = 0.92, p<0.001), incoherence (ICC = 0.91, p<0.001), and perseveration (ICC = 0.88, p<0.001). Word-finding difficulty and incoherence were useful constructs at distinguishing MCI and AD from controls, while perseveration and speech errors were less relevant. Word-finding difficulty as a construct was explained by three factors, including number and duration of pauses, word duration, and syntactic complexity. Incoherence was explained by two factors, including increased average word duration, use of past tense, and changes in age of acquisition, and more negative valence.ConclusionsVariables extracted through automated acoustic and linguistic analysis of MCI and AD speech were significantly correlated with clinician ratings of speech and language characteristics. Our results suggest that correlating NLP and ASA with clinician observations is an objective and novel approach to measuring speech and language changes in neurodegenerative disorders.

Project description:Purpose Differences across language environments of prelingually deaf children who receive cochlear implants (CIs) may affect language acquisition; yet, whether mothers show individual differences in how they modify infant-directed (ID) compared with adult-directed (AD) speech has seldom been studied. This study assessed individual differences in how mothers realized speech modifications in ID register and whether these predicted differences in language outcomes for children with CIs. Method Participants were 36 dyads of mothers and their children aged 0;8-2;5 (years;months) at the time of CI implantation. Mothers' spontaneous speech was recorded in a lab setting in ID or AD conditions before ~15 months postimplantation. Mothers' speech samples were characterized for acoustic-phonetic and lexical properties established as canonical indices of ID speech to typically hearing infants, such as vowel space area differences, fundamental frequency variability, and speech rate. Children with CIs completed longitudinal administrations of one or more standardized language assessment instruments at variable intervals from 6 months to 9.5 years postimplantation. Standardized scores on assessments administered longitudinally were used to calculate linear regressions, which gave rise to predicted language scores for children at 2 years postimplantation and language growth over 2-year intervals. Results Mothers showed individual differences in how they modified speech in ID versus AD registers. Crucially, these individual differences significantly predicted differences in estimated language outcomes at 2 years postimplantation in children with CIs. Maternal speech variation in lexical quantity and vowel space area differences across ID and AD registers most frequently predicted estimates of language attainment in children with CIs, whereas prosodic differences played a minor role. Conclusion Results support that caregiver language behaviors play a substantial role in explaining variability in language attainment in children receiving CIs. Supplemental Material https://doi.org/10.23641/asha.12560147.

Dataset Information

Language Impairment in Alzheimer's Disease-Robust and Explainable Evidence for AD-Related Deterioration of Spontaneous Speech Through Multilingual Machine Learning.

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