Spontaneous spatial information provided by dementia patients and elderly controls in narratives

Bosse, Solveig

doi:10.3765/plsa.v4i1.4463

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…A recent study shows that spatial preposition can reflect spatial neglect in AD patients with reduced awareness of one side of the visual field due to brain damage [37]. Prepositions such as “into” and “from” indicate directional or dynamic spatial information and can reflect deficits in spatial cognition of language [38].…”

Section: Discussionmentioning

confidence: 99%

Explainable Alzheimer’s Disease Detection Using Linguistic Features from Automatic Speech Recognition

Tang¹,

Zhang²,

Feng³

et al. 2023

Dement Geriatr Cogn Disord

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Introduction: Alzheimer’s disease (AD) is the most prevalent type of dementia and can cause abnormal cognitive function and progressive loss of essential life skills. Early screening is thus necessary for the prevention and intervention of AD. Speech dysfunction is an early onset symptom of AD patients. Recent studies have demonstrated the promise of automated acoustic assessment using acoustic or linguistic features extracted from speech. However, most previous studies have relied on manual transcription of text to extract linguistic features, which weakens the efficiency of automated assessment. The present study thus investigates the effectiveness of automatic speech recognition (ASR) in building an end-to-end automated speech analysis model for AD detection. Methods: We implemented three publicly available ASR engines and compared the classification performance using the ADReSS-IS2020 dataset. Besides, the SHapley Additive exPlanations algorithm was then used to identify critical features that contributed most to model performance. Results: Three automatic transcription tools obtained mean word error rate texts of 32%, 43%, and 40%, respectively. These automated texts achieved similar or even better results than manual texts in model performance for detecting dementia, achieving classification accuracies of 89.58%, 83.33%, and 81.25%, respectively. Conclusion: Our best model, using ensemble learning, is comparable to the state-of-the-art manual transcription-based methods, suggesting the possibility of an end-to-end medical assistance system for AD detection with ASR engines. Moreover, the critical linguistic features might provide insight into further studies on the mechanism of AD.

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Section: Discussionmentioning

confidence: 99%

Explainable Alzheimer’s Disease Detection Using Linguistic Features from Automatic Speech Recognition

Tang¹,

Zhang²,

Feng³

et al. 2023

Dement Geriatr Cogn Disord

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“…Preposition deficits for AD have been found in Brazilian Portuguese (Alegria et al, 2013 ). Another study—arguing that spontaneous speech mirroring the decline of effective spatial reasoning in language production—found that AD and HC used the same number of locative/stative prepositions (e.g., in, on, and at) but found significant differences for directional/dynamic prepositions (e.g., into, onto, from, and to; Bosse, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

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

Lindsay

Tröger

König

2021

Front. Aging Neurosci.

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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.

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“…Preposition deficits for AD have been found in Brazilian Portuguese (Alegria et al, 2013). Another study-arguing that spontaneous speech mirroring the decline of effective spatial reasoning in language production-found that AD and HC used the same number of locative/stative prepositions (e.g., in, on, and at) but found significant differences for directional/dynamic prepositions (e.g., into, onto, from, and to; Bosse, 2019).…”

Section: Syntactic Featuresmentioning

confidence: 99%

Alzheimer's Dementia Recognition Through Spontaneous Speech

2021

Frontiers Research Topics

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Frontiers is more than just an open-access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers Journal SeriesThe Frontiers Journal Series is a multi-tier and interdisciplinary set of open-access, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers Journal Series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to QualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation.

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Spontaneous spatial information provided by dementia patients and elderly controls in narratives

Cited by 4 publications

References 22 publications

Explainable Alzheimer’s Disease Detection Using Linguistic Features from Automatic Speech Recognition

Explainable Alzheimer’s Disease Detection Using Linguistic Features from Automatic Speech Recognition

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

Alzheimer's Dementia Recognition Through Spontaneous Speech

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