Language abnormalities in Alzheimer's disease indicate reduced informativeness.

Autor: Bayat S; Azad University Science and Research Branch, Sattari Highway, Tehran, Iran., Sanati M; Abrar Institute of Higher Education, Khorasan Square, Tehran, Iran., Mohammad-Panahi M; Institute for Cognitive Science Studies, Chamran Blvd, Tehran, Iran., Khodadadi A; Mashhad University of Medical Science, Vakil Abad Blvd, Mashhad, Iran., Ghasimi M; Shahid Beheshti University of Medical Sciences, Velenjak, Daneshjoo Blvd, Tehran, Iran., Rezaee S; Shahid Beheshti University of Medical Sciences, Velenjak, Daneshjoo Blvd, Tehran, Iran., Besharat S; Shahid Beheshti University of Medical Sciences, Velenjak, Daneshjoo Blvd, Tehran, Iran., Mahboubi-Fooladi Z; Shahid Beheshti University of Medical Sciences, Velenjak, Daneshjoo Blvd, Tehran, Iran., Almasi-Dooghaee M; Iran University of Medical Sciences, Hemmat Highway, Tehran, Iran., Sanei-Taheri M; Shahid Beheshti University of Medical Sciences, Velenjak, Daneshjoo Blvd, Tehran, Iran., Dickerson BC; Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, USA.; Athinoula A. Martinos Center for Biomedical Imaging, 149 13th Street, Boston, Massachusetts, USA.; Massachusetts Alzheimer's Disease Research Center, Boston, Massachusetts, 02114, USA., Rezaii N; Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, USA.; Athinoula A. Martinos Center for Biomedical Imaging, 149 13th Street, Boston, Massachusetts, USA.
Jazyk: angličtina
Zdroj: Annals of clinical and translational neurology [Ann Clin Transl Neurol] 2024 Nov; Vol. 11 (11), pp. 2946-2957. Date of Electronic Publication: 2024 Sep 18.
DOI: 10.1002/acn3.52205
Abstrakt: Objective: This study aims to elucidate the cognitive underpinnings of language abnormalities in Alzheimer's Disease (AD) using a computational cross-linguistic approach and ultimately enhance the understanding and diagnostic accuracy of the disease.
Methods: Computational analyses were conducted on language samples of 156 English and 50 Persian speakers, comprising both AD patients and healthy controls, to extract language indicators of AD. Furthermore, we introduced a machine learning-based metric, Language Informativeness Index (LII), to quantify empty speech.
Results: Despite considerable disparities in surface structures between the two languages, we observed consistency across language indicators of AD in both English and Persian. Notably, indicators of AD in English resulted in a classification accuracy of 90% in classifying AD in Persian. The substantial degree of transferability suggests that the language abnormalities of AD do not tightly link to the surface structures specific to English. Subsequently, we posited that these abnormalities stem from impairments in a more universal aspect of language production: the ability to generate informative messages independent of the language spoken. Consistent with this hypothesis, we found significant correlations between language indicators of AD and empty speech in both English and Persian.
Interpretation: The findings of this study suggest that language impairments in AD arise from a deficit in a universal aspect of message formation rather than from the breakdown of language-specific morphosyntactic structures. Beyond enhancing our understanding of the psycholinguistic deficits of AD, our approach fosters the development of diagnostic tools across various languages, enhancing health equity and biocultural diversity.
(© 2024 The Author(s). Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)
Databáze: MEDLINE
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