Language abnormalities in Alzheimer's disease indicate reduced informativeness
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...
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Published in | Annals of clinical and translational neurology Vol. 11; no. 11; pp. 2946 - 2957 |
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Main Authors | , , , , , , , , , , , |
Format | Journal Article |
Language | English |
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United States
John Wiley & Sons, Inc
01.11.2024
John Wiley and Sons Inc Wiley |
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Abstract | 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. |
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AbstractList | Abstract 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. 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.OBJECTIVEThis 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.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.METHODSComputational 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.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.RESULTSDespite 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.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.INTERPRETATIONThe 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. 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. 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. 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. 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. 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. 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. |
Author | Bayat, Sabereh Mahboubi‐Fooladi, Zahra Sanei‐Taheri, Morteza Ghasimi, Mahdieh Almasi‐Dooghaee, Mostafa Khodadadi, Amirhossein Sanati, Mahya Mohammad‐Panahi, Mehrdad Besharat, Sara Dickerson, Bradford C. Rezaii, Neguine Rezaee, Sahar |
AuthorAffiliation | 6 Iran University of Medical Sciences Hemmat Highway Tehran Iran 1 Azad University Science and Research Branch Sattari Highway Tehran Iran 7 Massachusetts General Hospital, Harvard Medical School 55 Fruit Street Boston USA 9 Massachusetts Alzheimer's Disease Research Center Boston Massachusetts 02114 USA 5 Shahid Beheshti University of Medical Sciences Velenjak, Daneshjoo Blvd Tehran Iran 3 Institute for Cognitive Science Studies Chamran Blvd Tehran Iran 8 Athinoula A. Martinos Center for Biomedical Imaging 149 13th Street Boston Massachusetts USA 2 Abrar Institute of Higher Education Khorasan Square Tehran Iran 4 Mashhad University of Medical Science Vakil Abad Blvd Mashhad Iran |
AuthorAffiliation_xml | – name: 4 Mashhad University of Medical Science Vakil Abad Blvd Mashhad Iran – name: 9 Massachusetts Alzheimer's Disease Research Center Boston Massachusetts 02114 USA – name: 1 Azad University Science and Research Branch Sattari Highway Tehran Iran – name: 2 Abrar Institute of Higher Education Khorasan Square Tehran Iran – name: 6 Iran University of Medical Sciences Hemmat Highway Tehran Iran – name: 5 Shahid Beheshti University of Medical Sciences Velenjak, Daneshjoo Blvd Tehran Iran – name: 7 Massachusetts General Hospital, Harvard Medical School 55 Fruit Street Boston USA – name: 8 Athinoula A. Martinos Center for Biomedical Imaging 149 13th Street Boston Massachusetts USA – name: 3 Institute for Cognitive Science Studies Chamran Blvd Tehran Iran |
Author_xml | – sequence: 1 givenname: Sabereh surname: Bayat fullname: Bayat, Sabereh organization: Azad University Science and Research Branch – sequence: 2 givenname: Mahya surname: Sanati fullname: Sanati, Mahya organization: Abrar Institute of Higher Education – sequence: 3 givenname: Mehrdad surname: Mohammad‐Panahi fullname: Mohammad‐Panahi, Mehrdad organization: Institute for Cognitive Science Studies – sequence: 4 givenname: Amirhossein surname: Khodadadi fullname: Khodadadi, Amirhossein organization: Mashhad University of Medical Science – sequence: 5 givenname: Mahdieh surname: Ghasimi fullname: Ghasimi, Mahdieh organization: Shahid Beheshti University of Medical Sciences – sequence: 6 givenname: Sahar surname: Rezaee fullname: Rezaee, Sahar organization: Shahid Beheshti University of Medical Sciences – sequence: 7 givenname: Sara surname: Besharat fullname: Besharat, Sara organization: Shahid Beheshti University of Medical Sciences – sequence: 8 givenname: Zahra surname: Mahboubi‐Fooladi fullname: Mahboubi‐Fooladi, Zahra organization: Shahid Beheshti University of Medical Sciences – sequence: 9 givenname: Mostafa surname: Almasi‐Dooghaee fullname: Almasi‐Dooghaee, Mostafa organization: Iran University of Medical Sciences – sequence: 10 givenname: Morteza surname: Sanei‐Taheri fullname: Sanei‐Taheri, Morteza organization: Shahid Beheshti University of Medical Sciences – sequence: 11 givenname: Bradford C. surname: Dickerson fullname: Dickerson, Bradford C. organization: Massachusetts Alzheimer's Disease Research Center – sequence: 12 givenname: Neguine orcidid: 0000-0002-8967-9237 surname: Rezaii fullname: Rezaii, Neguine email: nrezaii@mgh.harvard.edu organization: Athinoula A. Martinos Center for Biomedical Imaging |
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Copyright | 2024 The Author(s). published by Wiley Periodicals LLC on behalf of American Neurological Association. 2024 The Author(s). Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association. 2024. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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Notes | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10984049/ Funding Information The work has been uploaded to medRxiv as preprint . This work was supported by Alzheimer's Association Clinician Scientist Fellowship (AACSF) 2022A015154 and MGH Screening Technologies in Primary Care Innovation Fund (PCIF) 2023A063002, as well as National Institutes of Health grants R21 DC019567, R21 AG073744, and R01 NS131395. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 The work has been uploaded to medRxiv as preprint: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10984049/. |
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Snippet | Objective
This study aims to elucidate the cognitive underpinnings of language abnormalities in Alzheimer's Disease (AD) using a computational cross‐linguistic... This study aims to elucidate the cognitive underpinnings of language abnormalities in Alzheimer's Disease (AD) using a computational cross-linguistic approach... Objective This study aims to elucidate the cognitive underpinnings of language abnormalities in Alzheimer's Disease (AD) using a computational cross‐linguistic... Abstract Objective This study aims to elucidate the cognitive underpinnings of language abnormalities in Alzheimer's Disease (AD) using a computational... |
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SubjectTerms | Alzheimer's disease Automation Disease Hypotheses Language Large language models Neuropsychology Theft |
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Title | Language abnormalities in Alzheimer's disease indicate reduced informativeness |
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