Evidence map›Paper›PMID 38645255›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Impaired language in Alzheimer's disease: A comparison between English and Persian implicates content-word frequency rather than the noun-verb distinction.

Mahya Sanati, Sabereh Bayat, Mehrdad Mohammad Panahi, Amirhossein Khodadadi, Sahar Rezaee, Mahdieh Ghasimi, Sara Besharat, Zahra Mahboubi Fooladi, Mostafa Almasi Dooghaee, Morteza Sanei Taheri and 3 more

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 6 institutions in 2 countries.

Mahya SanatiAbrar Institute of Higher Education.
Sabereh BayatAzad University Science and Research Branch.
Mehrdad Mohammad PanahiInstitute for Cognitive Science Studies.
Amirhossein KhodadadiMashhad University of Medical Science.
Sahar RezaeeShahid Beheshti University of Medical Sciences.
Mahdieh GhasimiShahid Beheshti University of Medical Sciences.
Sara BesharatShahid Beheshti University of Medical Sciences.
Zahra Mahboubi FooladiShahid Beheshti University of Medical Sciences.
Mostafa Almasi DooghaeeAbrar Institute of Higher Education.
Morteza Sanei TaheriShahid Beheshti University of Medical Sciences.
Bradford C DickersonFrontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School.ORCID 0000-0002-5958-3445
Adele GoldbergPrinceton University.
Neguine RezaiiFrontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School.
Shahid Beheshti University of Medical Sciences · IRHarvard University · USInstitute for Cognitive Science Studies · IRIslamic Azad University, Science and Research Branch · IRMashhad University of Medical Sciences · IRPrinceton University · US

Funding

The Harvard Clinical and Translational Science CenterUL1TR002541 · NCATS · HARVARD MEDICAL SCHOOL · PI NADLER, LEE MARSHALL · 2018 to 2022
$93.0M
Toward Personalized Prognosis and Outcomes in Primary Progressive AphasiaRF1NS131395 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI DICKERSON, BRADFORD C · 2023 to 2023
$2.5M
Use of machine learning to quantify cognitive function in AD, FTD, and DLBR21AG073744 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI DICKERSON, BRADFORD C, WOLFF, PHILLIP MARK · 2021 to 2022
$443k
Computational psycholinguistic analysis of speech samples in PPA and AD and FTDR21DC019567 · NIDCD · MASSACHUSETTS GENERAL HOSPITAL · PI DICKERSON, BRADFORD C, WOLFF, PHILLIP MARK · 2022 to 2023
$443k
NCATS NIH HHS UL1 TR002541NIA NIH HHS R21 AG073744NIDCD NIH HHS R21 DC019567NINDS NIH HHS RF1 NS131395
6 · The paper itself

Abstract

This study challenges the conventional psycholinguistic view that the distinction between nouns and verbs is pivotal in understanding language impairments in neurological disorders. Traditional views link frontal brain region damage with verb processing deficits and posterior temporoparietal damage with noun difficulties. However, this perspective is contested by findings from patients with Alzheimer's disease (pwAD), who show impairments in both word classes despite their typical temporoparietal atrophy. Notably, pwAD tend to use semantically lighter verbs in their speech than healthy individuals. By examining English-speaking pwAD and comparing them with Persian-speaking pwAD, this research aims to demonstrate that language impairments in Alzheimer's disease (AD) stem from the distributional properties of words within a language rather than distinct neural processing networks for nouns and verbs. We propose that the primary deficit in AD language production is an overreliance on high-frequency words. English has a set of particularly high-frequency verbs that surpass most nouns in usage frequency. Since pwAD tend to use high-frequency words, the byproduct of this word distribution in the English language would be an over-usage of high-frequency verbs. In contrast, Persian features complex verbs with an overall distribution lacking extremely high-frequency verbs like those found in English. As a result, we hypothesize that Persian-speaking pwAD would not have a bias toward the overuse of high-frequency verbs. We analyzed language samples from 95 English-speaking pwAD and 91 healthy controls, along with 27 Persian-speaking pwAD and 27 healthy controls. Employing uniform automated natural language processing methods, we measured the usage rates of nouns, verbs, and word frequencies across both cohorts. Our findings showed that English-speaking pwAD use higher-frequency verbs than healthy individuals, a pattern not mirrored by Persian-speaking pwAD. Crucially, we found a significant interaction between the frequencies of verbs used by English and Persian speakers with and without AD. Moreover, regression models that treated noun and verb frequencies as separate predictors did not outperform models that considered overall word frequency alone in classifying AD. In conclusion, this study suggests that language abnormalities among English-speaking pwAD reflect the unique distributional properties of words in English rather than a universal noun-verb class distinction. Beyond offering a new understanding of language abnormalities in AD, the study highlights the critical need for further investigation across diverse languages to deepen our insight into the mechanisms of language impairments in neurological disorders.

Identifiers

PMID38645255
PMCPMC11030473
OpenAlexW4394675016

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.