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ORIGINAL RESEARCH article

Front. Aging Neurosci.
Sec. Alzheimer's Disease and Related Dementias
Volume 16 - 2024 | doi: 10.3389/fnagi.2024.1451326
This article is part of the Research Topic Blood, Cerebrospinal Fluid, and Vascular Biomarkers for Dementia View all 5 articles

Screening for Early Alzheimer's Disease: Enhancing Diagnosis with Linguistic Features and Biomarkers

Provisionally accepted
Chia-Ju Chou Chia-Ju Chou 1Chih-Ting Chang Chih-Ting Chang 2Ya-Ning Chang Ya-Ning Chang 3Chia-Ying Lee Chia-Ying Lee 4Yi-Fang Chuang Yi-Fang Chuang 5Yen- Ling Chiu Yen- Ling Chiu 6Wan-Lin Liang Wan-Lin Liang 1Yu-Ming Fan Yu-Ming Fan 7,8Yi-Chien Liu Yi-Chien Liu 1,7*
  • 1 Department of Neurology, Cardinal Tien Hospital, New Taipei City, Taiwan
  • 2 Department of Speech-Language Pathology and Audiology, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
  • 3 Miin Wu School of Computing, National Cheng Kung University, Tainan, Taiwan
  • 4 Academia Sinica, Taipei, Taipei County, Taiwan
  • 5 Institute of Public Health, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
  • 6 Department of Medical Research, Far Eastern Memorial Hospital (FEMH), New Taipei, Taiwan
  • 7 School of Medicine, Fu Jen Catholic University, New Taipei, Taiwan
  • 8 Department of Nuclear Medicine, Cardinal Tien Hospital, Taipei, Taiwan

The final, formatted version of the article will be published soon.

    Research has shown that speech analysis demonstrates sensitivity in detecting early Alzheimer's disease (AD), but the relation between linguistic features and cognitive tests or biomarkers remains unclear. This study aimed to investigate how linguistic features help identify cognitive impairments in patients in the early stages of AD.Method: This study analyzed connected speech from 80 participants and categorized the participants into early-AD and normal control (NC) groups. The participants underwent amyloid-β positron emission tomography scans, brain magnetic resonance imaging, and comprehensive neuropsychological testing. Participants' speech data from a picture description task were examined. A total of 15 linguistic features were analyzed to classify groups and predict cognitive performance.We found notable linguistic differences between the early-AD and NC groups in lexical diversity, syntactic complexity, and language disfluency. Using machine learning classifiers (SVM, KNN, and RF), we achieved up to 88% accuracy in distinguishing early-AD patients from normal controls, with mean length of utterance (MLU) and long pauses ratio (LPR) serving as core linguistic indicators. Moreover, the integration of linguistic indicators with biomarkers significantly improved predictive accuracy for AD. Regression analysis also highlighted crucial linguistic features, such as MLU, LPR, Type-to-Token ratio (TTR), and passive construction ratio (PCR), which were sensitive to changes in cognitive function.Findings support the efficacy of linguistic analysis as a screening tool for the early detection of AD and the assessment of subtle cognitive decline. Integrating linguistic features with biomarkers significantly improved diagnostic accuracy.

    Keywords: Alzheimer's disease, Linguistic features, cognitive impairment, amyloid-β, hippocampal volume, Speech analysis

    Received: 19 Jun 2024; Accepted: 11 Sep 2024.

    Copyright: © 2024 Chou, Chang, Chang, Lee, Chuang, Chiu, Liang, Fan and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yi-Chien Liu, Department of Neurology, Cardinal Tien Hospital, New Taipei City, Taiwan

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