Screening for early Alzheimer's disease: enhancing diagnosis with linguistic features and biomarkers

Chia-Ju Chou; Chih-Ting Chang; Ya-Ning Chang; Chia-Ying Lee; Yi-Fang Chuang; Yen-Ling Chiu; Wan-Lin Liang; Yu-Ming Fan; Yi-Chien Liu

doi:10.3389/fnagi.2024.1451326

Screening for early Alzheimer's disease: enhancing diagnosis with linguistic features and biomarkers

Front Aging Neurosci. 2024 Sep 23:16:1451326. doi: 10.3389/fnagi.2024.1451326. eCollection 2024.

Authors

Chia-Ju Chou¹, Chih-Ting Chang², Ya-Ning Chang³, Chia-Ying Lee⁴, Yi-Fang Chuang^{5

6

7}, Yen-Ling Chiu^{8

9

10}, Wan-Lin Liang¹, Yu-Ming Fan^{11

12}, Yi-Chien Liu^{1

11}

Affiliations

¹ Department of Neurology, Cardinal Tien Hospital, Taipei, 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, Taiwan.
⁵ Institute of Public Health, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
⁶ International Health Program, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
⁷ Health Innovation Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
⁸ Department of Medical Research, Far Eastern Memorial Hospital, Taipei, Taiwan.
⁹ Graduate Program in Biomedical Informatics and Graduate Institute of Medicine, Yuan Ze University, Taoyuan, Taiwan.
¹⁰ Graduate Institute of Clinical Medicine, National Taiwan University, Taipei, Taiwan.
¹¹ School of Medicine, Fu Jen Catholic University, Taipei, Taiwan.
¹² Department of Nuclear Medicine, Cardinal Tien Hospital, Taipei, Taiwan.

Abstract

Introduction: 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.

Results: 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.

Conclusion: 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; amyloid-β; cognitive impairment; hippocampal volume; linguistic features; speech analysis.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This project received funding from Academic Sinica (AS-HLGC-110-02) and the US National Academy of Medicine. Data collection and sharing were supported by Cardinal Tien Hospital (CTH-111A-2217) and the Taiwan National Science and Technology Council Grant (NSTC 113-2321-B-418-003).