{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T03:34:04Z","timestamp":1793763244034,"version":"4.6.1"},"reference-count":49,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T00:00:00Z","timestamp":1727049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Research has shown that speech analysis demonstrates sensitivity in detecting early Alzheimer\u2019s 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>This study analyzed connected speech from 80 participants and categorized the participants into early-AD and normal control (NC) groups. The participants underwent amyloid-<jats:italic>\u03b2<\/jats:italic> positron emission tomography scans, brain magnetic resonance imaging, and comprehensive neuropsychological testing. Participants\u2019 speech data from a picture description task were examined. A total of 15 linguistic features were analyzed to classify groups and predict cognitive performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnagi.2024.1451326","type":"journal-article","created":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T04:28:28Z","timestamp":1727065708000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["Screening for early Alzheimer\u2019s disease: enhancing diagnosis with linguistic features and biomarkers"],"prefix":"10.3389","volume":"16","author":[{"given":"Chia-Ju","family":"Chou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chih-Ting","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya-Ning","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chia-Ying","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Fang","family":"Chuang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yen-Ling","family":"Chiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wan-Lin","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Ming","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Chien","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,9,23]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1037\/a0031288","article-title":"Semantic processing in connected speech at a uniformly early stage of autopsy-confirmed Alzheimer\u2019s disease","volume":"27","author":"Ahmed","year":"","journal-title":"Neuropsychology"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"3727","DOI":"10.1093\/brain\/awt269","article-title":"Connected speech as a marker of disease progression in autopsy-proven Alzheimer\u2019s disease","volume":"136","author":"Ahmed","year":"","journal-title":"Brain"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"CD010783","DOI":"10.1002\/14651858.CD010783.pub3","article-title":"Mini-mental state examination (MMSE) for the early detection of dementia in people with mild cognitive impairment (MCI)","volume":"2021","author":"Arevalo-Rodriguez","year":"2021","journal-title":"Cochrane Database Syst. 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