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

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-ß 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.

White matter hyperintensities in cholinergic pathways may predict poorer responsiveness to acetylcholinesterase inhibitor treatment for Alzheimer's disease.

BACKGROUND: Acetylcholinesterase inhibitor (AChEI) drug regimens are the mainstay treatment options for patients with Alzheimer's disease (AD). Herein, We examined the association between clinical response to AChEI and white matter hyperintensities on magnetic resonance imaging (MRI) scan at baseline. METHODS: Between 2020 and 2021, we recruited 101 individuals with a clinical diagnosis of probable AD. Each participant underwent complete neuropsychological testing and 3T (Telsa) brain magnetic resonance imaging. Responsiveness to AChEI, as assessed after 12 months, was designated as less than two points of regression in Mini-Mental State Examination scores (MMSE) and stable clinical dementia rating scale. We also evaluated MRI images by examining scores on the Cholinergic Pathways Hyperintensities Scale (CHIPS), Fazekas scale, and medial temporal atrophy (MTA) scale. RESULTS: In our cohort, 52 patients (51.4%) were classified as responders. We observed significantly higher CHIPS scores in the nonresponder group (21.1 ± 12.9 vs. 14.9 ± 9.2, P = 0.007). Age at baseline, education level, sex, Clinical Dementia Rating sum of boxes scores, and three neuroimaging parameters were tested in regression models. Only CHIPS scores predicted clinical response to AChEI treatment. CONCLUSION: WMHs in the cholinergic pathways, not diffuse white matter lesions or hippocampal atrophy, correlated with poorer responsiveness to AChEI treatment. Therefore, further investigation into the role of the cholinergic pathway in AD is warranted.