Year: 2023
Type: article
Abstract: Introduction: Although brain magnetic resonance imaging (MRI) is a valuable tool for investigating structural changes in the brain associated with neurodegeneration, the development of non-invasive and cost-effective alternative methods for detecting early cognitive impairment is crucial. The human voice has been increasingly used as an indicator for effectively detecting cognitive disorders, but it remains unclear whether acoustic features are associated with structural neuroimaging. Methods: This study aims to investigate the association between acoustic features and brain volume and compare the predictive power of each for mild cognitive impairment (MCI) in a large community-based population. The study included participants from the Framingham Heart Study (FHS) who had at least one voice recording and an MRI scan. Sixty-five acoustic features were extracted with the OpenSMILE software (v2.1.3) from each voice recording. Nine MRI measures were derived according to the FHS MRI protocol. We examined the associations between acoustic features and MRI measures using linear regression models adjusted for age, sex, and education. Acoustic composite scores were generated by combining acoustic features significantly associated with MRI measures. The MCI prediction ability of acoustic composite scores and MRI measures were compared by building random forest models and calculating the mean area under the receiver operating characteristic curve (AUC) of 10-fold cross-validation. Results: The study included 4,293 participants (age 57 ± 13 years, 53.9% women). During 9.3±3.7 years follow-up, 106 participants were diagnosed with MCI. Seven MRI measures were significantly associated with more than 20 acoustic features after adjusting for multiple testing. The acoustic composite scores can improve the AUC for MCI prediction to 0.794, compared to 0.759 achieved by MRI measures. Discussion: We found multiple acoustic features were associated with MRI measures, suggesting the potential for using acoustic features as easily accessible digital biomarkers for the early diagnosis of MCI.
Source: Frontiers in Dementia
Authors: Huitong Ding, Alexander P. Hamel, Cody Karjadi, Ting F. A. Ang, Sophia Lu, Robert J. Thomas, Rhoda Au, Honghuang Lin
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,125
Awards: G1664136512, Assessing Alzheimer disease risk and heterogeneity using multimodal machine learning approaches, G2502872260, G250621538, Re-visiting Methods for MCI Diagnosis to Improve Biomarker and Trial Findings, Integrated Digital Technology Platform for Optimization of Precision Brain Health, G3562024847, G4869932426, G5164707915, G5771282042, Cognitive Heterogeneity in those with high Alzheimer's Disease Risk, MRI, Genetics & Cognitive Precursors of AD and Dementia, Cognitive heterogeneity in those with high Alzheimer's Disease Risk, THE FRAMINGHAM HEART STUDY-N01HC25195-268025195-268025195, G7264467602, Epigenetic changes in synaptic and inflammatory genes involved in the age-dependent development of Alzheimer's disease pathologies andcognitive decline, Epidemiology of Dementia in the Framingham Study, Precision Monitoring and Assessment in the Framingham Study: Cognitive, MRI, Genetic and Biomarker Precursors of AD & Dementia, Cognitive heterogeneity in those with high Alzheimer's Disease Risk, Precursors of Stroke Incidence and Prognosis
OpenAlex ID: W4388946040