This paper is in the following e-collection/theme issue:

New Methods (1134) Diagnostic Tools in Mental Health (326) Cognitive and Neurorehabilitation (349) Dementia and Cognitive Decline (696)

Published on in Vol 24, No 12 (2022): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42886, first published .
Association Between Acoustic Features and Neuropsychological Test Performance in the Framingham Heart Study: Observational Study

Association Between Acoustic Features and Neuropsychological Test Performance in the Framingham Heart Study: Observational Study

Association Between Acoustic Features and Neuropsychological Test Performance in the Framingham Heart Study: Observational Study

Authors of this article:

Huitong Ding1 Author Orcid Image ;   Amiya Mandapati2, 3 Author Orcid Image ;   Cody Karjadi1, 4 Author Orcid Image ;   Ting Fang Alvin Ang1, 4, 5, 6 Author Orcid Image ;   Sophia Lu5 Author Orcid Image ;   Xiao Miao7 Author Orcid Image ;   James Glass8 Author Orcid Image ;   Rhoda Au1, 4, 5, 6, 9 Author Orcid Image ;   Honghuang Lin10 Author Orcid Image
Article Authors Cited by (12) Tweetations (3) Metrics

Journals

  1. Watase T, Omiya Y, Tokuno S. Severity Classification Using Dynamic Time Warping–Based Voice Biomarkers for Patients With COVID-19: Feasibility Cross-Sectional Study. JMIR Biomedical Engineering 2023;8:e50924 View
  2. Ding H, Hamel A, Karjadi C, Ang T, Lu S, Thomas R, Au R, Lin H. Association between acoustic features and brain volumes: the Framingham Heart Study. Frontiers in Dementia 2023;2 View
  3. Ding H, Lister A, Karjadi C, Au R, Lin H, Bischoff B, Hwang P. Detection of Mild Cognitive Impairment From Non-Semantic, Acoustic Voice Features: The Framingham Heart Study. JMIR Aging 2024;7:e55126 View
  4. Ding H, Gifford K, Shih L, Ho K, Rahman S, Igwe A, Low S, Popp Z, Searls E, Li Z, Madan S, Burk A, Hwang P, Anda-Duran I, Kolachalama V, Au R, Lin H. Exploring the Perspectives of Older Adults on a Digital Brain Health Platform Using Natural Language Processing: Cohort Study. JMIR Formative Research 2024;8:e60453 View
  5. Xu L, Chen K, Mueller K, Liss J, Berisha V. Articulatory precision from connected speech as a marker of cognitive decline in Alzheimer’s disease risk-enriched cohorts. Journal of Alzheimer’s Disease 2025;103(2):476 View
  6. Arslan-Sarımehmetoğlu E, Barmak E. The effect of mild-stage Alzheimer’s disease on the acoustic parameters of voice. The Egyptian Journal of Otolaryngology 2025;41(1) View
  7. Ding H, Ye Z, Paschalidis A, Bennett D, Au R, Lin H. Dynamic lifetime risk prediction of Alzheimer's disease with longitudinal cognitive assessment measurements. Alzheimer's & Dementia 2025;21(3) View
  8. Zhao Y, Tang W, Liu Y, Wu X, Zhang W, Li X, Zhang Y, Xing Y, Wang D, Yin Y, Niu S, Yang Y, Ding J, Shan E, Li X. Objective biomarkers of cognitive performance in older adults with mild cognitive impairment: acoustic features of affective prosody. Geriatric Nursing 2025;64:103370 View
  9. Sung S, Pokojovy M, Kang D, Bae W, Hong Y, Kim S. Enhancing the Accuracy of Image Classification for Degenerative Brain Diseases with CNN Ensemble Models Using Mel-Spectrograms. Mathematics 2025;13(13):2100 View
  10. Wang L, Glass J, Kourtis L, Au R. Multi-modal data analysis for early detection of alzheimer’s disease and related dementias. The Journal of Prevention of Alzheimer's Disease 2026;13(1):100399 View
  11. Wang B, Ding H, Qi D, Tisminetzky M, Murabito J, Lin H. Plasma-based brain age as a potential biomarker for cognitive health and risk of brain-related disorders. Communications Medicine 2025 View

Books/Policy Documents

  1. . Neuropsychology in the Age of Digital Health. View