Remote Naturalistic Speech Analysis as a Tool to Classify Behavioral Variant Frontotemporal Dementia
Leveraging a flexible speech capture app with remote accessibility and state-of-the-art automated speech analysis, we identified linguistic features that can distinguish HC from bvFTD. This approach presents a promising screening tool for detecting bvFTD, offering quick, easily administered assessments with the potential for scalable implementation outside of clinical settings. Next steps are to investigate linguistic changes in presymptomatic mutation carriers and compare speech profiles between FTD and AD. These results hold meaningful implications for addressing current challenges in FTD therapeutics, potentially offering sensitive outcome measures for clinical trials and future tools for personalized cognitive assessment and long-term monitoring.
Authors
- Hilary W. Heuer (ORCID: https://orcid.org/0000-0002-3776-1685)
- Adam M. Staffaroni (ORCID: https://orcid.org/0000-0002-3903-9805)
- Adam L. Boxer (ORCID: https://orcid.org/0000-0002-1215-5064)
- Annie L Clark
- Jack C. Taylor (ORCID: https://orcid.org/0000-0002-6431-9956)
- Jet M. J. Vonk (ORCID: https://orcid.org/0000-0002-1808-8843)
- Bradley F. Boeve (ORCID: https://orcid.org/0000-0002-4153-8187)
- Howard J. Rosen (ORCID: https://orcid.org/0000-0001-9281-7402)
- Leah K. Forsberg (ORCID: https://orcid.org/0000-0003-4086-9598)
- Jacob Young (ORCID: https://orcid.org/0000-0003-3563-425X)
- Sreya Dhanam
- Brittany Morin
- Maria Luisa Gorno Tempini
- David Rosado‐Rolon
Institutions
- University of California, San Francisco (US)
- Mayo Clinic in Arizona (US)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1002/alz.091734
- Primary Topic
- Language Development and Disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00