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Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

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Biomarkers.

James Glass¹, Liming Wang¹, Saurabhchand Bhati¹

¹Massachusetts Institute of Technology, Cambridge, MA, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association

|December 24, 2025

Summary

This summary is machine-generated.

New digital voice analysis tools can help detect early Alzheimer's disease (AD) and related dementias (ADRD) by extracting key features from speech. These open-source tools address privacy concerns and enable broader research into preclinical AD/ADRD detection using voice biomarkers.

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Area of Science:

Neuroscience
Computational Linguistics
Biomedical Engineering

Background:

Digital voice (dVoice) analysis is an emerging, accessible method for early detection of Alzheimer's disease (AD) and related dementias (ADRD).
Challenges include protecting personal identifying information (PII) within voice data and the need for automated feature extraction tools.
The Global Research and Imaging Platform (GRIP) is developing open-source dVoice processing tools to overcome these barriers.

Purpose of the Study:

To develop and validate a suite of open-source tools for analyzing digital voice recordings for early AD/ADRD detection.
To address privacy concerns by implementing methods for masking voice prints and removing PII from transcripts.
To enable AI-driven analysis of voice features indicative of cognitive and behavioral symptoms.

Main Methods:

Utilized over 33,000 longitudinal voice recordings from the Framingham Heart Study (FHS).
Developed a fictitious voice conversion (FVC) method to mask voice prints while preserving audio features.
Created automated tools for audio, linguistic, and paralinguistic feature extraction, alongside an NLP framework for PII removal and a privacy-protecting AI analysis pipeline.

Main Results:

Applied FVC, feature extraction, NLP-PII, and privacy-protecting AI analysis to various datasets including FHS, ADReSS, and DementiaBank.
Successfully extracted acoustic, linguistic, and paralinguistic features from FHS dVoice recordings.
The FVC method showed limitations in maintaining analysis comparability compared to native voice recordings.

Conclusions:

The GRIP version 1 dVoice processing tools are robust for analyzing recordings of varying fidelity.
These tools facilitate research into voice biomarkers for measuring cognitive and behavioral symptoms of early and preclinical AD/ADRD.
The open-source release enables wider adoption and further development in the field of digital voice analysis for neurodegenerative disease detection.