# Protecting the Confidentiality of Participants in Studies of Alzheimer's Disease and Related Dementias by Replacing Face Imagery in MRI

> **NIH NIH R56** · MAYO CLINIC ROCHESTER · 2020 · $792,752

## Abstract

PROJECT SUMMARY / ABSTRACT
There exists a growing demand to share all publicly-funded research data, including magnetic resonance
images (MRI). Concurrently, it has been shown that high-resolution facial reconstructions can be generated
from MRI, and face recognition software can match these reconstructions with participant photos. Standard
MRI de-identification removes participant names from the image header, but does nothing to prevent face
recognition. Identified individual research participants would be irreversibly linked with all the collected
protected health information, such as diagnoses, biomarker results, genetic risk factors, and neuropsychiatric
testing. Although data use agreements can legally protect study administrators, these legal mechanisms do not
directly protect participants. If participants were publicly identified by a careless or malicious individual, this
event would significantly and permanently erode public trust and participation in medical research. Many large
imaging studies of Alzheimer’s disease (AD) and related dementias are vulnerable to this threat.
 To address this threat, we propose a novel technique that de-identifies MRI by replacing facial imagery with
a generic, average face (i.e., a digital face “transplant”). Unlike existing methods that remove or blur faces, our
approach minimizes added bias and noise in imaging biomarker measurements by producing a de-identified
MRI that resembles a natural image. This imminent privacy threat grows with burgeoning technology and with
the increased public sharing of research data. We propose to refine, validate, publicly share, and broadly apply
our technique to several of the field’s largest imaging studies of Alzheimer’s disease and related dementias.
Aim 1: Refine and validate an optimized face de-identification algorithm 1A) Refine the software to further
decrease the potential for face recognition; 1B) Refine the software to maximize robustness and minimize
impact upon common brain biomarker measurements.
Aim 2: Investigate effects of age, race, and sex 2A) Evaluate the effects of age, race, and sex on the
proposed de-identification method; 2B) Adapt software to ensure that the algorithm protects all participants
equally.
Aim 3: Apply our technique to large ongoing studies to protect participant privacy 3A) Implement our
de-identification method for data sharing in the Mayo Clinic Study of Aging and Mayo Clinic Alzheimer’s
Disease Research Center imaging studies; 3B) Implement our de-identification method for the A4 study,
prospectively; 3C) Implement our de-identification method for ALLFTD, both prospectively and retrospectively;
3D) Implement our de-identification method for ADNI, both prospectively and retrospectively.
Aim 4: Share the software freely for research use

## Key facts

- **NIH application ID:** 10232009
- **Project number:** 1R56AG068206-01
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** Christopher George Schwarz
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $792,752
- **Award type:** 1
- **Project period:** 2020-09-15 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10232009

## Citation

> US National Institutes of Health, RePORTER application 10232009, Protecting the Confidentiality of Participants in Studies of Alzheimer's Disease and Related Dementias by Replacing Face Imagery in MRI (1R56AG068206-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10232009. Licensed CC0.

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