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Updated: Jul 20, 2026

Basics of Multivariate Analysis in Neuroimaging Data
Published on: July 24, 2010
Implementation and validation of face de-identification (de-facing) in ADNI4
Christopher G Schwarz1, Mark Choe2, Stephanie Rossi3
1Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Introduction:
Recent technological advances have increased the risk that de-identified brain images could be re-identified from face imagery. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a leading source of publicly available de-identified brain imaging, who quickly acted to protect participants' privacy.
Methods:
An independent expert committee evaluated 11 face-deidentification ("de-facing") methods and selected four for formal testing.
Results:
Effects of de-facing on brain measurements were comparable across methods and sufficiently small to recommend de-facing in ADNI. The committee ultimately recommended mri_reface for advantages in reliability, and for some practical considerations. ADNI leadership approved the committee's recommendation, beginning in ADNI4.
Discussion:
ADNI4 de-faces all applicable brain images before subsequent pre-processing, analyses, and public release. Trained analysts inspect de-faced images to confirm complete face removal and complete non-modification of brain. This paper details the history of the algorithm selection process and extensive validation, then describes the production workflows for de-facing in ADNI.
Highlights:
ADNI is implementing "de-facing" of MRI and PET beginning in ADNI4. "De-facing" alters face imagery in brain images to help protect privacy. Four algorithms were extensively compared for ADNI and mri_reface was chosen. Validation confirms mri_reface is robust and effective for ADNI sequences. Validation confirms mri_reface negligibly affects ADNI brain measurements.
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