Biomarkers.

Area of Science:

• Neuroimaging
• Medical Informatics
• Health Disparities Research

Background:

• Automated de-facing software is crucial for protecting participant privacy in brain imaging studies.
• Existing de-facing tools are primarily validated on non-Hispanic White populations.
• Performance disparities in de-facing software across ethnoracial groups are not well understood.

Purpose of the Study:

• To evaluate the performance of de-facing software across diverse ethnoracial groups in brain MRI.
• To compare the impact of de-facing on neuroimaging measurements (cortical thickness, gray matter volume) across non-Hispanic White, African American, and Mexican American individuals.

Main Methods:

• Utilized T1-weighted MRI data from 305 participants in the Health and Aging Brain Study: Health Disparities (HABS-HD) cohort.
• Matched participants across three ethnoracial groups (non-Hispanic White, African American, Mexican American) by age, sex, and cognitive status.
• Applied mri_reface software for de-facing and FreeSurfer for neuroimaging analysis, comparing differences in measurements between de-faced and original images across groups.

Main Results:

• Significant differences in cortical thickness de-facing effects were observed between non-Hispanic White and Mexican American groups, and between African American and Mexican American groups.
• Gray matter volume differences due to de-facing were significant between African American and Mexican American groups.
• Observed differences in both cortical thickness and gray matter volume were generally small, with minimal regional impact.

Conclusions:

• The performance of de-facing software varies significantly across ethnoracial groups.
• Future research will focus on optimizing de-facing algorithms to ensure equitable performance.
• Current versions of de-facing software produce differences unlikely to affect practical outcomes in neuroimaging analyses.