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
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.