Novel imaging genetic biomarkers for sporadic frontotemporal dementia through machine learning

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Project Summary This is an application for a K01 award for Dr. Iris Broce-Diaz, a neuroimaging genetics postdoctoral fellow at the University of California, San Diego and University of California, San Francisco. Dr. Broce-Diaz is establishing herself as a young imaging geneticist conducting clinical research on neurodegenerative disease. This K01 will provide Dr. Broce-Diaz with the support necessary to accomplish the following goals: (1) gain proficiency in machine learning and computational modeling techniques, (2) gain proficiency in clinical and genetic research methodology for cognitive aging and complex spectrum of neurodegenerative diseases, including clinical characterization of frontotemporal dementia (FTD) and other Alzheimer’s Disease-Related Dementias, differential diagnosis, risk prediction, and biomarker development, and (3) develop an independent research career. To achieve these goals, Dr. Broce-Diaz has assembled an expert mentoring team, including her primary mentors: Dr Anders Dale (renowned computational neuroimaging genetics scientist) and co-primary mentor Bruce Miller (internationally recognized behavioral neurologist and leader in FTD), co-mentors: Drs. Jennifer Yokoyama (expert in FTD genetics) and Chun Chieh Fan (expert in epidemiology/biostatistics), and two collaborators: Drs. Adam Boxer (leader in clinical trials for FTD-spectrum disorders) and Wesley Thompson (expert in advanced statistics). The goal of the proposed project is to develop novel imaging genetics biomarkers for predicting individuals at risk of developing sporadic (non-familial) FTD and improving classification accuracy of sporadic FTD. Dr. Broce- Diaz will achieve this goal through the following specific aims: (1a) utilize a polygenic hazard approach to develop and validate a novel genetic biomarker for predicting age-specific risk of sporadic FTD; (1b) leverage pleiotropic information to increase accuracy of the genetic risk scores and derive biologically-based genetic risk scores; (2) use machine learning approaches to reliably and accurately classify FTD clinical subtypes and obtain personalized atrophy scores from these brain maps; and (3) improve FTD classification by integrating atrophy scores with genetic risk scores. This proposed study uses highly innovative methodological approaches for informing FTD prognosis, diagnosis, and, ultimately, clinical trial design. If validated, these biomarkers will make significant contributions by assisting clinicians in identifying patients at elevated risk for sporadic FTD and assisting in diagnosing sporadic FTD in its earliest stages—reducing diagnostic delays, accelerating the discovery of novel treatments, and improving recruitment accuracy in clinical trials. This K01 research project will provide Dr. Broce-Diaz with the protected research time and opportunity to train with leaders in the field she needs to master the skills required to establish an independent, patient-oriented, imaging genetics and biomarker development clinical research program that will inform diagnosis, prognosis, and guide treatments of FTD and other neurodegenerative diseases.