Development of Dynamic Resting State Functional Connectivity Machine Learning Framework for Dementia - Project Summary/Abstract The objective of this proposal is to provide a robust course of training for Fei Jiang, Ph.D., a candidate with an excellent foundation in statistical and machine learning research, to enable her to become an independent investigator in the field of quantitative data analysis and statistical/machine learning methods development for neuroimaging research. The proposed research aims to extract dynamic resting-state functional connectivity from multimodality imaging and use them for the prediction of cognitive decline. The central hypothesis is that the resting state functional connectivity changes over the imaging acquisition period, and this dynamic pattern is crucial for the optimal prediction of cognitive decline. Towards proving this hypothesis, a unique machine learn- ing framework is proposed to (1) robustly extract dynamic resting-state functional connectivity from multimodality imaging; (2) identify the important features that are associated with individuals' cognitive scores; and (3) predict cognitive decline using the identified important features. Successful completion of the proposed research will provide the next generation machine learning framework for the extraction and analysis of dynamic resting-state functional connectivity and lead to potential endpoints that can be used in the assessment of treatment effects. Recognizing the multidisciplinary nature of the work proposed, the author will be mentored and work closely with an expert committee from multiple scientific areas of relevance to the project (Neuroimaging, Neurodegenerative disease, Biostatistics): Srikantan Nagarajan (primary mentor), Ph.D., Department of Radiology and Biomedical Imaging, Ashish Raj (co-mentor), Ph.D., Department Radiology and Biomedical Imaging, William W. Seeley (ad- visor), M.D., Ph.D., Department of Neurology, John Kornak (advisor), Ph.D., Department of Epidemiology and Biostatistics, Marilu Gorno Tempini (collaborator), M.D., Ph.D., Department of Neurology, Charles McCulloch (collaborator), Ph.D., Department of Epidemiology and Biostatistics. This committee will be coordinated by Dr. Nagarajan. The goal is that by the end of the K25, Dr. Jiang will have the requisite knowledge, technical skills, and expertise to submit a successful R01 proposal that integrates her expertise in statistical and machine learn- ing methods with a knowledge of the questions and approaches pertaining to imaging in neuroscience, acquired through this training period.
