Frontotemporal degeneration (FTD) is a progressive disorder characterized by pathology and neurodegeneration in a predominantly frontal and temporal anatomic distribution. However, there is extensive heterogeneity of disease distribution across individuals with FTD, including several clinical phenotypes, several underlying pathological sources of disease, and distinct sources of genetic contributions. Multimodal neuroimaging, including structural MRI (sMRI) and diffusion MRI (dMRI), provides an important tool to characterize the heterogeneous and distributed loci of FTD in individuals in order to objectively improve the diagnostic discrimination between FTLD-tau and FTLD-TDP, relate these pathologies to phenotypes, and inform the biological mechanisms of these complex network disorders. Functional perfusion MRI (pMRI) and resting state BOLD fMRI (rsfMRI) approaches enhance sMRI and dMRI by providing additional unique insights into the origin, rate, and pattern of early disease and longitudinal progression. 7T MRI provides a unique opportunity to better understand the microstructural mechanisms that distinguish FTLD-TDP from FTLD-Tau. Thus, the overall goal of Imaging Core C is to provide the necessary infrastructure to support state-of-the-art 3T and 7T MRI acquisition and network analyses of neuroimaging data in Projects I-V of this research program. This Core will also be closely integrated with Cores B-E to facilitate our interdisciplinary and convergent in vivo and ex vivo approach to improving our understanding of FTD as a network disorder. In particular, we will generate graph ?nodes? reflecting MRI-derived structural properties of FTD brains and graph ?edges? reflecting structural and/or functional connectivity to facilitate the network neuroscience approach of each individual project. To accomplish this goal we propose 3 Specific Aims: (1) Collect state-of-the-art in vivo 3T MRI multimodal neuroimaging data in FTD patients including sMRI, dMRI, pMRI, and rsfMRI; (2) Provide a cross-sectional and longitudinal image processing pipeline for standard operating procedure (SOP) neuroimaging measurements; and (3) Develop novel 7 Tesla (7T) acquisition strategies to yield ultra-high- resolution (<0.3mm3) data and test biologically-motivated hypotheses.
