Data-Driven Regions of Interest for Longitudinal Change in Frontotemporal Lobar Degeneration at 3T (P4.033)

OBJECTIVE: The aim of this project was to create a region of interest (ROI) with maximal effect size for measuring longitudinal change in cortical volume in frontotemporal lobar degeneration (FTLD), and to compare the effect-size for measuring change within this region to the effect size in a-priori defined anatomical ROIs. This data-driven ROI will prove powerful in detecting treatment effects in clinical trials, resulting in reduced sample sizes relative to a-priori defined anatomical ROI’s. BACKGROUND: Recent studies in Alzheimer’s disease (AD) have shown that measurement of change in empirically derived ROI’s allows more reliable measurement of change over time compared with regions chosen a-priori based on known effects of AD. FTLD is a devastating neurodegenerative disorder for which there are no approved treatments. The goal of this study was to identify an empirical ROI that maximizes the effect size for the annual rate of grey matter atrophy in FTLD compared with healthy age-matched controls, and to estimate the effect size and associated power estimates to be utilized in future treatment trials. DESIGN/METHODS: 199 participants, including 102 patients with FTLD (45 behavioral-variant, 30 semantic-variant, 27 progressive non-fluent aphasia) were studied. All participants were imaged at 3T with a longitudinal average scan interval of 1.1 years. Patients received a battery of cognitive tests chosen to assess the range of cognitive symptoms typically seen in FTLD. RESULTS: The effect size for longitudinal change measured within the empirically derived ROI was larger than the effect sizes derived from a-priori anatomical ROIs. The specific regions identified in the change maps were what would be expected from prior cross-sectional and longitudinal studies of FTLD. CONCLUSIONS: These results indicate that empirically derived ROIs can reduce the number of subjects needed for FTLD treatment trials compared with a-priori ROIs. Disclosure: Dr. McKenna has nothing to disclose. Dr. Marx has nothing to disclose. Dr. Pankov has nothing to disclose. Dr. Binney has nothing to disclose. Dr. Suneth has nothing to disclose. Dr. He has nothing to disclose. Dr. Dutt has nothing to disclose. Dr. Elofson has nothing to disclose. Dr. Kornak has nothing to disclose. Dr. Miller has nothing to disclose. Dr. Rosen has nothing to disclose.