Data from: Longitudinal structural and metabolic changes in frontotemporal dementia
Cite this dataset
Bejanin, Alexandre et al. (2021). Data from: Longitudinal structural and metabolic changes in frontotemporal dementia [Dataset]. Dryad. https://doi.org/10.5061/dryad.dz08kprsw
Objective: To compare the sensitivity of structural MRI and 18F-Fludeoxyglucose PET (18FDG-PET) to detect longitudinal changes in frontotemporal dementia (FTD). Methods: Thirty patients with behavioral-variant FTD (bvFTD), 7 with non-fluent/agrammatic variant primary progressive aphasia (nfvPPA), 16 with semantic variant PPA (svPPA) and 43 cognitively normal controls underwent 2-4 MRI and 18FDG-PET scans (total scan/visit=270) as part of the Frontotemporal Lobar Degeneration Neuroimaging Initiative study. Linear mixed-effects models were carried out voxel-wise and in regions of interest to identify areas showing decreased volume or metabolism over time in patients as compared to controls. Results: At baseline, patients with bvFTD showed bilateral temporal, dorsolateral and medial prefrontal atrophy/hypometabolism that extended with time into adjacent structures and parietal lobe. In nfvPPA, baseline atrophy/hypometabolism in supplementary motor cortex extended with time into left greater than right precentral, dorsolateral and dorsomedial prefrontal cortex. In svPPA, baseline atrophy/hypometabolism encompassed the anterior temporal and medial prefrontal cortex and longitudinal changes were found in temporal, orbitofrontal and lateral parietal cortex. Across syndromes, there was substantial overlap in the brain regions showing volume and metabolism loss. Even though the pattern of metabolic decline was more extensive, metabolic changes were also more variable and sample size estimates were similar or higher for 18FDG-PET compared to MRI. Conclusion: Our findings demonstrated the sensitivity of 18FDG-PET and structural MRI for tracking disease progression in FTD. Both modalities showed highly overlapping patterns of longitudinal change and comparable sample size estimates to detect longitudinal changes in future clinical trials.