Voxel-based morphometry (VBM) is an established method for assessing grey matter volumes across the brain. The quality of magnetic resonance imaging (MRI) and the chosen data preprocessing steps can affect the outcome of VBM analyses. We recognized a lack of publicly available and commonly used protocols, which indicates that standardized and optimized preprocessing protocols are needed. This paper focuses on the time- and resource-consuming manual correction of misclassifications of grey matter voxels in cortical structures important in Alzheimer’s dementia. A total of 126 individuals, including 63 patients with very early Alzheimer’s disease and 63 cognitively normal participants, received thorough neuropsychological testing and 3-Tesla MRI. Automated preprocessing of T1 MPRAGE images was performed, and misclassifications of grey matter voxels were manually identified and corrected. In a second run, the manual correction step was skipped. Multiple regression analyses using DARTEL in SPM8 were then conducted with the manually corrected and uncorrected sample, respectively. Manual correction of voxel misclassifications did not have a major impact on the correlation between episodic memory performance and structural brain imaging results. We conclude that, although performing all preprocessing steps remains the gold standard, skipping manual correction of voxel misclassifications is permitted when investigating populations on the Alzheimer’s disease spectrum.
P3-481: EMOTIONAL MODULATION OF MEMORY ACROSS THE ALZHEIMER'S DISEASE SPECTRUM AND ASSOCIATION WITH GREY MATTER DENSITY