P1‐148: Hippocampal volume and shape analysis in patients with PIB (‐), PIB (+) subcortical vascular dementia and Alzheimer's disease

Abstract

promise in better distinguishing between the MCI and MCI converter subject groups.Methods:We investigated 124 subjects with mild cognitive impairment (MCI), 54 subjects with mild cognitive impairment that later converted to Alzheimer’s dementia (MCIconv). We computed cortical thickness of each subject by a method that is based on solving the potential equation between the gray and white matter surfaces, and integrating along the gradient field that runs perpendicular to each isosurface. Next, we parcellate each subject’s cortical surface into n1⁄4 350 different cortical regions by a recursive zonal equal-area partitioning algorithm.We averaged the cortical thickness at each cortical patch for each subject and investigated the differences in thickness of cortical mantle across the two subject groups. Results: Accounting for multiple comparison corrections, statistical analysis on the patch-wise cortical thickness data was able to localize regions, Fig. 1, where there are significant differences in thickness among the two subject groups. Patch-wise cortical thickness data was used as a biomarker in SVM-based classification with a classification accuracy of 71%. This is a significant improvements over using the conventional vertex-wise cortical thickness data, which is able to achieve a 62.4% accuracy. Conclusions: The proposed method to analyze cortical data is a powerful form of dimensionality reduction that can significantly reduce the effects of multiple comparison corrections in subsequent statistical analysis. Using this method we were able to localize the regions on the cortex where there are significant differences in cortical thickness among the two subject groups. We also observed a significant advantage in terms of classification accuracy of using patch-wise cortical thickness data as opposed to vertex-wise data.