Early identification of mild cognitive impairment (MCI) patients presents significant challenges due to mild symptoms and low sensitivity of the algorithms proposed for MCI identification. In this study we employed low-rank plus sparse (L+S) matrix decomposition for identifying gray matter volume differences in bilateral hippocampi between MCI patients who converted to Alzheimer’s disease within 18 months and MCI patients who did not. The L+S decomposition identifies features that are common across subjects while minimizing the influence of individual variabilities and outliers. Sensitivity and accuracy are greatly improved and voxel-wise differences that couldn’t be assessed by previous analyses are also identified.
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