|
Abstract:
|
Alzheimer's disease is a neurodegenerative disease in which the degree of cortical atrophy in specific structures of the brain serves as a useful imaging biomarker. A massive-univariate analysis, a simplified approach that fits a univariate model for every vertex along the cortex, is insufficient to model cortical atrophy because it does not account for the spatial relatedness of cortical thickness from magnetic resonance imaging (MRI), and it can suffer from Type I error rate control. We develop a permutation-based inference procedure to detect spatial clusters of vertices showing statistically significant differences in the rates of cortical atrophy. The proposed method uses spatial information to combine the signals adaptively across nearby vertices, yielding high statistical power while maintaining an accurate family-wise error rate (FWER). When the global null hypothesis is rejected, we use a cluster selection algorithm to identify the spatial clusters of significant vertices. We validate our method using simulation studies and apply it to the ADNI data to show its superior performance over existing methods.
|
