Comparison of statistical parametric mapping method and scaled subprofile model for functional neuroimage analysis

Two common analytic techniques for functional neuroimages are Statistical parametric mapping (SPM) and Scaled subprofile model based on principle component analysis (SSM/PCA). SPM is based on mass univariate testing in which signals from each region are compared between two groups. SSM/PCA is a multivariate PCA-based algorithm that construct the disease-related pattern that discriminate between patients and controls.
We developed analytical relation between the SPM and SSM/PCA results. We tested this relation on the results of SPM and SSM/PCA, performed on simulated images with various imaging scenarios addressed (various number of images, noise levels, image patterns) and on clinical 18F-flourodeoxyglucose positron emission tomography images of Alzheimer's dementia patients.
Investigation of SPM and SSM/PCA methodology revealed that SSM/PCA and SPM results are related: SSM/PCA disease-related pattern is proportional to the logarithm of SPM's t-map, increased by 1 and corrected by a factor depending on the number of images and imaging noise level. Tests with simulated and clinical images verified this relation.
This work confirmed that the results of SPM and SSM/PCA on the same data are not independent, despite the fundamental differences between those two methods.