Identification of disease-related spatial covariance patterns using neuroimaging data
P Spetsieris, Y Ma, S Peng, JH Ko, V Dhawan… - JoVE (Journal of …, 2013 - jove.com
The scaled subprofile model (SSM) 1-4 is a multivariate PCA-based algorithm that identifies
major sources of variation in patient and control group brain image data while rejecting
lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-
state multimodality images, an entire group image set can be reduced to a few significant
linearly independent covariance patterns and corresponding subject scores. Each pattern,
termed a group invariant subprofile (GIS), is an orthogonal principal component that …
major sources of variation in patient and control group brain image data while rejecting
lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-
state multimodality images, an entire group image set can be reduced to a few significant
linearly independent covariance patterns and corresponding subject scores. Each pattern,
termed a group invariant subprofile (GIS), is an orthogonal principal component that …