Identification of disease-related spatial covariance patterns using neuroimaging data.
Academic Article
Overview
abstract
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 represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data(2,5,6). Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors(7,8). Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects(5,6). Cross-validation within the derivation set can be performed using bootstrap resampling techniques(9). Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets(10). Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation(11). These standardized values can in turn be used to assist in differential diagnosis(12,13) and to assess disease progression and treatment effects at the network level(7,14-16). We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.
