Figure 4. Multivariate analysis of the SCI syndrome using data from two research sites.

A, Heat map of the bivariate correlation matrix, indicating all cross-correlations between behavioral and histological outcomes sorted in a randomized fashion. Blue indicates negative relationships and red indicates positive relationships. Heat reflects magnitude of Pearson correlation (r). B, Zoomed view of a small portion of the correlation matrix showing the interrelationships between a subset of outcomes. C, Principal components analysis (PCA) by eigenvalue decomposition was used to reduce the correlation matrix to synthetic multivariate variables known as principal components (PCs). PCs reflect clustered variance shared by numerous outcome measures. PC identities are indicated by significant PC loadings (arrows, loadings |>.40|). Each loading is equivalent to a Pearson correlation between individual outcomes and the PC. Loading magnitude is indicated by arrow width and heat (blue reflects negative and red reflects positive relationships). Exact loading values are shown next to each arrow. See Fig. S1 for non-significant loadings. D, Plot of individual subjects (N = 159) in the 3D multivariate syndrome space described by PC1-3. E–G, 2D plots of PC1-3 on their own axes. Significant differences: E,*P<.05 from sham, ** P<.05 from 75 kdyn and sham, §P<.05 from all groups except 6.25 mm. F, *P<.05 from sham, **P<.05 from all groups but sham, ***P<.05 from sham, 75 kdyn, 100 kdyn and hemisection. §P<.05 from all other groups. G, *P<.05 from sham, ** P<.05 from 75 and 100 kdyn.