Figure 7. Demonstrating the Utility of the Eigenmodes for Dimensionality Reduction, Differential Diagnosis, and Classification.

(A) Mean dot product between atrophy and the first three eigenmodes for each dementia group. The aged but cognitively normal group shows mixed presence of all three eigenmodes, whereas the other two disease groups show primary presence of the eigenmode hypothesized to be associated with the disease.

(B) Scatter plot of the dot product in (A) for AD and bvFTD subjects, showing clear separation of the two groups after projection onto the eigenmodes.

(C) Area under the ROC curve of three-way classification at various dimensions of feature space, based on eigenmodes as well as PCA. This plot shows that the eigenmodes are doing at least as good a job of dimensionality reduction as the principal components analysis.

(D) ROC curve of both classifiers, using four features each. The blue curve corresponds to classification using the first four eigenmodes of network diffusion, while the red curve corresponds to classification using the first four principle components of the atrophy z-scores. Clearly, the eigenmodes provide better classifiability in terms of area under the ROC curve.

See also Figure S5.