  Multimodal neuroimaging studies of corticobasal syndrome using volumetric MRI
and DTI successfully discriminate between Alzheimer's disease and
frontotemporal lobar degeneration but this evidence has typically included
clinically heterogeneous patient cohorts and has rarely assessed the network
structure of these distinct sources of pathology. Using structural MRI data, we
identify areas in fronto-temporo-parietal cortex with reduced gray matter
density in corticobasal syndrome relative to age matched controls. A support
vector machine procedure demonstrates that gray matter density poorly
discriminates between frontotemporal lobar degeneration and Alzheimer's disease
pathology subgroups with low sensitivity and specificity. In contrast, a
statistic of local network efficiency demonstrates excellent discriminatory
power, with high sensitivity and specificity. Our results indicate that the
underlying pathological sources of corticobasal syndrome can be classified more
accurately using graph theoretical statistics of white matter microstructure in
association cortex than by regional gray matter density alone. These results
highlight the importance of a multimodal neuroimaging approach to diagnostic
analyses of corticobasal syndrome and suggest that distinct sources of
pathology mediate the circuitry of brain regions affected by corticobasal
syndrome.
