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Multimodal neuroimaging studies of corticobasal syndrome using volumetric MRIand DTI successfully discriminate between Alzheimer's disease andfrontotemporal lobar degeneration but this evidence has typically includedclinically heterogeneous patient cohorts and has rarely assessed the networkstructure of these distinct sources of pathology. Using structural MRI data, weidentify areas in fronto-temporo-parietal cortex with reduced gray matterdensity in corticobasal syndrome relative to age matched controls. A supportvector machine procedure demonstrates that gray matter density poorlydiscriminates between frontotemporal lobar degeneration and Alzheimer's diseasepathology subgroups with low sensitivity and specificity. In contrast, astatistic of local network efficiency demonstrates excellent discriminatorypower, with high sensitivity and specificity. Our results indicate that theunderlying pathological sources of corticobasal syndrome can be classified moreaccurately using graph theoretical statistics of white matter microstructure inassociation cortex than by regional gray matter density alone. These resultshighlight the importance of a multimodal neuroimaging approach to diagnosticanalyses of corticobasal syndrome and suggest that distinct sources ofpathology mediate the circuitry of brain regions affected by corticobasalsyndrome.

Brain network efficiency is influenced by pathological source of corticobasal syndrome