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.
@article{arxiv.1601.07867,
title = {Brain network efficiency is influenced by pathological source of corticobasal syndrome},
author = {John D. Medaglia and Weiyu Huang and Santiago Segarra and Christopher Olm and James Gee and Murray Grossman and Alejandro Ribeiro and Corey T. McMillan and Danielle S. Bassett},
journal= {arXiv preprint arXiv:1601.07867},
year = {2016}
}