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Abstract #0882

Network analysis of multiple sclerosis using pair-wise matched controls and cohesive parcellation of rsfMRI

Ajay Nemani1, Katherine Koenig2, Xuemei Huang1, and Mark J Lowe2
1Imaging Institute, Cleveland Clinic, Cleveland, OH, United States, 2Cleveland Clinic, Cleveland, OH, United States


Multiple sclerosis (MS) is characterized by degenerative changes in white matter, resulting in motor and cognitive deficits. The corresponding damage to functional brain networks is not well understood. We studied several graph theoretic network features of multiple sclerosis patients imaged with rsfMRI. Using a data-driven parcellation designed for optimal node-based representation as well as sex and age-matched controls, we find no significant differences. This result was robust to individual and group parcellations, as well as paired and group comparisons. This differs from previous studies based on standard parcellations and pooled group comparisons. Methodologic reasons for the different observations are discussed.

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