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\subsubsection{Central Motor Conduction Time and Resting State fMRI}  Connectivity First, the metric generated from Aim 1 will be validated on this set of subjects, and will be correlated with CMCT. In addition, a seed-voxel analysis will be run with connectivity  maps based on the left and right precentral gyrus seeds will be generated for the 50 patients and controls. seeds.  Connectivity maps will be z-scored voxel-wise against the maps of the healthy controls. A voxel-wise OLS regression will be run on the connectivity maps to predict CMCT, EDSS, and NHPT. Next, a partial least squares analysis will be run to find components of the connectivity maps the correlate with each of the outcome variables. Partial least squares tends to overfit data, so I will implement cross-validation and permutation testing.Finally, the metric generated from Aim 1 will be validated on this set of subjects, and will be correlated with CMCT.  \subsection{Expected Results and Significance}  If the resting state metric correlates with CMCT, it could also have the prognostic value of CMCT, adding additional information to the prediction of future motor deficits.  I expect the voxel-wise OLS analysis to yield regions of the brain that are differentially connected to the motor cortex based on neurophysiology and clinical evaluations. I predict that the patients with less motor deficits have adapted to damage caused by MS, and that this is reflected in the resting state connectivity map of the motor cortex. The regions of the brain, brain  highlighted by the connectivty maps, maps  could be regions that contribute to adaptive or maladaptive mechanisms to damage, and are regions that could be monitored during a clinical trial.Additionally, a resting state metric that correlates with CMCT could also have the prognostic value of CMCT, which may add additional information to the prediction of future motor deficits.