Philip A. Cook1, Brian B. Avants1, Corey T. McMillan2, John Powers2, Jonathan E. Peelle3, James C. Gee1, Murray Grossman2
1Radiology, University of Pennsylvania, Philadelphia, PA, United States; 2Neurology, University of Pennsylvania, Philadelphia, PA, United States; 3Department of Otolaryngology, Washington University School of Medicine, St Louis, MO, United States
Language deficits are widely reported in frontotemporal dementia (FTD). We hypothesize that these deficits are due to disruption of a large-scale neural network involving both language and executive resources. We use multi-modal MRI and sparse statistical methods to evaluate whether imaging of white matter enhances prediction of language deficits when combined with imaging of cortex. We apply Eigenanatomy, a novel technique for data-driven parcellation of brain images, to find areas of the brain correlated with language performance. Both gray and white matter contribute to efficient models of verbal fluency and naming performance.