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Abstract #0959

Statistical modeling to assess the impact of cortical parameters on cognition in Multiple Sclerosis.

Vanessa Lippolis 1 , Daniel Altmann 2 , Nils Muhlert 3 , Egidio Ugo D'Angelo 4 , Lucia Della Croce 5 , Matteo Pardini 6,7 , Declan Chard 7 , David H. Miller 7 , Maria Ron 7 , Fulvia Palesi 8,9 , and Claudia A.M. Wheeler-Kingshott 7

1 Mathematics, University of Pavia, Pavia, Pavia, Italy, 2 Department of Medical Statistics, LSHTM, London, London, United Kingdom, 3 School of Psychology, Cardiff University, Cardiff, Wales, United Kingdom, 4 Dept of Brain and Behavioral Sciences, University of Pavia, Pavia, Pavia, Italy, 5 Mathematics, University of Pavia, Pavia, Italy, 6 Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Genoa, Italy, 7 NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Institute of Neurology, London, London, United Kingdom, 8 Department of Physics, University of Pavia, Pavia, Pavia, Italy, 9 Brain Connectivity Center, National Neurological Institute C. Mondino, Pavia, Pavia, Italy

We present a statistical model in the GLM framework to relate cognitive scores to MRI-based cortical parameters, using a cohort of patients with MS and healthy controls. The model determined that patients were significantly worse in the Stroop test and presented a significant loss of cortical thickness; indeed variables that best predict MS status are the Right Medial Thickness and Right and Left Lateral Area. Cortical parameters are associated with cognitive scores, but there is no evidence that pathology of MS has an effect on these associations. In further works models can be expanded designing therapeutic interventions.

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