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
Mathematics, University of Pavia, Pavia,
of Medical Statistics, LSHTM, London, London, United
of Psychology, Cardiff University, Cardiff, Wales,
of Brain and Behavioral Sciences, University of Pavia,
Pavia, Pavia, Italy,
of Pavia, Pavia, Italy,
of Neuroscience, Rehabilitation, Ophthalmology,
Genetics, Maternal and Child Health, University of
Genoa, Genoa, Genoa, Italy,
Research Unit, Department of Neuroinflammation, Queen
Square MS Centre, UCL Institute of Neurology, London,
London, United Kingdom,
of Physics, University of Pavia, Pavia, Pavia, Italy,
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
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