Dr. Sabina Tangaro Profile

Dr. Sabina Tangaro

at Istituto Nazionale di Fisica Nucleare

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Proceedings Article | 19 September 2017 Presentation + Paper

Association between MRI structural features and cognitive measures in pediatric multiple sclerosis

N. Amoroso, R. Bellotti, A. Fanizzi, A. Lombardi, A. Monaco, M. Liguori, L. Margari, M. Simone, R. G. Viterbo, S. Tangaro

Proceedings Volume 10396, 103961A (2017) https://doi.org/10.1117/12.2273834

KEYWORDS: Magnetic resonance imaging, Brain, Neuroimaging, Cognitive modeling, Magnetism, Statistical analysis, Artificial neural networks, Pattern recognition

Read Abstract +

Multiple sclerosis (MS) is an inflammatory and demyelinating disease associated with neurodegenerative processes that lead to brain structural changes. The disease affects mostly young adults, but 3–5% of cases has a pediatric onset (POMS). Magnetic Resonance Imaging (MRI) is generally used for diagnosis and follow-up in MS patients, however the most common MRI measures (e.g. new or enlarging T2-weighted lesions, T1-weighted gadolinium- enhancing lesions) have often failed as surrogate markers of MS disability and progression. MS is clinically heterogenous with symptoms that can include both physical changes (such as visual loss or walking difficulties) and cognitive impairment. 30–50% of POMS experience prominent cognitive dysfunction. In order to investigate the association between cognitive measures and brain morphometry, in this work we present a fully automated pipeline for processing and analyzing MRI brain scans. Relevant anatomical structures are segmented with FreeSurfer; besides, statistical features are computed. Thus, we describe the data referred to 12 patients with early POMS (mean age at MRI: 15.5 ± 2.7 years) with a set of 181 structural features. The major cognitive abilities measured are verbal and visuo-spatial learning, expressive language and complex attention. Data was collected at the Department of Basic Sciences, Neurosciences and Sense Organs, University of Bari, and exploring different abilities like the verbal and visuo-spatial learning, expressive language and complex attention. Different regression models and parameter configurations are explored to assess the robustness of the results, in particular Generalized Linear Models, Bayes Regression, Random Forests, Support Vector Regression and Artificial Neural Networks are discussed.

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