M
Maria A. Rocca
Researcher at Vita-Salute San Raffaele University
Publications - 647
Citations - 29881
Maria A. Rocca is an academic researcher from Vita-Salute San Raffaele University. The author has contributed to research in topics: Multiple sclerosis & Magnetic resonance imaging. The author has an hindex of 83, co-authored 556 publications receiving 25283 citations. Previous affiliations of Maria A. Rocca include University at Buffalo & Katholieke Universiteit Leuven.
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Journal ArticleDOI
Atypical idiopathic inflammatory demyelinating lesions: prognostic implications and relation to multiple sclerosis (vol 260, pg 2016, 2013)
M Wallner-Blazek,Alex Rovira,Massimo Filippi,Maria A. Rocca,David Miller,Klaus Schmierer,Jette L. Frederiksen,Achim Gass,Hugo Pereira Pinto Gama,Charles Peter Tilbery,Antônio José da Rocha,José Flores,Frederik Barkhof,Alexandra Seewann,Jacqueline Palace,Tarek A. Yousry,Xavier Montalban,Christian Enzinger,Franz Fazekas +18 more
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Editorial: Plasticity in Multiple Sclerosis: From Molecular to System Level, from Adaptation to Maladaptation
Daniel Zeller,Maria A. Rocca +1 more
TL;DR: An update on plasticity in MS is provided and a variety of different research tools, including behavioral, neurophysiological, and neuroimaging techniques, which have addressed neuroplasticity at different systems are presented, from motor to visual and to cognitive.
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The role of cerebellar damage in explaining disability and cognition in multiple sclerosis phenotypes: a multiparametric MRI study
Raffaello Bonacchi,Alessandro Masoni,Elisabetta Pagani,Olga Marchesi,Massimo Filippi,Maria A. Rocca +5 more
TL;DR: In patients with cerebellar disability, three clusters with homogenous MRI metrics are found: patients with high brain lesion volumes, those with marked cerebellum GM atrophy and patients with severe cord damage, which might improve knowledge of MRI-clinical correlations.
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Prediction of the information processing speed performance in multiple sclerosis using a machine learning approach in a large multicenter magnetic resonance imaging data set
Chiara Marzi,Alessandro d’Ambrosio,Stefano Diciotti,Alvino Bisecco,Manuela Altieri,Massimo Filippi,Maria A. Rocca,Loredana Storelli,Patrizia Pantano,Silvia Tommasin,Rosa Cortese,N. De Stefano,Gioacchino Tedeschi,Antonio Gallo,The Inni Network +14 more
TL;DR: In this article , the contribution of brain MRI structural volumes in the prediction of information processing speed (IPS) deficits when combined with demographic and clinical features was assessed, through machine learning techniques.
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Evaluation and training of hands and feet movements performed with different strategies: a kinematic study.
TL;DR: Combination of movements of the hands and feet is easier when planning the movements with respect to the upper or lower segments of the body rather than right and left side.