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Abstract #3994

An ICA-based approach to study altered resting state functional networks in brain tumors

Manuela Moretto1,2, Erica Silvestri1,2, Marco Castellaro1,2, Mariagiulia Anglani3, Silvia Facchini1,4, Elena Monai1,4, Domenico D'Avella1,4, Alessandro Della Puppa5, Diego Cecchin1,6, Maurizio Corbetta1,4,7, and Alessandra Bertoldo1,2
1Padova Neuroscience Center, University of Padova, Padova, Italy, 2Department of Information Engineering, University of Padova, Padova, Italy, 3Neuroradiology Unit, University of Padova, Padova, Italy, 4Department of Neuroscience, University of Padova, Padova, Italy, 5Department of Neurosurgery, Neuroscience, Psychology, Pharmacology, and Child Health, University of Firenze, Firenze, Italy, 6Department of Medicine, Unit of Nuclear Medicine, University of Padova, Padova, Italy, 7Department of Neurology, Radiology, Neuroscience, Washington University School of Medicine, St Louis, MO, United States

Brain tumors can alter not only functions located in the perilesional area, but also the distal ones. Thus, the possibility to inform preoperatively surgeons about the state of preservation/alteration of a network could be a powerful aid for a better patient outcome. In this work we used independent component analysis (ICA) to map resting state networks (RSNs) at the single-subject level characterizing their alterations in terms of cosine similarity spatial patterns. Comparing the patient-specific spatial maps with those obtained for a group of healthy controls, we defined the presence of an alteration for each of the 44 analyzed RSNs.

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