Abstract #3994

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

Manuela Moretto^1,2, Erica Silvestri^1,2, Marco Castellaro^1,2, Mariagiulia Anglani³, Silvia Facchini^1,4, Elena Monai^1,4, Domenico D'Avella^1,4, Alessandro Della Puppa⁵, Diego Cecchin^1,6, Maurizio Corbetta^1,4,7, and Alessandra Bertoldo^1,2

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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