108 citations · 116 across the 5 of their papers we have counts for
5 papers
Deep neuroevolution to predict primary brain tumor grade from functional MRI adjacency matrices
Joseph Stember, Mehrnaz Jenabi, Luca Pasquini +3
Whereas MRI produces anatomic information about the brain, functional MRI (fMRI) tells us about neural activity within the brain, including how various regions communicate with eac…
Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder
Joseph Stember, Danielle Stember, Luca Pasquini +3
Purpose : Because functional MRI (fMRI) data sets are in general small, we sought a data efficient approach to resting state fMRI classification of autism spectrum disorder (ASD) v…
Monolingual and bilingual language networks in healthy subjects using functional MRI and graph theory
Qiongge Li, Luca Pasquini, Gino Del Ferraro +4
Pre-surgical language mapping with functional magnetic resonance imaging (fMRI) is routinely conducted to assist the neurosurgeon in preventing damage to brain regions responsible…
Core language brain network for fMRI-language task used in clinical applications
Qiongge Li, Gino Del Ferraro, Luca Pasquini +3
Functional magnetic resonance imaging (fMRI) is widely used in clinical applications to highlight brain areas involved in specific cognitive processes. Brain impairments, such as t…
Finding influential nodes for integration in brain networks using optimal percolation theory
Gino Del Ferraro, Andrea Moreno, Byungjoon Min +7
Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficientl…