Resting-state networks (RSNs) has been exploited to disentangle spatial and temporal dynamics in epilepsy (Centeno et al,2014). Most ofthe studies are based on blood-oxygenation-level-dependent (BOLD) combined with independent component analysis (ICA) to separatesignals into maps of covarying voxels (Smith et al., 2009), although promising results have been achieved in this field with Arterial SpinLabeling (ASL) (Jann et al,2015).Here, we aim to: 1) assess the ability of ASL combined with two preprocessing pipelines in detecting ICA based RSNs, compared toliterature networks; 2) quantify spatial RSNs in controls (HCs) and epilepsy patients (PTs); 3) evaluate the temporal properties of spatialindependent components (IC) for measuring brain connectivity.
Exploring the Brain Connectivity of Epileptic Resting State Networks using Arterial Spin Labeling
STORTI, Silvia Francesca;Boscolo Galazzo, Ilaria;PIZZINI, Francesca;MENEGAZ, Gloria;
2016-01-01
Abstract
Resting-state networks (RSNs) has been exploited to disentangle spatial and temporal dynamics in epilepsy (Centeno et al,2014). Most ofthe studies are based on blood-oxygenation-level-dependent (BOLD) combined with independent component analysis (ICA) to separatesignals into maps of covarying voxels (Smith et al., 2009), although promising results have been achieved in this field with Arterial SpinLabeling (ASL) (Jann et al,2015).Here, we aim to: 1) assess the ability of ASL combined with two preprocessing pipelines in detecting ICA based RSNs, compared toliterature networks; 2) quantify spatial RSNs in controls (HCs) and epilepsy patients (PTs); 3) evaluate the temporal properties of spatialindependent components (IC) for measuring brain connectivity.
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