Arterial Spin Labeling Reveals Disrupted Brain Networks and Functional Connectivity in Drug-Resistant Temporal Epilepsy - Boscolo Galazzo, Ilaria; Storti, Silvia Francesca; Barnes, Anna; De Blasi, Bianca; De Vita, Enrico; Koepp, Matthias; Duncan, John Sidney; Groves, Ashley; Pizzini, Francesca Benedetta; Menegaz, Gloria; Fraioli, Francesco (Frontiers in Neuroinformatics, 2019)
Resting-state networks (RSNs) and functional connectivity (FC) have been increasingly exploited for mapping brain activity and identifying abnormalities in pathologies, including epilepsy. The majority of studies currently available are based on blood-oxygenation-level-dependent (BOLD) contrast in combination with either independent component analysis (ICA) or pairwise region of interest (ROI) correlations. Despite its success, this approach has several shortcomings as BOLD is only an indirect and qualitative measure of brain activity. Conversely, promising results have recently been achieved by arterial spin labeling (ASL) MRI, primarily developed to quantify brain perfusion. However, the wide application of ASL-based FC has been hampered by its complexity and relatively low robustness to noise, leaving several aspects of this approach still largely unexplored. In this study, we firstly aimed at evaluating the effect of noise reduction on spatio-temporal ASL analyses and quantifying the impact of two ad-hoc processing pipelines (basic and advanced) on connectivity measures. Once the optimal strategy has been defined, we investigated the applicability of ASL for connectivity mapping in patients with drug-resistant temporal epilepsy vs controls (10 per group), aiming at revealing between-group voxel-wise differences in each RSN and ROI-wise FC changes. We firstly found ASL was able to identify the main network (DMN) along with all the others generally detected with BOLD but never previously reported from ASL. For all RSNs, ICA-based denoising (advanced pipeline) allowed to increase their similarity with the corresponding BOLD template. ASL-based RSNs were visibly consistent with literature findings, however, group differences could be identified in the structure of some networks. Indeed, statistics revealed areas of significant FC decrease in patients within different RSNs, as DMN and Cerebellum (CER), while significant increases were found in some cases, as the Visual networks. Finally, the ROI-based analyses identified several inter-hemispheric dysfunctional links (controls>patients) mainly between areas belonging to the DMN, right-left Thalamus and right-left Temporal Lobe (TL). Conversely, fewer connections, predominantly intra-hemispheric, showed the opposite pattern (controls<patients). All these elements provide novel insights into the pathological modulations characterizing a ‘network disease’ as epilepsy, shading light on the importance of perfusion-based approaches to identify the disrupted areas and communications between brain regions.