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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neuroinform. | doi: 10.3389/fninf.2018.00101

Arterial spin labeling reveals disrupted brain networks and functional connectivity in drug-resistant temporal epilepsy

 Ilaria Boscolo Galazzo1*,  Silvia F. Storti1,  Anna Barnes2, Bianca De Blasi3,  Enrico De Vita4, Matthias Koepp5,  John S. Duncan2, Ashley Groves2, Francesca B. Pizzini6,  Gloria Menegaz1 and Francesco Fraioli2
  • 1Dipartimento di Informatica, Università degli studi di Verona, Italy
  • 2Institute of Nuclear Medicine, University College London, United Kingdom
  • 3Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom
  • 4School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom
  • 5Institute of Neurology, University College London, United Kingdom
  • 6Neuroradiologia, Azienda Ospedaliera Universitaria Integrata Verona, Italy

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 (controlsAll 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.

Keywords: Arterial Spin Labeling, functional connectivity, resting-state, ICA, Perfusion, Epilepsy

Received: 30 Jul 2018; Accepted: 12 Dec 2018.

Edited by:

Sen Song, Tsinghua University, China

Reviewed by:

Federico Giove, Centro Fermi - Museo storico della fisica e Centro studi e ricerche Enrico Fermi, Italy
Kay Jann, University of Southern California, United States  

Copyright: © 2018 Boscolo Galazzo, Storti, Barnes, De Blasi, De Vita, Koepp, Duncan, Groves, Pizzini, Menegaz and Fraioli. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Ilaria Boscolo Galazzo, Dipartimento di Informatica, Università degli studi di Verona, Verona, 37134, Veneto, Italy,