Functional imaging data processing and analyses were performed using MATLAB R2014a (MathWorks, Natick, MA, USA;, SPM12 (Wellcome Department of Cognitive Neurology, UK;, and ASLtbx ( The first of 91 ASL volumes was used as the M0 image, with the remaining 90 volumes used as 45 control-label pairs (labeling first). A six-parameter rigid body motion spatial transformation was used to align the raw EPI time series. Then, the functional images were co-registered to the T1w images. The spurious motion component caused by the systematic label/control alternation was regressed out from the motion parameters before applying the transformation on the images. Each tag and control pair was subtracted to create 45 perfusion-weighted images. These images were used to create quantified maps of CBF using the software ASLtbx. Specifically, CBF was quantified as ml/100 g per minute (absolute CBF) using the equation recommended by the ASL white paper (13)f=ΔMλR1aexp(ωR1a)2M0α[1exp(τR1a)]1where f is the CBF, ΔM is the difference signal between the control and label acquisitions, R1a is the longitudinal relaxation rate of blood, τ is the labeling time, ω is the postlabeling delay time, α is the labeling efficiency, λ is the blood/tissue water partition coefficient, and M0 is approximated by the control image intensity. Four-dimensional CBF images were masked to remove out-of-brain voxels and normalized to the Montreal Neurological Institute (MNI) template in SPM12. CBF images were processed using partial volume correction in the native ASL spaces and subsequently normalized to MNI space using a linear affine transformation.

CBF-positive values were extracted from the voxels of the three concentric ROIs (necrotic core, solid tumor, and edema). The edema and solid tumor ROIs were obtained by subtraction (i.e., edema − solid part = real edema defined as “edema” in the manuscript; solid part − necrotic core = real solid, defined as “solid tumor”), while the necrotic ROI was kept the same (“necrotic core”). To verify the selectivity and specificity of the tES effect, we extracted the CBF absolute values from additional ROIs that did not show any sign of tumor invasion at the MRI scans in both the contralateral and ipsilateral hemisphere with respect to the lesion (“control ROIs”). Last, following recent guidelines for CBF analysis, we calculated also the normalized CBF by dividing the CBF absolute values by the healthy contralateral WM’s CBF (22).

As a control analysis, quantitative evaluation of CBF were also performed by using the automated Olea Sphere 3.0 postprocessing platform integrated in the MRI scan (Olea Medical SA, La Ciotat, France; The Bayesian method was applied to motion corrected images to calculate CBF maps. Given that Olea Sphere is designed for clinical reading of ASL images, the software was not suitable for additional custom analyses required by the study design (e.g., correction for CBF values of contralateral WM tissue). CBF values obtained on this platform were compared with those obtained by the ad hoc pipeline developed on MATLAB R2014a, SPM12, and ASLtbx. Raw CBF values were identical for the two pipelines, following the same preprocessing of T1w and ASL data.

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