178 Chapter 7 Pre-processing Pre-processing steps were performed using the Oxford Centre for Functional MRI of the BRAIN (FMRIB) Software Library (FSL – version 6.0).25 First, both the T1 and T2FLAIR images were brain extracted using BET 26. Tissue segmentation into grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) was performed on the T1 image using FSL Automated Segmentation Tool (FAST).27 Additionally, an edema mask was created based on the T1 and T2FLAIR images using the lesion growth algorithm as implemented in the Lesion Segmentation Tool (LST, https://www.statistical-modelling.de/lst.html) for SPM.28 Based on previous experience, the initial threshold was set at 0.14. The resulting edema mask was manually adapted to eliminate any false positives or false negatives from the LST edema mask. BOLD data were motion corrected (MCFLIRT)26, corrected for geometric distortion using TOPUP 29,30 and linear spatial co-registered to the T1 image using the ‘epi_reg’ function 26,31. QSM maps were calculated from the raw phase and magnitude data of the ME-GRE images using the MEDI toolbox.32 The local field was estimated by the projection onto dipole field (PDF) method33, and susceptibility values were computed by Morphology Enabled Dipole Inversion (MEDI)34. Data analysis MRI analysis CVR maps were derived using the open-source seeVR toolbox (Figure 1).35 In brief, in order to remove signal contribution from large veins that might overshadow tissue responses, a modified whole-brain mask was generated using the ‘remLV.m’ function of the seeVR toolbox.35 For this,a temporal noise-to-signal (tNSR) map was calculated by taking the inverse of the temporal signal-to-noise (tSNR) map. Next, voxels showing values higher than the 98th percentile tNSR value were removed from the original whole brain-mask to remove the large veins. This modified mask was then used in subsequent analyses.35 Next, a manual bulk alignment was performed between the PetCO2 and average GM time-series to minimize alignment errors that can occur when using automated correlation methods. Thereby, any bulk delays between end-tidal gas measurements at the lungs and BOLD signal responses in the brain were accounted for. Residual motion signals with a correlation higher than 0.3 with the GM time-series, along with a linear drift term were regressed out using a general linear model. BOLD data was then temporally de-noised using a waveletbased approach.36 A linear regression was performed between the bulk-aligned PetCO2 trace with each processed BOLD voxel time-series. The slope of this linear regression was taken as the CVR (%ΔBOLD/mmHg PetCO2).
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