# Brain Network Modularity and Resilience Signaled by Betweenness Centrality Percolation Spiking
## 2 Materials and Methods
### Human Neuroimaging Methodology
#### 2.2.2 Human Model Image Preprocessing and Network Generation  
Resting-state processing was carried out using software tools in Analysis of Functional NeuroImages (AFNI) [26], FMRIB Software Library (FSL) [27], and Advanced Normalization Tools (ANTs) [28]. A schematic of the image processing pipeline can be found in Figure 1. First, (1) the brain was extracted for both anatomical and functional scans using FSL BET, then (2) the scans were motion-corrected using FSL MCFLIRT and filtering signal drift (high-pass filtering below 0.009 Hz) prior to running subject independent component analysis (ICA) using FSL MELODIC. This initial subject ICA was used to detect noisy signal components from thirty total components per subject. A trained observer labeled noisy components. These components were suppressed using a soft (\"non-aggressive\") regression approach, as implemented in FMRIB Software Library (FSL 6.0.3) using fsl_regfilt [27]. A low-pass filter (>0.12 Hz) and spatial smoothing (5 mm FWHM) was then applied to the fMRI scans. (3) Preprocessed anatomical and fMRI scans were aligned to the MNII152 template (2 mm version). The extracted brain maps were linearly registered to the template using the FSL linear registration tool (FLIRT) [27]. The linear registration output was then nonlinearly warped to the template space using ANTs. Anatomical-to-template linear and nonlinear transformation matrices were applied to fMRI scans at a later stage. Time-series functional images were split into 197 individual volumes, and the first in the series was linearly aligned to the anatomical scan using FLIRT. ANTs were used to warp the lower resolution functional images to their structural counterpart (using a single stage step deformable b-spline syn with a 26-step b-spline distance). (4) Linear and nonlinear warping matrices for fMRI-to-anatomical alignment were applied to individual scans in the time series, then the merged 4D functional time series were moved to the atlas space using the prior anatomical-to-template transformation matrices. A total of 300 ROI masks, divided into 150 left and 150 right ROIs, were included in our analyses. (5) Nodes were generated under the guidance of the Schaefer 300 functional parcellation [29]. Center voxel coordinates (see above) were used for 3D network visualizations in BrainNet viewer in MATLAB [30]. (6) Signal time series were extracted from preprocessed fMRI scans with the assistance of ROI mask overlays. This generated 300 individual ROI text files per subject that contained L2-normalized resting-state signals as a vector of 197 data points. (7) The time-series files were used in cross-correlations and in calculations of Pearson r coefficients for every pairwise combination of ROIs (1 dCorrelate in AFNI). The resulting number of pairwise correlations was 44.850 per subject (after removing 300 self-correlations). Correlation coefficients were imported to MATLAB and [PERSON]'s transform was applied to ensure a normal distribution of z values prior to analyses. (8) Correlation matrices were then normalized in MATLAB from zero to one. (9) Matrices were thresholded across different graph density thresholds (5-30%) to remove spurious edges and negative correlations. That is, for example, in the normalization step, all values are rescaled to the range \([-1,1]\), and in thresholding, all values that fall below the threshold are turned to 0 (e.g., for 5% threshold, all values less than 0.95 are removed). Figure 1: Overview of connectomic pipeline from human neuroimaging to network generation. (1) The brain of each subject was extracted using FSL BET. (2) fMRI time series were then despiked, motion-corrected, and detrended. Nuisance signals were removed using ICA-based regression and the signal underwent low-pass filtering and spatial smoothing. (3) Preprocessed anatomical and fMRI scans were aligned to the MNI152 template (2 mm version).