Left-hemisphere glioma drives systematic patterns of contralesional functional connectivity

[Abstract to be included upon release of embargo].This dataset contains the following:README.md - Read this document first! It contains critical usage info. It also contains more information about usage for scripts and what data is contained in the excel/csv files.fMRIPrep - contains fmriprep.sh script. Requirements: Docker (we used v20.10.12), freesurfer license (we do not actually use freesurfer in our command but the license is required to run fMRIPrep), fMRIPrep (we used v21.0.1). Note: we have found that pydeface performs inadequately to sufficiently deface (e.g.,remaining eye socket, ears) and therefore protect the anonymity of our subjects. Please contact Brad Mahon ([e-mail]) with Emma Strawderman ([e-mail]) in CC if you would like access to the original BIDS data used in this analysis. We would be more than happy to work with you to set up a data use agreement. Despite not providing the raw BIDS data, we have supplied the exact code used to preprocess the BIDS data in fMRIPrep.CONN - The pre-processed resting fMRI data outputted by fMRIPrep was analyzed using CONN (release 22.a) and SPM (release 12.7771) through MATLAB R2022a. We used the GUI interface so there is no code to provide; however, the published Supplementary Materials contains all details necessary to replicate the settings used in CONN. Additionally, we have provided the ROI-to-ROI connectivity results (conn_glioma, conn_controls) that are required to use the MATLAB code for SVM analyses.MATLAB - We used version 2022a. Requires the Statistics and Machine Learning Toolbox and Parallel Computing Toolbox (freely available from MathWorks). We provide the connectivity matrices in the form of MATLAB variables (i.e., load('variables.mat')), as well as the code to reconstruct them from the provided CONN first-level results. The code in svm_analysis.m is used for the data in Figure 2, 5 and Supplemental Figures 6, 7, and 9. Note that you may not get the exact same numerical values as we do given the inherent variability resulting from K-fold stratification over many iterations. Additionally, the end of the svm_analysis.m script contains code required to cluster patients by their ROI-to-ROI connectivity matrices (Supplementary Fig 2) - remaining code/files related to cluster analyses and to generate Figure 3 and Supplementary Fig 3 are in the subfolder called Clusters. Lastly, the subfolder ROI-specific contains the data and code required to replicate findings in Figure 4 and Supplementary Figures 4-5. Reminder: this is not ready-to-run software - paths are hardcoded, you must read comments in order to use appropriately. Make sure to read the README.md file!R Code - R code necessary to produce model performance violin plots and permutation test histograms.