## 2 Materials and Methods
### Data Acquisitions and Preprocessing  
All fMRI acquisitions were performed using a 3 Tesla MRI (Magnetom Skyra, Siemens medical systems, Erlangen, Germany) with slice thickness of 4 mm, which yielded a voxel size of \(3\times 3\times 4\) mm\({}^{3}\) and consequently a 3-dimensional image of 42 px \(\times\) 51 px \(\times\) 34 channels. The fMRI sequences were acquired for each patient in the following order: an anatomical 3D T1, one resting-state acquisition, and two task-induced activities. All patients and healthy volunteers enrolled did not have language impairment at the moment of the fMRI acquisition and during the surgical procedure. The first three volumes acquired in each sequence were discarded to allow the stabilization of the magnetic field gradients.  
Data preprocessing was performed using MatLab (The MathWorks, Natick, MA, USA) with Anatomy, SPM8, and VBM 12 toolboxes. The preprocessing of fMRI data was performed using the following steps: slice-timing correction, realignment to the first volume of the first session, and unwrapping to correct head movements and magnetic distortions. Images were then segmented and normalized to the Montreal Neurological Institute template [42]. Rs-fMRI data of each patient were segmented into 55 spatial independent components (ICs) through an intrinsic connectivity network spatial independent component analysis (SICA) approach employing a customized version of the infomax algorithm running under Matlab [18,43]. ICs correspond to 3D fMRI activation volumes of brain areas with spontaneous synchronous activity.  
The identification of reference fMRI brain networks was performed manually for each subject by two independent and experienced reviewers ([PERSON]-M.L. and [PERSON]) without any disagreement. Based on fMRI activation peaks and spatial distribution of these activations, we selected seven main networks of DMN, LANG, VAN, IFPCN, rFPCN, SAL, and DAN among the 55 generated ICs for each patient. The annotated images were used in two versions: full images (connectivity map) and corresponding thresholded images. Figure 2 shows an image sample of networks. Individual spatial components were thresholded at z = 2 at the cluster level, corresponding to the 5% most activated voxels in each intrinsic connectivity network. This methodology is consistent with the literature and allowed us to overcome the background activation noise to identify the anatomical location of specifically activated brain areas [44].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Unhealthy Patient Image Data Description**} \\ \hline  & **Files Provided** & **Description** \\ \hline
1 & Lesion.nii & This file is the binary mask for the brain tumor, each corresponds to a patients \\ \hline
2 & Grey Matter mask (mrwp1) & Is the mask for the gray matter (useful since the activation are all in the gray matter) \\ \hline
3 & White Matter mask (mrwp2) & Is the mask for the white matter (no activation inside the white matter, but may be a good way to estimate the brain deformations linked to the tumor and the peritumoral edema) \\ \hline
4 & Cerebrospinal fluid mask (mrwp3) & The mask for the cerebrospinal fluid (like for the white matter, no activation inside, but may be useful to estimate brain deformations) \\ \hline
5 & Whole brain–white gray matter (wms) & The whole brain (white and gray matter) in T1 anatomical MRI sequence, with the skin and skull clipped \\ \hline
6 & Whole brain (wmrs) & This provides view of the whole brain cerebrospinal fluid, skull and skin included \\ \hline \hline \end{tabular}
\end{table}
Table 1: Unhealthy patient data description.