Brain Tumor Connectomics Data

Post-operative data of 7 glioma patients, 12 meningioma patients (1 [sub-PAT11] with bad data quality) and 10 control subjects.
Out of the 11 glioma patients, 14 meningioma patients, and 11 healthy controls that were included pre-surgically ("BTC_preop, ds001226" on OpenNeuro), 7 glioma patients (1 drop-out, 1 no resection, 2 end of study), 12 meningioma patients (1 drop-out, 1 MRI not possible because of cochlear implant), and 10 healthy controls (1 drop-out) agreed to participate post-operatively. 

Data used in the papers

Aerts H, Schirner M, Jeurissen B, Van Roost D, Achten E, Ritter P, Marinazzo D. Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain. eNeuro. 2018 Jun 4;5(3):ENEURO.0083-18.2018. doi: 10.1523/ENEURO.0083-18.2018. PMID: 29911173; PMCID: PMC6001263.

and

Aerts H, Schirner M, Dhollander T, Jeurissen B, Achten E, Van Roost D, Ritter P, Marinazzo D. Modeling brain dynamics after tumor resection using The Virtual Brain. Neuroimage. 2020 Jun;213:116738. doi: 10.1016/j.neuroimage.2020.116738. Epub 2020 Mar 16. PMID: 32194282.

Contact information:
Name: Hannelore Aerts & Daniele Marinazzo
Email: daniele.marinazzo@ugent.be

Compared to the initial database, 6 patients were excluded: 2 because of glioma grade 4, 3 because of subtentorial tumor, 1 because of absence of MRI data (subdural grid).

Of all subjects the following data were acquired:
- T1w MPRAGE anatomical scan (anat)
- resting-state fMRI (func)
- multi-shell HARDI diffusion-weighted MRI (dwi, acq=AP)
- short DWI with reverse phase encoding directions (dwi, acq=PA)
- cognitive assessment using the Cambridge Neuropsychological Test Automated Battery (CANTAB): MOT, RVP, RTI, SSP & SOC
- questionnaires assessing demographic information, lifestyle habits & emotional functioning

The "derivatives" folder contains

- tumor masks, obtained with a combination of manual delineation and disconnectome
- time series, structural and functional connectivity matrices, and resting state HRF for each ROI.
- rsHRF parameters obtained with the rsHRF toolbox described here Wu GR, Colenbier N, Van Den Bossche S, Clauw K, Johri A, Tandon M, Marinazzo D. rsHRF: A toolbox for resting-state HRF estimation and deconvolution. Neuroimage. 2021 Dec 1;244:118591. doi: 10.1016/j.neuroimage.2021.118591. Epub 2021 Sep 21. PMID: 34560269.
- fMRI quality control: Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, Gorgolewski KJ; MRIQC: Advancing the Automatic Prediction of Image Quality in MRI from Unseen Sites; PLOS ONE 12(9):e0184661; doi:10.1371/journal.pone.0184661.
- diffusion quality control using
Bastiani M, Cottaar M, Fitzgibbon SP, Suri S, Alfaro-Almagro F, Sotiropoulos SN, Jbabdi S, Andersson JLR. Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction. Neuroimage. 2019 Jan 1;184:801-812. doi: 10.1016/j.neuroimage.2018.09.073. Epub 2018 Sep 26. PMID: 30267859; PMCID: PMC6264528.
G. Theaud and M. Descoteaux, dMRIQCpy: a python-based toolbox for diffusion MRI quality control and beyond, International Symposium in Magnetic Resonance in Medicine (ISMRM 2022).
The structural connectivity (SC) is derived with the TVB pipeline (https://github.com/BrainModes/TVB-empirical-data-pipeline) with manual segmentation when necessary.
The regions are according to the Desikan-Killiany atlas.

A companion dataset, containing the preoperatory data, is accessible at https://openneuro.org/datasets/ds001226