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MRI
BTC_preop

OpenNeuro Accession Number: ds001226Files: 436Size: 5.28GB

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BTC_preop
BTC_preop
  •   CHANGES
  •   dataset_description.json
  •   participants.tsv
  •   README
  • derivatives
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README

Brain Tumor Connectomics Data

Pre-operative data of 11 glioma patients, 14 meningioma patients and 11 control subjects that were 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. 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. The resting state HRF is obtained with the 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.

A companion dataset, containing the postoperatory data, is accessible at https://openneuro.org/datasets/ds002080

Comments

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By aertshannelore@gmail.com - almost 3 years ago
Snapshot created with DOI.

Authors

  • Aerts, H
  • Marinazzo, D

Available Modalities

MRI

Versions

Draft
Updated: 2022-03-10
Versions

Tasks

rest

Uploaded by

Hannelore Aerts on 2018-02-12 - over 4 years ago

Last Updated

2022-03-10 - 4 months ago

Sessions

1

Participants

36

Dataset DOI

doi:10.18112/openneuro.ds001226.v4.0.0

License

CC0

Acknowledgements

We would like to thank Prof. Dr. Van Roost and Prof. Dr. Eric Achten for their support in recruiting and testing patients at the Ghent University Hospital. We are also grateful to Stephanie Bogaert, Pieter Vandemaele and Ben Jeurissen for their technical assistance in setting up the scanning protocols. Further, we would like to thank Stephanie Bogaert, Robby De Pauw, Iris Coppieters, Jeroen Kregel, Mireille Augustijn, Helena Verhelst, and Hannes Almgren for their help in acquiring the data.

How to Acknowledge

This dataset was obtained from the OpenNeuro database.

Funding

  • Special Research Funds (BOF) of the University of Ghent (01MR0210 and 01J10715)
  • Interuniversity Attraction Poles Program of the Belgian Federal Government (P7/11)

References and Links

Ethics Approvals

  • N/A
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