Pre- and post-surgery brain tumor multimodal magnetic resonance imaging data optimized for large scale computational modelling

Author:

Aerts Hannelore,Colenbier Nigel,Almgren Hannes,Dhollander Thijs,Daparte Javier RaseroORCID,Clauw Kenzo,Johri Amogh,Meier Jil,Palmer Jessica,Schirner MichaelORCID,Ritter Petra,Marinazzo DanieleORCID

Abstract

AbstractWe present a dataset of magnetic resonance imaging (MRI) data (T1, diffusion, BOLD) acquired in 25 brain tumor patients before the tumor resection surgery, and six months after the surgery, together with the tumor masks, and in 11 controls (recruited among the patients’ caregivers). The dataset also contains behavioral and emotional scores obtained with standardized questionnaires. To simulate personalized computational models of the brain, we also provide structural connectivity matrices, necessary to perform whole-brain modelling with tools such as The Virtual Brain. In addition, we provide blood-oxygen-level-dependent imaging time series averaged across regions of interest for comparison with simulation results. An average resting state hemodynamic response function for each region of interest, as well as shape maps for each voxel, are also contributed.

Funder

Universiteit Gent

EC | Horizon 2020 Framework Programme

Deutsche Forschungsgemeinschaft

Berlin Institute of Health & Foundation Charité Johanna Quandt Excellence Initiative

Federaal Wetenschapsbeleid

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability

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