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

Hannelore Aerts, Nigel Colenbier, Hannes Almgren, Thijs Dhollander, Javier Rasero Daparte, Kenzo Clauw, Amogh Johri, Jil Meier, Jessica Palmer, Michael Schirner, Petra Ritter & Daniele Marinazzo

We 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.

Sci Data. 9, 676 (2022)


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