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

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...

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Autores principales: Aerts, Hannelore, Colenbier, Nigel, Almgren, Hannes, Dhollander, Thijs, Daparte, Javier Rasero, Clauw, Kenzo, Johri, Amogh, Meier, Jil, Palmer, Jessica, Schirner, Michael, Ritter, Petra, Marinazzo, Daniele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9637199/
https://www.ncbi.nlm.nih.gov/pubmed/36335218
http://dx.doi.org/10.1038/s41597-022-01806-4
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author Aerts, Hannelore
Colenbier, Nigel
Almgren, Hannes
Dhollander, Thijs
Daparte, Javier Rasero
Clauw, Kenzo
Johri, Amogh
Meier, Jil
Palmer, Jessica
Schirner, Michael
Ritter, Petra
Marinazzo, Daniele
author_facet Aerts, Hannelore
Colenbier, Nigel
Almgren, Hannes
Dhollander, Thijs
Daparte, Javier Rasero
Clauw, Kenzo
Johri, Amogh
Meier, Jil
Palmer, Jessica
Schirner, Michael
Ritter, Petra
Marinazzo, Daniele
author_sort Aerts, Hannelore
collection PubMed
description 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.
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spelling pubmed-96371992022-11-07 Pre- and post-surgery brain tumor multimodal magnetic resonance imaging data optimized for large scale computational modelling Aerts, Hannelore Colenbier, Nigel Almgren, Hannes Dhollander, Thijs Daparte, Javier Rasero Clauw, Kenzo Johri, Amogh Meier, Jil Palmer, Jessica Schirner, Michael Ritter, Petra Marinazzo, Daniele Sci Data Data Descriptor 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. Nature Publishing Group UK 2022-11-05 /pmc/articles/PMC9637199/ /pubmed/36335218 http://dx.doi.org/10.1038/s41597-022-01806-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Aerts, Hannelore
Colenbier, Nigel
Almgren, Hannes
Dhollander, Thijs
Daparte, Javier Rasero
Clauw, Kenzo
Johri, Amogh
Meier, Jil
Palmer, Jessica
Schirner, Michael
Ritter, Petra
Marinazzo, Daniele
Pre- and post-surgery brain tumor multimodal magnetic resonance imaging data optimized for large scale computational modelling
title Pre- and post-surgery brain tumor multimodal magnetic resonance imaging data optimized for large scale computational modelling
title_full Pre- and post-surgery brain tumor multimodal magnetic resonance imaging data optimized for large scale computational modelling
title_fullStr Pre- and post-surgery brain tumor multimodal magnetic resonance imaging data optimized for large scale computational modelling
title_full_unstemmed Pre- and post-surgery brain tumor multimodal magnetic resonance imaging data optimized for large scale computational modelling
title_short Pre- and post-surgery brain tumor multimodal magnetic resonance imaging data optimized for large scale computational modelling
title_sort pre- and post-surgery brain tumor multimodal magnetic resonance imaging data optimized for large scale computational modelling
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9637199/
https://www.ncbi.nlm.nih.gov/pubmed/36335218
http://dx.doi.org/10.1038/s41597-022-01806-4
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