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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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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. |
format | Online Article Text |
id | pubmed-9637199 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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