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术前和术后脑肿瘤多模态磁共振成像数据,经过优化可用于大规模计算建模。

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

机构信息

Department of Data Analysis, Ghent University, Ghent, Belgium.

Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.

出版信息

Sci Data. 2022 Nov 5;9(1):676. doi: 10.1038/s41597-022-01806-4.

Abstract

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.

摘要

我们提供了一组磁共振成像(MRI)数据(T1、扩散、BOLD),这些数据是在 25 名脑瘤患者手术前和手术后 6 个月采集的,同时还提供了肿瘤掩模,以及 11 名对照组患者(从患者的护理人员中招募)的数据。该数据集还包含通过标准化问卷获得的行为和情感评分。为了模拟大脑的个性化计算模型,我们还提供了结构连接矩阵,这是使用 The Virtual Brain 等工具进行全脑建模所必需的。此外,我们还提供了感兴趣区域的血氧水平依赖成像时间序列的平均值,以便与模拟结果进行比较。我们还提供了每个感兴趣区域的平均静息状态血液动力学响应函数,以及每个体素的形状图。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/98d8/9637199/5c3c4545276e/41597_2022_1806_Fig1_HTML.jpg

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