阿法西亚恢复队列,一个开源的慢性中风存储库
Makayla Gibson1, Roger Newman-Norlund1, Leonardo Bonilha2
1Department of Psychology, University of South Carolina, Columbia, SC, USA.
Scientific data
|September 9, 2024
概括
这项研究共享了230名长期中风幸存者的神经成像数据,使得对中风恢复和大脑健康进行新的研究. 该数据集包括成像,人口统计和行为测量,以推进机器学习应用程序.
科学领域:
- 神经成像是一种神经成像.
- 脑卒中研究 脑卒中研究
- 数据科学数据科学数据科学
背景情况:
- 神经成像数据共享对于科学进步至关重要,但在中风研究中是有限的.
- 脑卒中是导致残疾的主要原因,但纵向成像和行为数据很少.
- 现有的数据集缺乏用于中风研究的新型应用所需的全面数据.
研究的目的:
- 发布第一个全面的数据集从阿法西亚恢复队列.
- 提供230名患有失语症的慢性中风幸存者的成像,人口和行为数据.
- 促进用于中风研究的机器学习工具的开发.
主要方法:
- 收集和策划的神经成像数据 (功能和扩散成像).
- 从参与者那里收集人口统计和行为指标.
- 开发和共享用于数据分析和预测的脚本.
主要成果:
- 阿法西亚恢复队列数据集现在是公开的.
- 该数据集包括230名患有失言症的慢性中风幸存者.
- 包含的脚本展示了成像数据对损伤的预测能力.
结论:
- 大量,多样化的数据集对于机器学习的进步至关重要.
- 临床数据共享,就像这个队列一样,可以改善中风诊断和预后.
- 该资源将有助于自动检测脑损伤,识别白质超强度和评估大脑健康.
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