认知任务,解剖MRI和功能MRI数据评估自我调节的构造
Patrick G Bissett1, Ian W Eisenberg2, Sunjae Shim3
1Department of Psychology, Stanford University, Stanford, USA. pbissett@stanford.edu.
Scientific data
|July 20, 2024
概括
这项研究共享了103名健康成年人的神经成像和认知数据,研究自我调节. 开放式访问数据集包括fMRI和结构性MRI扫描,以及来自十个认知任务的行为数据.
科学领域:
- 神经科学是一个神经科学.
- 认知心理学 认知心理学
- 数据科学数据科学数据科学
背景情况:
- 自律对于适应性行为至关重要.
- 了解自我调节的神经基础需要全面的数据.
- 现有的数据集往往缺乏多模式神经成像和多样化的认知评估.
研究的目的:
- 分享一个丰富的,多模式数据集,用于自我调节研究.
- 促进对自我调节背后的认知和神经机制的研究.
- 为神经科学界提供一个标准化数据集 (BIDS).
主要方法:
- 从103名健康成年人获得基于任务的fMRI,静止状态的fMRI和结构性MRI.
- 在两个90分钟的扫描课程中进行了十个不同的认知任务.
- 收集的调查数据与神经成像和认知任务表现一起收集.
主要成果:
- 创建了一个包含认知,调查和神经成像数据的全面数据集.
- 数据通过OpenNeuro平台公开提供.
- 数据集遵循大脑成像数据结构 (BIDS) 标准的互操作性.
结论:
- 这种公开共享的数据集为调查自我监管提供了宝贵的资源.
- 多模式方法允许对认知,神经和自我报告措施进行综合分析.
- 标准化数据格式 (BIDS) 在自我调节研究中促进数据共享和可重复性.
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