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对fMRI数据处理管道进行系统评估,以实现一致的功能连接学.

Andrea I Luppi1,2,3,4, Helena M Gellersen5,6, Zhen-Qi Liu7

  • 1Division of Anaesthesia, University of Cambridge, Cambridge, UK. al857@cam.ac.uk.

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概括
此摘要是机器生成的。

选择正确的数据处理管道对于准确的大脑网络分析至关重要. 本研究确定了休息状态功能性MRI的最佳管道,以确保可靠的功能连接学研究.

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科学领域:

  • 神经科学是一个神经科学.
  • 网络科学 网络科学
  • 数据科学数据科学数据科学

背景情况:

  • 通过网络科学研究大脑区域之间的功能相互作用.
  • 休息状态功能性MRI (fMRI) 是绘制大脑网络的关键工具.

研究的目的:

  • 系统地评估768个数据处理管道,用于静态fMRI网络的重建.
  • 为了确定管道,最大限度地减少运动工件,并提高测试-重新测试可靠性.
  • 寻找敏感于学科间和实验变化的管道.

主要方法:

  • 基于大脑分片,连接性定义和全球信号回归的768条管道的评估.
  • 标准包括尽量减少运动混和测试-重新测试差异.
  • 评估对受试者之间的差异和实验效应的敏感性.

主要成果:

  • 在管道适合功能连接经济学方面发现了广泛和系统的变化.
  • 大多数管道至少不符合一个标准,导致潜在的误导性结果.
  • 一个最佳管道子集在不同的数据集和时间尺度上始终满足所有标准.

结论:

  • 数据处理管道的选择显著影响功能连接经济学的发现.
  • 存在最优的管道,确保强大可靠的大脑网络分析.
  • 提供了建议,以指导未来该领域的最佳实践.