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随着时间变化的功能连接,因为Wishart的过程.

Onno P Kampman1, Joe Ziminski1,2, Soroosh Afyouni1,3,4

  • 1Department of Psychology, University of Cambridge, Cambridge, United Kingdom.

Imaging neuroscience (Cambridge, Mass.)
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PubMed
概括

维沙特过程 (Wishart processes,简称WP) 在fMRI数据中有效估计时间变化的功能连接 (TVFC). 基准测试表明WP具有竞争力的表现,在特定的预测任务中表现优于其他方法,并减少虚假阳性.

关键词:
在Wishart的流程中,Wishart的流程.大脑的连接性大脑的连接性功能性核磁共振成像 (MRI) 的使用.功能连接性的功能连接性方法 基准测试 方法 基准测试时间变化的功能连接性.

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科学领域:

  • 神经成像是一种神经成像.
  • 计算神经科学是一种神经科学.
  • 统计建模 统计建模

背景情况:

  • 功能磁共振成像 (fMRI) 测量大脑活动.
  • 时间变化的功能连接 (TVFC) 捕捉了动态的大脑区域的相互作用.
  • 估计TVFC对于理解大脑功能至关重要.

研究的目的:

  • 评估Wishart过程 (WP) 在fMRI中对TVFC估计的实用性.
  • 将WP的性能与已建立的TVFC方法进行比较.
  • 评估WP在神经成像应用中的实际可行性.

主要方法:

  • 为TVFC方法开发了一个全面的基准测试框架.
  • 包括模拟,表型预测,测试-重新测试,大脑状态分析和刺激预测任务.
  • 利用可扩展的近似推断和开源库来实现WP.

主要成果:

  • 在所有基准中,Wishart 流程 (WP) 显示出具有竞争力的表现.
  • 在外部刺激预测方面,WP的表现优于滑窗和DCC-MGARCH方法.
  • 在TVFC零模型中,WP对虚假阳性反应的敏感性降低.

结论:

  • 从fMRI数据中估计时间变化的功能连接的Wishart过程是一个可行的和有效的工具.
  • 与传统方法相比,WP提供了优势,特别是在预测建模和统计稳定性方面.
  • 这项研究验证了WP作为动态大脑连接分析的强大方法.