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从任务唤起的有效连接中预测响应速度和年龄.

Shufei Zhang1,2, Kyesam Jung1,2, Robert Langner1,2

  • 1Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.

Network neuroscience (Cambridge, Mass.)
|June 9, 2025
PubMed
概括

任务唤起的有效连接 (EC) 比功能连接 (FC) 更好地预测反应时间 (RT). 动态因果建模 (DCM) 设计影响预测准确性,与事件相关的模型优于基于区块的模型.

关键词:
分析灵活性 分析灵活性基于大脑的预测预测动态因果建模 动态因果建模功能连接性的功能连接性.机器学习 机器学习刺激与反应的兼容性任务 fMRI 的任务

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科学领域:

  • 神经成像是一种神经成像.
  • 认知神经科学 认知神经科学
  • 计算神经科学是一种神经科学.

背景情况:

  • 任务唤起的功能连接 (FC) 在预测个体特征方面表现有前途.
  • 对个体差异的任务唤起有效连接 (EC) 的预测能力在很大程度上仍未被探索.

研究的目的:

  • 调查内在EC (I-EC) 和任务调制EC (M-EC) 对个体反应时间 (RT) 和年龄的预测能力.
  • 在预测这些特征时,比较EC与任务唤起的FC的表现.
  • 评估不同数据处理和建模选择对预测准确性的影响.

主要方法:

  • 动态因果建模 (DCM) 用于在刺激-反应兼容性任务中从fMRI数据计算I-EC和M-EC.
  • 一般线性模型 (GLM) 设计 (与事件相关的与基于区块的),贝叶斯模型减少和交叉验证方案各不相同.
  • 机器学习模型被用来预测RT和年龄.

主要成果:

  • 与I-EC和任务唤起的FC相比,M-EC证明了RT的优越预测.
  • 所有连接类型在预测年龄方面都表现类似.
  • 与事件相关的GLM和DCM设计比基于区块的设计提供了更好的预测.
  • 在I-EC和M-EC之间预测RT和年龄方面观察到显著差异.

结论:

  • 任务唤起的EC,特别是M-EC,在预测像RT这样的行为特征方面具有重大潜力.
  • 选择GLM和DCM设计极大地影响了基于EC的预测的准确性.
  • 这些发现有助于理解神经成像分析选择如何影响个体差异的预测建模.