一种基于预测器的多主体贝叶斯式方法,用于动态功能连接
Jaylen Lee1, Sana Hussain2, Ryan Warnick3
1Department of Statistics, University of California, Irvine, Irvine, California, United States of America.
这项研究引入了贝叶斯框架,通过结合生理数据来分析大脑成像 (fMRI) 中的动态功能连接. 该方法揭示了像瞳孔膨胀这样的因素如何影响大脑状态的过渡.
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:
- 动态功能连接 (dFC) 在fMRI期间跟踪改变大脑区域相互作用.
- 像注意力和认知努力这样的生理因素可以调节这些连接状态.
- 现有的方法可能无法完全捕捉时间变化的生理信号对大脑网络动态的影响.
研究的目的:
- 开发一个新的多主题贝叶斯框架来估计动态功能网络.
- 将时间变化的生理共变体纳入功能磁共振成像 (fMRI) 数据的分析中.
- 研究生理变化 (如瞳孔膨胀) 和大脑连接状态转换之间的关系.
主要方法:
- 利用动态高斯图形模型与非均隐藏的马尔科夫模型来识别潜在的神经状态.
- 整合了时间变化的外源生理共变量,以模拟各个受试者的状态过渡概率.
- 采用了用于网络稀疏性的收缩先验和用于边缘选择的贝叶斯虚假发现率控制.
主要成果:
- 开发的贝叶斯框架成功估计了由生理共变量告知的动态功能网络.
- 该模型确定了反复出现的连接模式,并允许受试者之间共享网络.
- 对静止状态fMRI和瞳孔测量数据的分析揭示了瞳孔扩张对大脑状态转换的异质影响.
结论:
- 提出的贝叶斯框架为分析受生理因素影响的动态功能连接提供了一个强大的方法.
- 了解各个学科的状态占用异质性,可以了解认知处理.
- 这种方法通过将神经动态与并发的生理测量联系起来,提高了fMRI数据的解释.
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