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Updated: Jun 8, 2025

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

多区域马科维-高斯过程:一种有效的方法来发现跨多个大脑区域的方向通信.

Weihan Li¹, Chengrui Li¹, Yule Wang¹

¹School of Computational Science & Engineering, Georgia Institute of Technology, Atlanta, USA.

Proceedings of machine learning research

|November 1, 2024

概括

我们介绍了一种新模型,即多区域马科维安高斯过程 (MRM-GP),它结合了高斯过程和线性动态系统. 这种方法通过建模频率和相位延迟来增强对大脑区域通信的理解.

科学领域:

神经科学是一个神经科学.
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:

在神经科学中,了解大脑区域间的神经通信至关重要.
像高斯过程 (GP) 和线性动态系统 (LDS) 这样的现有方法在捕捉复杂的大脑相互作用方面存在局限性.
医生擅长识别潜在变量和频段,而LDS提供计算效率但表达能力有限.

研究的目的:

开发一个新的统计框架,整合GP和LDS模型的优势.
创建一种能够明确表示神经数据中的频率和相位延迟的模型.
为了实现多区域大脑通信的高效和可解释的分析.

主要方法:

我们提出了多区域马科维安高斯过程 (MRM-GP),这是一个新的模型,结构为线性动态系统,反映了多输出高斯过程.
这种方法在LDS和多输出GP形式主义之间建立了直接联系.
该MRM-GP的设计是为了在时间点上的线性推理成本运行.

主要成果:

MRM-GP成功地模拟了神经记录的潜在空间内的频率和相位延迟.
该模型提供了一个可解释的,低维的神经活动表示.
我们展示了该模型能够揭示大脑区域之间的通信方向,并将振荡通信分成不同的频段.

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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

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Published on: October 24, 2012

结论:

在MRM-GP提供了一个强大的和计算效率高的方法来分析复杂的神经通信模式.
这种综合方法提高了多区域大脑记录中的潜在表示的可解释性.
我们的发现推动了研究不同频谱的大脑连接和动态的统计工具包.