由频率转移解释的区域间连贯性的注意调制
Jarrod Robert Dowdall1, Marius Schneider2, Martin Vinck2
1Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, Germany; Robarts Research Institute, Western University, London, Ontario, Canada; Donders Centre for Neuroscience, Department of Neurophysics, Radboud University, Nijmegen, the Netherlands.
NeuroImage
|July 1, 2023
概括
区域间连贯性是大脑通信的衡量标准,受振荡频率的影响. 计算模型显示,接收器属性决定了连贯性模式,这表明连贯性可能是一个误导性的相互作用度量.
科学领域:
- 计算神经科学是一种计算神经科学.
- 系统神经科学 系统神经科学
- 神经振荡是一种神经振荡.
背景情况:
- 假设区域间的连贯性可以调解大脑的通信.
- 经验研究将增加的连贯性与注意力和刺激突出性联系起来.
- 在V1中马振荡峰值频率的变化表明频率在通信中起作用.
研究的目的:
- 通过使用计算建模,研究发射器的振荡峰值频率如何影响区域间的连贯性.
- 确定接收器特性 (集成与共振) 在塑造连贯性模式中的作用.
- 开发一个更准确的衡量区域间相互作用的方法.
主要方法:
- 神经网络的计算建模.
- 模拟不同振荡频率的发送机接收器相互作用.
- 分析基于接收器集成或共振特性的一致性模式.
- 开发和验证"解释功率"指标.
主要成果:
- 发射器的峰值频率显著影响连贯性大小.
- 接收器属性 (集成器与共振器) 决定了连贯性变化的模式.
- 响应接收器产生了与经验数据不一致的连贯性模式.
- 集成器接收器产生了与频率转移的经验观测一致的连贯性模式.
- 一致性可能是区域间相互作用的误导性指标.
结论:
- 区域间的连贯性模式严重依赖于接收器内在的特性.
- 单靠一致性可能无法准确地反映神经通信.
- "解释功率"指标可以更直接地量化大脑区域之间传输的信号.
- 这些发现为理解由于频率转移而导致的连贯性和格兰杰因果关系的变化提供了一个模型.
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