在网络中介尺度的见解:一个多模式的内数据集
Vasiliki Bougou1,2, Michaël Vanhoyland3,4,5, Evy Cleeren6,7
1Research Group of Experimental Neurosurgery and Neuroanatomy, Department of Neurosciences, KU Leuven and the Leuven Brain Institute, Leuven, Belgium. vasiliki.bougou@kuleuven.be.
Scientific data
|May 10, 2025
概括
这个数据集捕捉了患者的内脑电图 (iEEG),局部场势 (LFP) 和多单元活动 (MUA). 它可以在焦点中进行大尺度网络分析,并探索高频振荡 (HFO).
科学领域:
- 神经科学是一个神经科学.
- 的研究研究.
- 数据科学数据科学数据科学
背景情况:
- 了解中网络动态是病理生理学和治疗的关键.
- 现有的研究往往缺乏在中等层次的多模态神经记录.
- 焦点呈现出复杂的时空动态,需要详细的神经信号分析.
研究的目的:
- 呈现一个综合数据集的多模态神经记录从患者.
- 通过使用内脑电图 (iEEG),局部场势 (LFP) 和多单元活动 (MUA) 来研究中网络.
- 促进研究高频振荡 (HFO) 与焦点中神经活动之间的关系.
主要方法:
- 获取内脑电图 (iEEG),局部场势 (LFP) 和多单元活动 (MUA) 数据的数据.
- 使用微电极阵列 (MEAs;犹他阵列;黑岩) 进行神经记录.
- 在5名患者中记录了12次发作.
主要成果:
- 现在可以获得一个包含同步iEEG,LFP和MUA记录的全面数据集.
- 该数据集允许研究各种神经信号之间的复杂相互作用.
- 高时间分辨率有助于计算iEEG和LFP信号中的高频振荡 (HFOs).
结论:
- 这一数据集为研究焦点中介尺度网络提供了宝贵的资源.
- 它可以通过整合iEEG,LFP和MUA来探索网络的时空动态.
- 这些数据支持研究高频振荡 (HFOs) 和多单元活动 (MUA) 之间的关系.
相关概念视频
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