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Optical flow approaches to the identification of brain dynamics.

机译:光流方法可识别大脑动力学。

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The superior temporal resolution of magneto- and electroencephalography (MEEG) provides unique insight into the dynamics of brain function. The analysis of the spatial dimensions of MEEG recordings can take a multiplicity of approaches: from the original scalp recordings to the identification of their generators through localizing or imaging techniques. Overall, both MEEG native or imaging data may be considered as multidimensional structures with potentially dense information contents. Quantitative analysis of the spatiotemporal flow of information conveyed by MEEG begins with a feature-extraction problem, thereby leading to improved insight in the multidimensional structure of brain dynamics. In this contribution, we approach this endeavor by suggesting that brain dynamic features from local through global spatial scales may be identified using the previously-introduced technique of surface-based optical flow. We illustrate this assertion by the quantitative analysis of time-resolved sequences of brain activity through the identification of episodes of relative topographical stability. In that respect, we revisit the concept of brain microstates with a new approach and distinct operational hypotheses. Local dynamic features from a variety of brain systems may also be explored through this methodology, as illustrated by experimental data on fast responses in the visual system as revealed by MEEG source imaging.
机译:磁和脑电图(MEEG)的卓越时间分辨率为大脑功能的动力学提供了独特的见识。对MEEG记录的空间尺寸的分析可以采取多种方法:从原始头皮记录到通过定位或成像技术识别其产生者。总体而言,MEEG原始数据或成像数据都可以被视为具有潜在密集信息内容的多维结构。对MEEG传递的时空信息流的定量分析始于特征提取问题,从而提高了对大脑动力学多维结构的了解。在这项贡献中,我们通过建议可以使用先前引入的基于表面的光流技术来识别从局部空间尺度到全局空间尺度的大脑动态特征来实现这一目标。我们通过时间分辨的大脑活动时间序列的定量分析,通过对相对地形稳定性事件的识别来说明这一主张。在这方面,我们用一种新方法和独特的操作假设重新审视了大脑微状态的概念。如MEEG源成像所揭示的有关视觉系统快速响应的实验数据所示,也可以通过这种方法探索来自各种大脑系统的局部动态特征。

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