Multi-Scale Neural Sources of EEG: Genuine, Equivalent, and Representative. A Tutorial Review

被引:0
作者
Paul L. Nunez
Michael D. Nunez
Ramesh Srinivasan
机构
[1] Cognitive Dissonance LLC,Department of Cognitive Science
[2] University of California at Irvine,Department of Biomedical Engineering
[3] University of California at Irvine,undefined
来源
Brain Topography | 2019年 / 32卷
关键词
EEG; Sources; Synchronization; Dipole localization; Laplacian; Spatial scale;
D O I
暂无
中图分类号
学科分类号
摘要
A biophysical framework needed to interpret electrophysiological data recorded at multiple spatial scales of brain tissue is developed. Micro current sources at membrane surfaces produce local field potentials, electrocorticography, and electroencephalography (EEG). We categorize multi-scale sources as genuine, equivalent, or representative. Genuine sources occur at the micro scale of cell surfaces. Equivalent sources provide identical experimental outcomes over a range of scales and applications. In contrast, each representative source distribution is just one of many possible source distributions that yield similar experimental outcomes. Macro sources (“dipoles”) may be defined at the macrocolumn (mm) scale and depend on several features of the micro sources—magnitudes, micro synchrony within columns, and distribution through the cortical depths. These micro source properties are determined by brain dynamics and the columnar structure of cortical tissue. The number of representative sources underlying EEG data depends on the spatial scale of neural tissue under study. EEG inverse solutions (e.g. dipole localization) and high resolution estimates (e.g. Laplacian, dura imaging) have both strengths and limitations that depend on experimental conditions. The proposed theoretical framework informs studies of EEG source localization, source characterization, and low pass filtering. It also facilitates interpretations of brain dynamics and cognition, including measures of synchrony, functional connections between cortical locations, and other aspects of brain complexity.
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页码:193 / 214
页数:21
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