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Erschienen in: Brain Topography 2/2019

25.01.2019 | Review

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

verfasst von: Paul L. Nunez, Michael D. Nunez, Ramesh Srinivasan

Erschienen in: Brain Topography | Ausgabe 2/2019

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Abstract

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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Metadaten
Titel
Multi-Scale Neural Sources of EEG: Genuine, Equivalent, and Representative. A Tutorial Review
verfasst von
Paul L. Nunez
Michael D. Nunez
Ramesh Srinivasan
Publikationsdatum
25.01.2019
Verlag
Springer US
Erschienen in
Brain Topography / Ausgabe 2/2019
Print ISSN: 0896-0267
Elektronische ISSN: 1573-6792
DOI
https://doi.org/10.1007/s10548-019-00701-3

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