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The Scalp Time-Varying Networks of N170: Reference, Latency, and Information Flow

Using the scalp time-varying network method, the present study is the first to investigate the temporal influence of the reference on N170, a negative event-related potential component (ERP) appeared about 170 ms that is elicited by facial recognition, in the network levels. Two kinds of scalp elect...

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Autores principales: Tian, Yin, Xu, Wei, Zhang, Huiling, Tam, Kin Y., Zhang, Haiyong, Yang, Li, Li, Zhangyong, Pang, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5915542/
https://www.ncbi.nlm.nih.gov/pubmed/29720933
http://dx.doi.org/10.3389/fnins.2018.00250
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author Tian, Yin
Xu, Wei
Zhang, Huiling
Tam, Kin Y.
Zhang, Haiyong
Yang, Li
Li, Zhangyong
Pang, Yu
author_facet Tian, Yin
Xu, Wei
Zhang, Huiling
Tam, Kin Y.
Zhang, Haiyong
Yang, Li
Li, Zhangyong
Pang, Yu
author_sort Tian, Yin
collection PubMed
description Using the scalp time-varying network method, the present study is the first to investigate the temporal influence of the reference on N170, a negative event-related potential component (ERP) appeared about 170 ms that is elicited by facial recognition, in the network levels. Two kinds of scalp electroencephalogram (EEG) references, namely, AR (average of all recording channels) and reference electrode standardization technique (REST), were comparatively investigated via the time-varying processing of N170. Results showed that the latency and amplitude of N170 were significantly different between REST and AR, with the former being earlier and smaller. In particular, the information flow from right temporal-parietal P8 to left P7 in the time-varying network was earlier in REST than that in AR, and this phenomenon was reproduced by simulation, in which the performance of REST was closer to the true case at source level. These findings indicate that reference plays a crucial role in ERP data interpretation, and importantly, the newly developed approximate zero-reference REST would be a superior choice for precise evaluation of the scalp spatio-temporal changes relating to various cognitive events.
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spelling pubmed-59155422018-05-02 The Scalp Time-Varying Networks of N170: Reference, Latency, and Information Flow Tian, Yin Xu, Wei Zhang, Huiling Tam, Kin Y. Zhang, Haiyong Yang, Li Li, Zhangyong Pang, Yu Front Neurosci Neuroscience Using the scalp time-varying network method, the present study is the first to investigate the temporal influence of the reference on N170, a negative event-related potential component (ERP) appeared about 170 ms that is elicited by facial recognition, in the network levels. Two kinds of scalp electroencephalogram (EEG) references, namely, AR (average of all recording channels) and reference electrode standardization technique (REST), were comparatively investigated via the time-varying processing of N170. Results showed that the latency and amplitude of N170 were significantly different between REST and AR, with the former being earlier and smaller. In particular, the information flow from right temporal-parietal P8 to left P7 in the time-varying network was earlier in REST than that in AR, and this phenomenon was reproduced by simulation, in which the performance of REST was closer to the true case at source level. These findings indicate that reference plays a crucial role in ERP data interpretation, and importantly, the newly developed approximate zero-reference REST would be a superior choice for precise evaluation of the scalp spatio-temporal changes relating to various cognitive events. Frontiers Media S.A. 2018-04-18 /pmc/articles/PMC5915542/ /pubmed/29720933 http://dx.doi.org/10.3389/fnins.2018.00250 Text en Copyright © 2018 Tian, Xu, Zhang, Tam, Zhang, Yang, Li and Pang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Tian, Yin
Xu, Wei
Zhang, Huiling
Tam, Kin Y.
Zhang, Haiyong
Yang, Li
Li, Zhangyong
Pang, Yu
The Scalp Time-Varying Networks of N170: Reference, Latency, and Information Flow
title The Scalp Time-Varying Networks of N170: Reference, Latency, and Information Flow
title_full The Scalp Time-Varying Networks of N170: Reference, Latency, and Information Flow
title_fullStr The Scalp Time-Varying Networks of N170: Reference, Latency, and Information Flow
title_full_unstemmed The Scalp Time-Varying Networks of N170: Reference, Latency, and Information Flow
title_short The Scalp Time-Varying Networks of N170: Reference, Latency, and Information Flow
title_sort scalp time-varying networks of n170: reference, latency, and information flow
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5915542/
https://www.ncbi.nlm.nih.gov/pubmed/29720933
http://dx.doi.org/10.3389/fnins.2018.00250
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