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Inferring entire spiking activity from local field potentials
Extracellular recordings are typically analysed by separating them into two distinct signals: local field potentials (LFPs) and spikes. Previous studies have shown that spikes, in the form of single-unit activity (SUA) or multiunit activity (MUA), can be inferred solely from LFPs with moderately goo...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463692/ https://www.ncbi.nlm.nih.gov/pubmed/34561480 http://dx.doi.org/10.1038/s41598-021-98021-9 |
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author | Ahmadi, Nur Constandinou, Timothy G. Bouganis, Christos-Savvas |
author_facet | Ahmadi, Nur Constandinou, Timothy G. Bouganis, Christos-Savvas |
author_sort | Ahmadi, Nur |
collection | PubMed |
description | Extracellular recordings are typically analysed by separating them into two distinct signals: local field potentials (LFPs) and spikes. Previous studies have shown that spikes, in the form of single-unit activity (SUA) or multiunit activity (MUA), can be inferred solely from LFPs with moderately good accuracy. SUA and MUA are typically extracted via threshold-based technique which may not be reliable when the recordings exhibit a low signal-to-noise ratio (SNR). Another type of spiking activity, referred to as entire spiking activity (ESA), can be extracted by a threshold-less, fast, and automated technique and has led to better performance in several tasks. However, its relationship with the LFPs has not been investigated. In this study, we aim to address this issue by inferring ESA from LFPs intracortically recorded from the motor cortex area of three monkeys performing different tasks. Results from long-term recording sessions and across subjects revealed that ESA can be inferred from LFPs with good accuracy. On average, the inference performance of ESA was consistently and significantly higher than those of SUA and MUA. In addition, local motor potential (LMP) was found to be the most predictive feature. The overall results indicate that LFPs contain substantial information about spiking activity, particularly ESA. This could be useful for understanding LFP-spike relationship and for the development of LFP-based BMIs. |
format | Online Article Text |
id | pubmed-8463692 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-84636922021-09-29 Inferring entire spiking activity from local field potentials Ahmadi, Nur Constandinou, Timothy G. Bouganis, Christos-Savvas Sci Rep Article Extracellular recordings are typically analysed by separating them into two distinct signals: local field potentials (LFPs) and spikes. Previous studies have shown that spikes, in the form of single-unit activity (SUA) or multiunit activity (MUA), can be inferred solely from LFPs with moderately good accuracy. SUA and MUA are typically extracted via threshold-based technique which may not be reliable when the recordings exhibit a low signal-to-noise ratio (SNR). Another type of spiking activity, referred to as entire spiking activity (ESA), can be extracted by a threshold-less, fast, and automated technique and has led to better performance in several tasks. However, its relationship with the LFPs has not been investigated. In this study, we aim to address this issue by inferring ESA from LFPs intracortically recorded from the motor cortex area of three monkeys performing different tasks. Results from long-term recording sessions and across subjects revealed that ESA can be inferred from LFPs with good accuracy. On average, the inference performance of ESA was consistently and significantly higher than those of SUA and MUA. In addition, local motor potential (LMP) was found to be the most predictive feature. The overall results indicate that LFPs contain substantial information about spiking activity, particularly ESA. This could be useful for understanding LFP-spike relationship and for the development of LFP-based BMIs. Nature Publishing Group UK 2021-09-24 /pmc/articles/PMC8463692/ /pubmed/34561480 http://dx.doi.org/10.1038/s41598-021-98021-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Ahmadi, Nur Constandinou, Timothy G. Bouganis, Christos-Savvas Inferring entire spiking activity from local field potentials |
title | Inferring entire spiking activity from local field potentials |
title_full | Inferring entire spiking activity from local field potentials |
title_fullStr | Inferring entire spiking activity from local field potentials |
title_full_unstemmed | Inferring entire spiking activity from local field potentials |
title_short | Inferring entire spiking activity from local field potentials |
title_sort | inferring entire spiking activity from local field potentials |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463692/ https://www.ncbi.nlm.nih.gov/pubmed/34561480 http://dx.doi.org/10.1038/s41598-021-98021-9 |
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