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Uncovering the organization of neural circuits with Generalized Phase Locking Analysis
Despite the considerable progress of in vivo neural recording techniques, inferring the biophysical mechanisms underlying large scale coordination of brain activity from neural data remains challenging. One obstacle is the difficulty to link high dimensional functional connectivity measures to mecha...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10109521/ https://www.ncbi.nlm.nih.gov/pubmed/37011110 http://dx.doi.org/10.1371/journal.pcbi.1010983 |
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author | Safavi, Shervin Panagiotaropoulos, Theofanis I. Kapoor, Vishal Ramirez-Villegas, Juan F. Logothetis, Nikos K. Besserve, Michel |
author_facet | Safavi, Shervin Panagiotaropoulos, Theofanis I. Kapoor, Vishal Ramirez-Villegas, Juan F. Logothetis, Nikos K. Besserve, Michel |
author_sort | Safavi, Shervin |
collection | PubMed |
description | Despite the considerable progress of in vivo neural recording techniques, inferring the biophysical mechanisms underlying large scale coordination of brain activity from neural data remains challenging. One obstacle is the difficulty to link high dimensional functional connectivity measures to mechanistic models of network activity. We address this issue by investigating spike-field coupling (SFC) measurements, which quantify the synchronization between, on the one hand, the action potentials produced by neurons, and on the other hand mesoscopic “field” signals, reflecting subthreshold activities at possibly multiple recording sites. As the number of recording sites gets large, the amount of pairwise SFC measurements becomes overwhelmingly challenging to interpret. We develop Generalized Phase Locking Analysis (GPLA) as an interpretable dimensionality reduction of this multivariate SFC. GPLA describes the dominant coupling between field activity and neural ensembles across space and frequencies. We show that GPLA features are biophysically interpretable when used in conjunction with appropriate network models, such that we can identify the influence of underlying circuit properties on these features. We demonstrate the statistical benefits and interpretability of this approach in various computational models and Utah array recordings. The results suggest that GPLA, used jointly with biophysical modeling, can help uncover the contribution of recurrent microcircuits to the spatio-temporal dynamics observed in multi-channel experimental recordings. |
format | Online Article Text |
id | pubmed-10109521 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-101095212023-04-18 Uncovering the organization of neural circuits with Generalized Phase Locking Analysis Safavi, Shervin Panagiotaropoulos, Theofanis I. Kapoor, Vishal Ramirez-Villegas, Juan F. Logothetis, Nikos K. Besserve, Michel PLoS Comput Biol Research Article Despite the considerable progress of in vivo neural recording techniques, inferring the biophysical mechanisms underlying large scale coordination of brain activity from neural data remains challenging. One obstacle is the difficulty to link high dimensional functional connectivity measures to mechanistic models of network activity. We address this issue by investigating spike-field coupling (SFC) measurements, which quantify the synchronization between, on the one hand, the action potentials produced by neurons, and on the other hand mesoscopic “field” signals, reflecting subthreshold activities at possibly multiple recording sites. As the number of recording sites gets large, the amount of pairwise SFC measurements becomes overwhelmingly challenging to interpret. We develop Generalized Phase Locking Analysis (GPLA) as an interpretable dimensionality reduction of this multivariate SFC. GPLA describes the dominant coupling between field activity and neural ensembles across space and frequencies. We show that GPLA features are biophysically interpretable when used in conjunction with appropriate network models, such that we can identify the influence of underlying circuit properties on these features. We demonstrate the statistical benefits and interpretability of this approach in various computational models and Utah array recordings. The results suggest that GPLA, used jointly with biophysical modeling, can help uncover the contribution of recurrent microcircuits to the spatio-temporal dynamics observed in multi-channel experimental recordings. Public Library of Science 2023-04-03 /pmc/articles/PMC10109521/ /pubmed/37011110 http://dx.doi.org/10.1371/journal.pcbi.1010983 Text en © 2023 Safavi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Safavi, Shervin Panagiotaropoulos, Theofanis I. Kapoor, Vishal Ramirez-Villegas, Juan F. Logothetis, Nikos K. Besserve, Michel Uncovering the organization of neural circuits with Generalized Phase Locking Analysis |
title | Uncovering the organization of neural circuits with Generalized Phase Locking Analysis |
title_full | Uncovering the organization of neural circuits with Generalized Phase Locking Analysis |
title_fullStr | Uncovering the organization of neural circuits with Generalized Phase Locking Analysis |
title_full_unstemmed | Uncovering the organization of neural circuits with Generalized Phase Locking Analysis |
title_short | Uncovering the organization of neural circuits with Generalized Phase Locking Analysis |
title_sort | uncovering the organization of neural circuits with generalized phase locking analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10109521/ https://www.ncbi.nlm.nih.gov/pubmed/37011110 http://dx.doi.org/10.1371/journal.pcbi.1010983 |
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