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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...

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Autores principales: Safavi, Shervin, Panagiotaropoulos, Theofanis I., Kapoor, Vishal, Ramirez-Villegas, Juan F., Logothetis, Nikos K., Besserve, Michel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
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.
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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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