Uncovering the organization of neural circuits with Generalized Phase Locking Analysis

Safavi S, Panagiotaropoulos TI, Kapoor V, Ramirez Villegas JF, Logothetis NK, Besserve M. 2023. Uncovering the organization of neural circuits with Generalized Phase Locking Analysis. PLoS Computational Biology. 19(4), e1010983.

Download
OA 2023_PLoSCompBio_Safavi.pdf 4.74 MB [Published Version]

Journal Article | Published | English

Scopus indexed
Author
Safavi, Shervin; Panagiotaropoulos, Theofanis I.; Kapoor, Vishal; Ramirez Villegas, Juan FISTA; Logothetis, Nikos K.; Besserve, Michel
Department
Abstract
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.
Publishing Year
Date Published
2023-04-01
Journal Title
PLoS Computational Biology
Publisher
Public Library of Science
Acknowledgement
We thank Britni Crocker for help with preprocessing of the data and spike sorting; Joachim Werner and Michael Schnabel for their excellent IT support; Andreas Tolias for help with the initial implantation’s of the Utah arrays. All authors were supported by the Max Planck Society. M.B. was supported by the German Federal Ministry of Education and Research (BMBF) through the funding scheme received by the Tübingen AI Center, FKZ: 01IS18039B. N.K.L. and V.K. acknowledge the support from the Shanghai Municipal Science and Technology Major Project (Grant No. 2019SHZDZX02). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Volume
19
Issue
4
Article Number
e1010983
eISSN
IST-REx-ID

Cite this

Safavi S, Panagiotaropoulos TI, Kapoor V, Ramirez Villegas JF, Logothetis NK, Besserve M. Uncovering the organization of neural circuits with Generalized Phase Locking Analysis. PLoS Computational Biology. 2023;19(4). doi:10.1371/journal.pcbi.1010983
Safavi, S., Panagiotaropoulos, T. I., Kapoor, V., Ramirez Villegas, J. F., Logothetis, N. K., & Besserve, M. (2023). Uncovering the organization of neural circuits with Generalized Phase Locking Analysis. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1010983
Safavi, Shervin, Theofanis I. Panagiotaropoulos, Vishal Kapoor, Juan F Ramirez Villegas, Nikos K. Logothetis, and Michel Besserve. “Uncovering the Organization of Neural Circuits with Generalized Phase Locking Analysis.” PLoS Computational Biology. Public Library of Science, 2023. https://doi.org/10.1371/journal.pcbi.1010983.
S. Safavi, T. I. Panagiotaropoulos, V. Kapoor, J. F. Ramirez Villegas, N. K. Logothetis, and M. Besserve, “Uncovering the organization of neural circuits with Generalized Phase Locking Analysis,” PLoS Computational Biology, vol. 19, no. 4. Public Library of Science, 2023.
Safavi S, Panagiotaropoulos TI, Kapoor V, Ramirez Villegas JF, Logothetis NK, Besserve M. 2023. Uncovering the organization of neural circuits with Generalized Phase Locking Analysis. PLoS Computational Biology. 19(4), e1010983.
Safavi, Shervin, et al. “Uncovering the Organization of Neural Circuits with Generalized Phase Locking Analysis.” PLoS Computational Biology, vol. 19, no. 4, e1010983, Public Library of Science, 2023, doi:10.1371/journal.pcbi.1010983.
All files available under the following license(s):
Creative Commons Attribution 4.0 International Public License (CC-BY 4.0):
Main File(s)
Access Level
OA Open Access
Date Uploaded
2023-04-25
MD5 Checksum
edeb9d09f3e41ba7c0251308b9e372e7


Export

Marked Publications

Open Data ISTA Research Explorer

Web of Science

View record in Web of Science®

Search this title in

Google Scholar