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Dynamical differential covariance recovers directional network structure in multiscale neural systems

Investigating neural interactions is essential to understanding the neural basis of behavior. Many statistical methods have been used for analyzing neural activity, but estimating the direction of network interactions correctly and efficiently remains a difficult problem. Here, we derive dynamical d...

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Autores principales: Chen, Yusi, Rosen, Burke Q., Sejnowski, Terrence J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9214501/
https://www.ncbi.nlm.nih.gov/pubmed/35679342
http://dx.doi.org/10.1073/pnas.2117234119
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author Chen, Yusi
Rosen, Burke Q.
Sejnowski, Terrence J.
author_facet Chen, Yusi
Rosen, Burke Q.
Sejnowski, Terrence J.
author_sort Chen, Yusi
collection PubMed
description Investigating neural interactions is essential to understanding the neural basis of behavior. Many statistical methods have been used for analyzing neural activity, but estimating the direction of network interactions correctly and efficiently remains a difficult problem. Here, we derive dynamical differential covariance (DDC), a method based on dynamical network models that detects directional interactions with low bias and high noise tolerance under nonstationarity conditions. Moreover, DDC scales well with the number of recording sites and the computation required is comparable to that needed for covariance. DDC was validated and compared favorably with other methods on networks with false positive motifs and multiscale neural simulations where the ground-truth connectivity was known. When applied to recordings of resting-state functional magnetic resonance imaging (rs-fMRI), DDC consistently detected regional interactions with strong structural connectivity in over 1,000 individual subjects obtained by diffusion MRI (dMRI). DDC is a promising family of methods for estimating connectivity that can be generalized to a wide range of dynamical models and recording techniques and to other applications where system identification is needed.
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spelling pubmed-92145012022-06-23 Dynamical differential covariance recovers directional network structure in multiscale neural systems Chen, Yusi Rosen, Burke Q. Sejnowski, Terrence J. Proc Natl Acad Sci U S A Biological Sciences Investigating neural interactions is essential to understanding the neural basis of behavior. Many statistical methods have been used for analyzing neural activity, but estimating the direction of network interactions correctly and efficiently remains a difficult problem. Here, we derive dynamical differential covariance (DDC), a method based on dynamical network models that detects directional interactions with low bias and high noise tolerance under nonstationarity conditions. Moreover, DDC scales well with the number of recording sites and the computation required is comparable to that needed for covariance. DDC was validated and compared favorably with other methods on networks with false positive motifs and multiscale neural simulations where the ground-truth connectivity was known. When applied to recordings of resting-state functional magnetic resonance imaging (rs-fMRI), DDC consistently detected regional interactions with strong structural connectivity in over 1,000 individual subjects obtained by diffusion MRI (dMRI). DDC is a promising family of methods for estimating connectivity that can be generalized to a wide range of dynamical models and recording techniques and to other applications where system identification is needed. National Academy of Sciences 2022-06-09 2022-06-14 /pmc/articles/PMC9214501/ /pubmed/35679342 http://dx.doi.org/10.1073/pnas.2117234119 Text en Copyright © 2022 the Author(s). Published by PNAS https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Biological Sciences
Chen, Yusi
Rosen, Burke Q.
Sejnowski, Terrence J.
Dynamical differential covariance recovers directional network structure in multiscale neural systems
title Dynamical differential covariance recovers directional network structure in multiscale neural systems
title_full Dynamical differential covariance recovers directional network structure in multiscale neural systems
title_fullStr Dynamical differential covariance recovers directional network structure in multiscale neural systems
title_full_unstemmed Dynamical differential covariance recovers directional network structure in multiscale neural systems
title_short Dynamical differential covariance recovers directional network structure in multiscale neural systems
title_sort dynamical differential covariance recovers directional network structure in multiscale neural systems
topic Biological Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9214501/
https://www.ncbi.nlm.nih.gov/pubmed/35679342
http://dx.doi.org/10.1073/pnas.2117234119
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