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Introducing a Comprehensive Framework to Measure Spike-LFP Coupling

Measuring the coupling of single neuron's spiking activities to the local field potentials (LFPs) is a method to investigate neuronal synchronization. The most important synchronization measures are phase locking value (PLV), spike field coherence (SFC), pairwise phase consistency (PPC), and sp...

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Autores principales: Zarei, Mohammad, Jahed, Mehran, Daliri, Mohammad Reza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6196284/
https://www.ncbi.nlm.nih.gov/pubmed/30374297
http://dx.doi.org/10.3389/fncom.2018.00078
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author Zarei, Mohammad
Jahed, Mehran
Daliri, Mohammad Reza
author_facet Zarei, Mohammad
Jahed, Mehran
Daliri, Mohammad Reza
author_sort Zarei, Mohammad
collection PubMed
description Measuring the coupling of single neuron's spiking activities to the local field potentials (LFPs) is a method to investigate neuronal synchronization. The most important synchronization measures are phase locking value (PLV), spike field coherence (SFC), pairwise phase consistency (PPC), and spike-triggered correlation matrix synchronization (SCMS). Synchronization is generally quantified using the PLV and SFC. PLV and SFC methods are either biased on the spike rates or the number of trials. To resolve these problems the PPC measure has been introduced. However, there are some shortcomings associated with the PPC measure which is unbiased only for very high spike rates. However evaluating spike-LFP phase coupling (SPC) for short trials or low number of spikes is a challenge in many studies. Lastly, SCMS measures the correlation in terms of phase in regions around the spikes inclusive of the non-spiking events which is the major difference between SCMS and SPC. This study proposes a new framework for predicting a more reliable SPC by modeling and introducing appropriate machine learning algorithms namely least squares, Lasso, and neural networks algorithms where through an initial trend of the spike rates, the ideal SPC is predicted for neurons with low spike rates. Furthermore, comparing the performance of these three algorithms shows that the least squares approach provided the best performance with a correlation of 0.99214 and R(2) of 0.9563 in the training phase, and correlation of 0.95969 and R(2) of 0.8842 in the test phase. Hence, the results show that the proposed framework significantly enhances the accuracy and provides a bias-free basis for small number of spikes for SPC as compared to the conventional methods such as PLV method. As such, it has the general ability to correct for the bias on the number of spike rates.
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spelling pubmed-61962842018-10-29 Introducing a Comprehensive Framework to Measure Spike-LFP Coupling Zarei, Mohammad Jahed, Mehran Daliri, Mohammad Reza Front Comput Neurosci Neuroscience Measuring the coupling of single neuron's spiking activities to the local field potentials (LFPs) is a method to investigate neuronal synchronization. The most important synchronization measures are phase locking value (PLV), spike field coherence (SFC), pairwise phase consistency (PPC), and spike-triggered correlation matrix synchronization (SCMS). Synchronization is generally quantified using the PLV and SFC. PLV and SFC methods are either biased on the spike rates or the number of trials. To resolve these problems the PPC measure has been introduced. However, there are some shortcomings associated with the PPC measure which is unbiased only for very high spike rates. However evaluating spike-LFP phase coupling (SPC) for short trials or low number of spikes is a challenge in many studies. Lastly, SCMS measures the correlation in terms of phase in regions around the spikes inclusive of the non-spiking events which is the major difference between SCMS and SPC. This study proposes a new framework for predicting a more reliable SPC by modeling and introducing appropriate machine learning algorithms namely least squares, Lasso, and neural networks algorithms where through an initial trend of the spike rates, the ideal SPC is predicted for neurons with low spike rates. Furthermore, comparing the performance of these three algorithms shows that the least squares approach provided the best performance with a correlation of 0.99214 and R(2) of 0.9563 in the training phase, and correlation of 0.95969 and R(2) of 0.8842 in the test phase. Hence, the results show that the proposed framework significantly enhances the accuracy and provides a bias-free basis for small number of spikes for SPC as compared to the conventional methods such as PLV method. As such, it has the general ability to correct for the bias on the number of spike rates. Frontiers Media S.A. 2018-10-15 /pmc/articles/PMC6196284/ /pubmed/30374297 http://dx.doi.org/10.3389/fncom.2018.00078 Text en Copyright © 2018 Zarei, Jahed and Daliri. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Zarei, Mohammad
Jahed, Mehran
Daliri, Mohammad Reza
Introducing a Comprehensive Framework to Measure Spike-LFP Coupling
title Introducing a Comprehensive Framework to Measure Spike-LFP Coupling
title_full Introducing a Comprehensive Framework to Measure Spike-LFP Coupling
title_fullStr Introducing a Comprehensive Framework to Measure Spike-LFP Coupling
title_full_unstemmed Introducing a Comprehensive Framework to Measure Spike-LFP Coupling
title_short Introducing a Comprehensive Framework to Measure Spike-LFP Coupling
title_sort introducing a comprehensive framework to measure spike-lfp coupling
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6196284/
https://www.ncbi.nlm.nih.gov/pubmed/30374297
http://dx.doi.org/10.3389/fncom.2018.00078
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