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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-10-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fncom.2018.00078/full |
| _version_ | 1811214917769887744 |
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| author | Mohammad Zarei Mehran Jahed Mohammad Reza Daliri Mohammad Reza Daliri |
| author_facet | Mohammad Zarei Mehran Jahed Mohammad Reza Daliri Mohammad Reza Daliri |
| author_sort | Mohammad Zarei |
| collection | DOAJ |
| 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 R2 of 0.9563 in the training phase, and correlation of 0.95969 and R2 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. |
| first_indexed | 2024-04-12T06:13:30Z |
| format | Article |
| id | doaj.art-22a515875ca94074acf579885cf26f6a |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-12T06:13:30Z |
| publishDate | 2018-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-22a515875ca94074acf579885cf26f6a2022-12-22T03:44:36ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882018-10-011210.3389/fncom.2018.00078381280Introducing a Comprehensive Framework to Measure Spike-LFP CouplingMohammad Zarei0Mehran Jahed1Mohammad Reza Daliri2Mohammad Reza Daliri3Department of Electrical Engineering, Sharif University of Technology, Tehran, IranDepartment of Electrical Engineering, Sharif University of Technology, Tehran, IranSchool of Electrical Engineering, Iran University of Science and Technology, Tehran, IranSchool of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, IranMeasuring 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 R2 of 0.9563 in the training phase, and correlation of 0.95969 and R2 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.https://www.frontiersin.org/article/10.3389/fncom.2018.00078/fulllocal field potentialsphase locking valuespike field coherencepairwise phase consistencyspike-LFP phase coupling |
| spellingShingle | Mohammad Zarei Mehran Jahed Mohammad Reza Daliri Mohammad Reza Daliri Introducing a Comprehensive Framework to Measure Spike-LFP Coupling Frontiers in Computational Neuroscience local field potentials phase locking value spike field coherence pairwise phase consistency spike-LFP phase 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 | local field potentials phase locking value spike field coherence pairwise phase consistency spike-LFP phase coupling |
| url | https://www.frontiersin.org/article/10.3389/fncom.2018.00078/full |
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