A Novel Method for the Quantitative Analysis of Phase-locking Relationship between Neuronal Spikes and Local Field Potentials_Journal of Biomedical Engineering

Authors：

WUYunyun ,  FENGZhouyan , ZHENGXiaojing

College of Biomedical Engineering and Instrumentation Science, Key Lab of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China;

Corresponding author：

FENGZhouyan, Email: fengzhouyan@139.com

Keywords：

spike; local field potential; spike-triggered average; phase-locking; circular statistics

DOI：

10.7507/1001-5515.20140034

Video：

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Abstract Full text Figures/Tables Video References Cited by

The phase-locking relationship between the firings of neuronal action potentials (i.e., spikes) and the oscillations of local field potentials (LFP) reflects important neural coding information. However, the present analysis methods can only determine whether there has phase-locking, but not the different strengths among various types of phase-locking. In the present paper, we used spike-triggered average (STA) signals and the percentage ratio (named φ) of the STA power to the power of original LFP as an index to evaluate the strengths of phase-locking. Experimental recordings obtained from rat hippocampal CA1 region as well as simulation data were used to evaluate the method. The results showed that the index φ changed monotonically as a function of the strength of phase-locking, and it could provide an effective critical value to divide phase-locking from non-phase-locking. Because the calculation of the index does not need pre-filtering, it can avoid the unwanted influences caused by intentionally limiting the frequencies of LFP oscillations such as in the traditional bin statistical method. Therefore, the index φ provides a novel method to investigate the mechanisms underlying neuronal coding in brain.

Citation： WUYunyun, FENGZhouyan, ZHENGXiaojing. A Novel Method for the Quantitative Analysis of Phase-locking Relationship between Neuronal Spikes and Local Field Potentials. Journal of Biomedical Engineering, 2014, 31(1): 172-180. doi: 10.7507/1001-5515.20140034 Copy

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1. BUZSÁKI G, DRAGUHN A. Neuronal oscillations in cortical networks[J]. Science, 2004, 304(5679):1926-1929.
2. BUZSÁKI G. Rhythms of the brain[M]. New York:Oxford University Press, 2006:114,191.
3. PENTTONEN M, BUZSÁiKI G. Natural logarithmic relationship between brain oscillators[J]. Thalamus Relat Syst, 2003, 2(2):145-152.
4. LITAUDON P, GARCIA S, BUONVISO N. Strong coupling between pyramidal cell activity and network oscillations in the olfactory cortex[J]. Neuroscience, 2008, 156(3):781-787.
5. MASQUELIER T, HUGUES E, DECO G, et al. Oscillations, phase-of-firing coding, and spike timing-dependent plasticity:an efficient learning scheme[J]. J Neurosci, 2009, 29(43):13484-13493.
6. RAY S, MAUNSELL J H. Differences in gamma frequencies across visual cortex restrict their possible use in computation[J]. Neuron, 2010, 67(5):885-896.
7. RUTISHAUSER U, ROSS I B, AN, et al. Human memory strength is predicted by theta-frequency phase-locking of single neurons[J]. Nature, 2010, 464(7290):903-907.
8. KLAUSBERGER T, MÁRTON L F, BAUDE A, et al. Spike timing of dendrite-targeting bistratified cells during hippocampal network oscillations in vivo[J]. Nat Neurosci, 2004, 7(1):41-47.
9. BUZSÁKI G. Theta oscillations in the hippocampus[J]. Neuron, 2002, 33(3):325-340.
10. 封洲燕,光磊,郑晓静,等.应用线性硅电极阵列检测海马场电位和单细胞动作电位[J].生物化学与生物物理进展,2007,34(4):401-407.
11. 封洲燕,王静,汪洋,等.神经元锋电位信号滤波频率的选择[J].浙江大学学报(工学版),2012,46(2):351-358.
12. RASCH M J, GRETTON A, MURAYAMA Y, et al. Inferring spike trains from local field potentials[J]. J Neurophysiol, 2008, 99(3):1461-1476.
13. CSICSVARI J, JAMIESON B, WISE K D, et al. Mechanisms of gamma oscillations in the hippocampus of the behaving rat[J]. Neuron, 2003, 37(2):311-322.
14. FRIES P, REYNOLDS J H, RORIE A E, et al. Modulation of oscillatory neuronal synchronization by selective visual attention[J]. Science, 2001, 291(5508):1560-1563.
15. FRIES P, ROELFSEMA P R, ENGEL A K, et al. Synchronization of oscillatory responses in visual cortex correlates with perception in interocular rivalry[J]. Proc Natl Acad Sci U S A, 1997, 94(23):12699-12704.
16. RAY S, MAUNSELL J H. Network rhythms influence the relationship between spike-triggered local field potential and functional connectivity[J]. J Neurosci, 2011, 31(35):12674-12682.
17. SHIAVI R. Introduction to applied statistical signal analysis:guide to biomedical and electrical engineering applications[M]. 3rd ed. Burlington:Academic Press, 2007:51-82.
18. SIROTA A, MONTGOMERY S, FUJISAWA S, et al. Entrainment of neocortical neurons and gamma oscillations by the hippocampal theta rhythm[J]. Neuron, 2008, 60(4):683-697.
19. MARDIA K V, JUPP P E. Directional statistics[M]. New York:John Wiley & Sons ltd, 2000:15-17.
20. BERENS P. CircStat:A MATLAB toolbox for circular statistics[J]. J Stat Softw, 2009, 31(10):1-21, 94-99.
21. CSICSVARI J, HIRASE H, CZURKÓ A, et al. Oscillatory coupling of hippocampal pyramidal cells and interneurons in the behaving rat[J]. J Neurosci, 1999, 19(1):274-287.
22. KLAUSBERGER T, MAGILL P J, MÁRTON L F, et al. Brain-state-and cell-type-specific firing of hippocampal interneurons in vivo[J]. Nature, 2003, 421(6925):844-848.

Journal of Biomedical Engineering

A Novel Method for the Quantitative Analysis of Phase-locking Relationship between Neuronal Spikes and Local Field Potentials

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