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Decoupling action potential bias from cortical local field potentials.

David SV, Malaval N, Shamma SA - Comput Intell Neurosci (2010)

Bottom Line: This filtering procedure can be applied for well-isolated single units or multiunit activity.We illustrate the effects of this correction in simulation and on spike data recorded from primary auditory cortex.We find that local spiking activity can explain a significant portion of LFP power at most recording sites and demonstrate that removing the spike-correlated component can affect measurements of auditory tuning of the LFP.

View Article: PubMed Central - PubMed

Affiliation: Institute for Systems Research, University of Maryland, College Park, MD 20742, USA. svd@umd.edu

ABSTRACT
Neurophysiologists have recently become interested in studying neuronal population activity through local field potential (LFP) recordings during experiments that also record the activity of single neurons. This experimental approach differs from early LFP studies because it uses high impedence electrodes that can also isolate single neuron activity. A possible complication for such studies is that the synaptic potentials and action potentials of the small subset of isolated neurons may contribute disproportionately to the LFP signal, biasing activity in the larger nearby neuronal population to appear synchronous and cotuned with these neurons. To address this problem, we used linear filtering techniques to remove features correlated with spike events from LFP recordings. This filtering procedure can be applied for well-isolated single units or multiunit activity. We illustrate the effects of this correction in simulation and on spike data recorded from primary auditory cortex. We find that local spiking activity can explain a significant portion of LFP power at most recording sites and demonstrate that removing the spike-correlated component can affect measurements of auditory tuning of the LFP.

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Effect of removing coupled spike activity on total LFP power. (a) Histogram of the ratio of power in the LFP after removing components explained by SUA4 and power in the raw LFP (n = 127 recording sites). For a small number of sites, LFP power increased slightly, reflecting the introduction of a small amount of noise by the cross-validation procedure used for filter estimation. (b) Histogram of change in power after removing the SUA3 component. The average power was significantly lower than for SUA4 (jackknifed t-test, P = .0008). (c) Histogram of change in power after removing the MUA component. The average power was significantly lower than for SUA3 (jackknifed t-test, P = .0007).
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fig4: Effect of removing coupled spike activity on total LFP power. (a) Histogram of the ratio of power in the LFP after removing components explained by SUA4 and power in the raw LFP (n = 127 recording sites). For a small number of sites, LFP power increased slightly, reflecting the introduction of a small amount of noise by the cross-validation procedure used for filter estimation. (b) Histogram of change in power after removing the SUA3 component. The average power was significantly lower than for SUA4 (jackknifed t-test, P = .0008). (c) Histogram of change in power after removing the MUA component. The average power was significantly lower than for SUA3 (jackknifed t-test, P = .0007).

Mentions: Standard errors on spike-LFP filters (Figures 2 and 3) were estimated by jackknifing [17]. This method allows unbiased significance tests for differences between random variables with non-Gaussian distributions, such as those often encountered in neural data. Significant changes in the LFP signal across the population of recording sites due to the removal of spike-correlated activity (Figure 4) were tested by a jackknifed t-test based on the same method of standard error estimation [17].


Decoupling action potential bias from cortical local field potentials.

David SV, Malaval N, Shamma SA - Comput Intell Neurosci (2010)

Effect of removing coupled spike activity on total LFP power. (a) Histogram of the ratio of power in the LFP after removing components explained by SUA4 and power in the raw LFP (n = 127 recording sites). For a small number of sites, LFP power increased slightly, reflecting the introduction of a small amount of noise by the cross-validation procedure used for filter estimation. (b) Histogram of change in power after removing the SUA3 component. The average power was significantly lower than for SUA4 (jackknifed t-test, P = .0008). (c) Histogram of change in power after removing the MUA component. The average power was significantly lower than for SUA3 (jackknifed t-test, P = .0007).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2821772&req=5

fig4: Effect of removing coupled spike activity on total LFP power. (a) Histogram of the ratio of power in the LFP after removing components explained by SUA4 and power in the raw LFP (n = 127 recording sites). For a small number of sites, LFP power increased slightly, reflecting the introduction of a small amount of noise by the cross-validation procedure used for filter estimation. (b) Histogram of change in power after removing the SUA3 component. The average power was significantly lower than for SUA4 (jackknifed t-test, P = .0008). (c) Histogram of change in power after removing the MUA component. The average power was significantly lower than for SUA3 (jackknifed t-test, P = .0007).
Mentions: Standard errors on spike-LFP filters (Figures 2 and 3) were estimated by jackknifing [17]. This method allows unbiased significance tests for differences between random variables with non-Gaussian distributions, such as those often encountered in neural data. Significant changes in the LFP signal across the population of recording sites due to the removal of spike-correlated activity (Figure 4) were tested by a jackknifed t-test based on the same method of standard error estimation [17].

Bottom Line: This filtering procedure can be applied for well-isolated single units or multiunit activity.We illustrate the effects of this correction in simulation and on spike data recorded from primary auditory cortex.We find that local spiking activity can explain a significant portion of LFP power at most recording sites and demonstrate that removing the spike-correlated component can affect measurements of auditory tuning of the LFP.

View Article: PubMed Central - PubMed

Affiliation: Institute for Systems Research, University of Maryland, College Park, MD 20742, USA. svd@umd.edu

ABSTRACT
Neurophysiologists have recently become interested in studying neuronal population activity through local field potential (LFP) recordings during experiments that also record the activity of single neurons. This experimental approach differs from early LFP studies because it uses high impedence electrodes that can also isolate single neuron activity. A possible complication for such studies is that the synaptic potentials and action potentials of the small subset of isolated neurons may contribute disproportionately to the LFP signal, biasing activity in the larger nearby neuronal population to appear synchronous and cotuned with these neurons. To address this problem, we used linear filtering techniques to remove features correlated with spike events from LFP recordings. This filtering procedure can be applied for well-isolated single units or multiunit activity. We illustrate the effects of this correction in simulation and on spike data recorded from primary auditory cortex. We find that local spiking activity can explain a significant portion of LFP power at most recording sites and demonstrate that removing the spike-correlated component can affect measurements of auditory tuning of the LFP.

Show MeSH