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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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Example spike and LFP responses for a second recording site. Data are plotted as in Figure 2. (a) The impulse responses for SUA4 and SUA3 (subpanel at lower right) are smaller than the impulse responses in Figure 2, and using this function to remove LFP components that could be predicted by SUA events had very little effect. (b) Similarly, removing LFP components that could be predicted by MUA had very little effect on the LFP for this site.
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fig3: Example spike and LFP responses for a second recording site. Data are plotted as in Figure 2. (a) The impulse responses for SUA4 and SUA3 (subpanel at lower right) are smaller than the impulse responses in Figure 2, and using this function to remove LFP components that could be predicted by SUA events had very little effect. (b) Similarly, removing LFP components that could be predicted by MUA had very little effect on the LFP for this site.

Mentions: The raw local field potential (LFP), L0(t), was extracted from the electrophysiological recording by low-pass filtering (<150 Hz, linear-phase FIR, duration 100 ms) of the low frequency component of the recorded electrophysiological signal [6, 10, 11]. The signals L0(t) and rh(t) existed in entirely different frequency bands and thus were orthogonal (i.e., linearly uncorrelated). However, extracting single or multiunit activity from rh(t) involved nonlinear computations that could reintroduce linear correlation between them. This coupled component was identified by measuring their cross covariance (3)csl(τ)=〈(L0(t)−〈L0〉t)(s(t−τ)−〈s〉t)〉t. The spike signal used here, s(t), could be any of the different spike signals defined above. In this study, csl, css, and h (see below) were estimated for τ = −500,…, 500 ms. Larger values of τ had no effect on filter estimates (note width of nonzero filter range <200 ms in Figures 2 and 3).


Decoupling action potential bias from cortical local field potentials.

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

Example spike and LFP responses for a second recording site. Data are plotted as in Figure 2. (a) The impulse responses for SUA4 and SUA3 (subpanel at lower right) are smaller than the impulse responses in Figure 2, and using this function to remove LFP components that could be predicted by SUA events had very little effect. (b) Similarly, removing LFP components that could be predicted by MUA had very little effect on the LFP for this site.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Example spike and LFP responses for a second recording site. Data are plotted as in Figure 2. (a) The impulse responses for SUA4 and SUA3 (subpanel at lower right) are smaller than the impulse responses in Figure 2, and using this function to remove LFP components that could be predicted by SUA events had very little effect. (b) Similarly, removing LFP components that could be predicted by MUA had very little effect on the LFP for this site.
Mentions: The raw local field potential (LFP), L0(t), was extracted from the electrophysiological recording by low-pass filtering (<150 Hz, linear-phase FIR, duration 100 ms) of the low frequency component of the recorded electrophysiological signal [6, 10, 11]. The signals L0(t) and rh(t) existed in entirely different frequency bands and thus were orthogonal (i.e., linearly uncorrelated). However, extracting single or multiunit activity from rh(t) involved nonlinear computations that could reintroduce linear correlation between them. This coupled component was identified by measuring their cross covariance (3)csl(τ)=〈(L0(t)−〈L0〉t)(s(t−τ)−〈s〉t)〉t. The spike signal used here, s(t), could be any of the different spike signals defined above. In this study, csl, css, and h (see below) were estimated for τ = −500,…, 500 ms. Larger values of τ had no effect on filter estimates (note width of nonzero filter range <200 ms in Figures 2 and 3).

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