Is gamma-band activity in the local field potential of V1 cortex a "clock" or filtered noise? - PubMed (original) (raw)
Is gamma-band activity in the local field potential of V1 cortex a "clock" or filtered noise?
Samuel P Burns et al. J Neurosci. 2011.
Abstract
Gamma-band (25-90 Hz) peaks in local field potential (LFP) power spectra are present throughout the cerebral cortex and have been related to perception, attention, memory, and disorders (e.g., schizophrenia and autism). It has been theorized that gamma oscillations provide a "clock" for precise temporal encoding and "binding" of signals about stimulus features across brain regions. For gamma to function as a clock, it must be autocoherent: phase and frequency conserved over a period of time. We computed phase and frequency trajectories of gamma-band bursts, using time-frequency analysis of LFPs recorded in macaque primary visual cortex (V1) during visual stimulation. The data were compared with simulations of random networks and clock signals in noise. Gamma-band bursts in LFP data were statistically indistinguishable from those found in filtered broadband noise. Therefore, V1 LFP data did not contain clock-like gamma-band signals. We consider possible functions for stochastic gamma-band activity, such as a synchronizing pulse signal.
Figures
Figure 1
Data, Noise model and Burst model signals. A. Data recorded from visually driven macaque V1. B. Noise model: simulated by assigning random phases to the Fourier amplitude spectrum of the stimulated data and taking the inverse tranform. C. Burst model: simulated by generating background noise from the spontaneous activity amplitude spectrum as in (B) summed with bursts fit to the power spectrum of the stimulated data. D. Spectrograms and power spectra. Power spectrum of Burst model matched the data's spectrum.
Figure 2
Example of autocoherence analysis of V1 LFP data. A. Gamma-band spectrogram of a stimulated V1 LFP recording. B. Time course of the recording in (A) with numbered bursts C. Phase portraits of detected gamma-band bursts. Numbers and colors of bursts corresponds to matching periods in the time course in (B). Frequencies of bursts are labelled above each portrait, arrows in portraits show direction of increasing time, and burst color indicates mean phase of bursts as coded by the color bar in (B). Range of burst frequencies and times shown here are representative of those typically measured in V1 LFP recordings.
Figure 3
Empirical joint probability distributions of measured burst frequencies and time scales over entire population of 53 sites A. Distribution of bursts measured in Noise model simulations. B. Distribution of bursts measured in the data. Distribution Peak: 40Hz and 71ms. C. Distribution of bursts measured in Burst model simulations.
Figure 4
Statistical Results. A. Marginal frequency distributions. B. Histogram of p-values using the Kolmogorov-Smirnov (KS) test for data and Noise model (green) and data and Burst model (red). C. P-values described in (B) plotted versus gamma-band power. P-values were not correlated with the amount of gamma activity.
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