Interpreting the electrophysiological power spectrum

Abstract

Recent experimental findings suggest that there may be rich physiological information embedded within the power spectrum of neurophysiological recordings, which, in addition to power in specific oscillatory frequencies, can be extracted with the appropriate model. This article reviews previous empirical and modeling results, as well as the canonical power law model that is often used to describe the power spectrum. In addition, a modified power law model with parameters estimating synaptic and spiking contributions is proposed.

Keywords: electrophysiology; local field potential; power law; power spectrum.

Fig. 1.

Effect of adding Poisson population spiking to a power law local field potential (LFP). A: power spectral density (PSD) of a summed Poisson spike train (10,000 neurons) is flat and increases as a function of firing rate (FR). B: robust linear fit (dashed lines) is applied to the sum of power law PSD and flat-spectrum signals from A and demonstrates flattening of slope as a result of increased FR, although the power law parameter χ remains unchanged. Colors correspond to firing rates in A. Inset: intersection frequency between fitted PSD and f⁻² decreases as FR (and broadband gamma power) increases.