Discrete structure of the brain rhythms

TL;DR

This paper introduces a novel, unbiased method for decomposing brain rhythms into discrete oscillons, revealing that hippocampal LFPs consist of a small number of physically meaningful oscillatory processes.

Contribution

The authors present a high-resolution, data-driven approach to identify discrete brain oscillations, challenging traditional Fourier-based methods and providing a more accurate depiction of neuronal activity.

Findings

Hippocampal LFP contains a single theta oscillon.

Presence of slow and fast gamma oscillons.

Oscillons may represent the true physical structure of brain rhythms.

Abstract

Neuronal activity in the brain generates synchronous oscillations of the Local Field Potential (LFP). The traditional analyses of the LFPs are based on decomposing the signal into simpler components, such as sinusoidal harmonics. However, a common drawback of such methods is that the decomposition primitives are usually presumed from the onset, which may bias our understanding of the signal's structure. Here, we introduce an alternative approach that allows an impartial, high resolution, hands-off decomposition of the brain waves into a small number of discrete, frequency-modulated oscillatory processes, which we call oscillons. In particular, we demonstrate that mouse hippocampal LFP contain a single oscillon that occupies the $\theta$-frequency band and a couple of $\gamma$-oscillons that correspond, respectively, to slow and fast $\gamma$-waves. Since the oscillons were identified empirically, they may represent the actual, physical structure of synchronous oscillations in neuronal ensembles, whereas Fourier-defined "brain waves" are nothing but poorly resolved oscillons.