Intrinsic frequency distribution characterises neural dynamics

TL;DR

This paper introduces a novel approach using the distribution of intrinsic frequencies from dynamic mode decomposition to characterize neural dynamics, effectively distinguishing healthy individuals from patients with neurological disorders.

Contribution

It proposes a new biomarker based on the distribution of intrinsic frequencies derived from DMD, outperforming traditional Fourier-based methods in classifying neural states.

Findings

DM frequency distribution accurately distinguishes patients from healthy subjects.

Distribution provides distinct information from amplitude spectra.

Method shows potential as a biomarker for neurological conditions.

Abstract

Decomposing multivariate time series with certain basic dynamics is crucial for understanding, predicting and controlling nonlinear spatiotemporally dynamic systems such as the brain. Dynamic mode decomposition (DMD) is a method for decomposing nonlinear spatiotemporal dynamics into several basic dynamics (dynamic modes; DMs) with intrinsic frequencies and decay rates. In particular, unlike Fourier transform-based methods, which are used to decompose a single-channel signal into the amplitudes of sinusoidal waves with discrete frequencies at a regular interval, DMD can derive the intrinsic frequencies of a multichannel signal on the basis of the available data; furthermore, it can capture nonstationary components such as alternations between states with different intrinsic frequencies. Here, we propose the use of the distribution of intrinsic frequencies derived from DMDs (DM frequencies) to characterise neural activities. The distributions of DM frequencies in the electroencephalograms of healthy subjects and patients with dementia or Parkinson's disease in a resting state were evaluated. By using the distributions, these patients were distinguished from healthy subjects with significantly greater accuracy than when using amplitude spectra derived by discrete Fourier transform. This finding suggests that the distribution of DM frequencies exhibits distinct behaviour from amplitude spectra, and therefore, the distribution may serve as a new biomarker by characterising the nonlinear spatiotemporal dynamics of electrophysiological signals.