The role of spectral complexity in connectivity estimation

TL;DR

This paper investigates how spectral complexity affects the estimation of neural connectivity from MEG data, revealing that regularization depends on signal-to-noise ratio and spectral complexity, impacting connectivity accuracy.

Contribution

It demonstrates the relationship between spectral complexity, regularization, and connectivity estimation accuracy using synthetic MEG data.

Findings

Regularization correlates with signal-to-noise ratio.

Spectral complexity influences the regularization process.

Optimal connectivity estimation is affected by spectral properties.

Abstract

The study of functional connectivity from magnetoecenphalographic (MEG) data consists in quantifying the statistical dependencies among time series describing the activity of different neural sources from the magnetic field recorded outside the scalp. This problem can be addressed by utilizing connectivity measures whose computation in the frequency domain often relies on the evaluation of the cross-power spectrum of the neural time-series estimated by solving the MEG inverse problem. Recent studies have focused on the optimal determination of the cross-power spectrum in the framework of regularization theory for ill-posed inverse problems, providing indications that, rather surprisingly, the regularization process that leads to the optimal estimate of the neural activity does not lead to the optimal estimate of the corresponding functional connectivity. Along these lines, the present paper utilizes synthetic time series simulating the neural activity recorded by an MEG device to show that the regularization of the cross-power spectrum is significantly correlated with the signal-to-noise ratio of the measurements and that, as a consequence, this regularization correspondingly depends on the spectral complexity of the neural activity.