Cross-correlation markers in stochastic dynamics of complex systems

TL;DR

This paper demonstrates how the memory functions formalism can analyze cross-correlations in MEG signals, revealing differences between healthy and epileptic brains, and suggesting a new method for detecting neurological abnormalities.

Contribution

The study extends the memory functions formalism to analyze cross-correlations in MEG signals, highlighting its potential for identifying pathological brain activity.

Findings

Healthy subjects show frequency synchronization and low-frequency dominance.

Epileptic patient exhibits abnormal frequency synchronization and high-frequency processes.

Cross-correlation dynamics can indicate neurological abnormalities.

Abstract

The neuromagnetic activity (magnetoencephalogram, MEG) from healthy human brain and from an epileptic patient against chromatic flickering stimuli has been earlier analyzed on the basis of a memory functions formalism (MFF). Information measures of memory as well as relaxation parameters revealed high individuality and unique features in the neuromagnetic brain responses of each subject. The current paper demonstrates new capabilities of MFF by studying cross-correlations between MEG signals obtained from multiple and distant brain regions. It is shown that the MEG signals of healthy subjects are characterized by well-defined effects of frequency synchronization and at the same time by the domination of low-frequency processes. On the contrary, the MEG of a patient is characterized by a sharp abnormality of frequency synchronization, and also by prevalence of high-frequency quasi-periodic processes. Modification of synchronization effects and dynamics of cross-correlations offer a promising method of detecting pathological abnormalities in brain responses.