Frequency Independent Stable Cross-Correlation Pattern in the Peri-Ictal Transition of Focal Onset Seizures

Abstract

Objective:

Here we aim to search for stable intra- and inter-band cross-correlations during the peri-ictal transition of focal onset seizures. Furthermore, we search for dynamic features by analyzing relative eigenvalues of the cross-correlation matrix.

Methods:

In this study, we analyze 50 extracranial electroencephalographic recordings from 24 patients with different types of focal epilepsy, separating the data into different frequency bands. Thereby we construct a multiband cross-correlation matrix, evaluate stability of the correlation structures and the time evolution of relative eigenvalues using a running window approach.

Results:

We find a consistent, pronounced average cross-correlation pattern that is independent of the physiological state, is subject-independent, and is highly similar across different frequency bands. In contrast, dynamic features of brain activity are encoded in deviations from this baseline pattern, expressed by relative eigenvalues along the whole spectrum.

Conclusion:

We associate the stable background pattern as the dynamics upon (or close to) the attractor dynamics, necessary to maintain the brain in an efficient operational mode. Transient dynamical features are expressed by temporal deviations from this pattern. Our results are congruent with the hypothesis that the brain is a complex system operating close to a critical point of a phase transition.

Impact Statement

We found a distinct average cross-correlation pattern, which is independent of the physiological state, is subject-independent, and is very similar in different frequency bands. This is surprising, as it is well known that different brain activities are associated with different frequency bands. We relate this counterintuitive result to the hypothesis that the brain operates near a critical point of a phase transition, which explains the presence of permanently active, pronounced large-scale cross-correlations. Our research opens new avenues of linear correlation analysis of brain activity, which can help in understanding sleep architecture, cognitive processes, and learning of brain activity during epileptic events.