Functional balance at rest of hemispheric homologs assessed via normalized compression distance

Abstract

Introduction: The formation and functioning of neural networks hinge critically on the balance between structurally homologous areas in the hemispheres. This balance, reflecting their physiological relationship, is fundamental for learning processes. In our study, we explore this functional homology in the resting state, employing a complexity measure that accounts for the temporal patterns in neurodynamics.

Methods: We used Normalized Compression Distance (NCD) to assess the similarity over time, neurodynamics, of the somatosensory areas associated with hand perception (S1). This assessment was conducted using magnetoencephalography (MEG) in conjunction with Functional Source Separation (FSS). Our primary hypothesis posited that neurodynamic similarity would be more pronounced within individual subjects than across different individuals. Additionally, we investigated whether this similarity is influenced by hemisphere or age at a population level.

Results: Our findings validate the hypothesis, indicating that NCD is a robust tool for capturing balanced functional homology between hemispheric regions. Notably, we observed a higher degree of neurodynamic similarity in the population within the left hemisphere compared to the right. Also, we found that intra-subject functional homology displayed greater variability in older individuals than in younger ones.

Discussion: Our approach could be instrumental in investigating chronic neurological conditions marked by imbalances in brain activity, such as depression, addiction, fatigue, and epilepsy. It holds potential for aiding in the development of new therapeutic strategies tailored to these complex conditions, though further research is needed to fully realize this potential.

Keywords: functional source separation; neurodynamics; normalized compression distance; resting state; temporal course of the neuronal electrical activity.

Figure 1

NCD of resting-state neurodynamics. The Functional Source Separation (FSS) algorithm derives from the MEG data the resting-state neurodynamics of S1 sources in the left and right hemispheres ( $F{S}_{S1}^{sn}\left(t\right)$ and $F{S}_{S1}^{dx}\left(t\right)$ , respectively) of each subject (i = 1, 28). From time signal of the sources, the normalized compression distance (NCD) calculates the homologous similarities.

Figure 2

NCD-estimated similarity of local neurodynamics intra- and inter-subjects. (A) With the codes included in the legend and introduced in Figure 1 legend, for each subject, NCD between the rest S1 neurodynamics in the left and right hemispheres. Data of the subjects are displayed in order of their age, each equidistant from the successive, range [24–95] years; thus, Subject 1 is the youngest and Subject 28 is the oldest. The greater variability of the blue full circles in the elderly group is visible, including the smaller and biggest values. (B) The five mean values of NCD in the same scale of the values of the single subject. The vertical segments express the differences between corresponding values (color code defined by the lower values, that is the most similar neurodynamics. Blue segments express the statistical results that homologous S1 neurodynamics within the same subject were more similar than all other cases (Table 1); red segments report that self-similarity within the left-dominant hemispheres was higher than all other cases (but ${\mathrm{N}\mathrm{C}\mathrm{D}}_{\mathrm{d}\mathrm{x}}^{\mathrm{s}\mathrm{n}}$ ; Table 1).