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.

This proof-of-concept (PoC) study presents a pipeline made by two blocks: 1. the identification of the network that generates interictal epileptic activity; and 2. the study of the time course of the electrical activity that it generates, called neurodynamics, and the study of its functional connectivity to the other parts of the brain. Network identification is achieved with the Functional Source Separation (FSS) algorithm applied to electroencephalographic (EEG) recordings, the neurodynamics quantified through signal complexity with the Higuchi Fractal Dimension (HFD), and functional connectivity with the Directed Transfer Function (DTF). This PoC is enhanced by the data collected before and after neuromodulation via transcranial Direct Current Stimulation (tDCS, both Real and Sham) in a single drug-resistant epileptic person. We observed that the signal complexity of the epileptogenic network, reduced in the pre-Real, pre-Sham, and post-Sham, reached the level of the rest of the brain post-Real tDCS. DTF changes post-Real tDCS were maintained after one month. The proposed approach can represent a valuable tool to enhance understanding of the relationship between brain neurodynamics characteristics, the effects of non-invasive brain stimulation, and epileptic symptoms.

The neuronal functional connectivity is a complex and non-stationaryphenomenon creating dynamic networks synchronization determining thebrain states and needed to produce tasks. Here, as a measure that quantifiesthe synchronization between the neuronal electrical activity of two brainregions, we used the normalized compression distance (NCD), which is thelength of the compressed file constituted by the concatenated two signals,normalized by the length of the two compressed files including each singlesignal. To test the NCD sensitivity to physiological properties, we used NCDto measure the cortico-muscular synchronization, a well-known mechanismto control movements, in 15 healthy volunteers during a weak handgrip.Independently of NCD compressor (Huffman or Lempel Ziv), we foundout that the resulting measure is sensitive to the dominant-non dominantasymmetry when novelty management is required (p = 0.011; p = 0.007,respectively) and depends on the level of novelty when moving the non-dominant hand (p = 0.012; p = 0.024). Showing lower synchronization levelsfor less dexterous networks, NCD seems to be a measure able to enrich theestimate of functional two-node connectivity within the neuronal networksthat control the body.

Introduction: The brain is a connected network, requiring complex-system measures to describe its organization principles [1,2]. Here, we aim at testing whether the normalized compression distance (NCD) [3] is a suitable quantifier of the functional connectivity between cortical regions. This new measure estimates the information shared by two signals comparing the compression length of one signal given the other, without requiring any representation of the single in harmonics or selecting a specific time window where to compare the two signals. We show that this new measure is a good candidate to estimate the inter-nodes connectivity since it displays features 'expected' for brain connectivity, i.e. it is maximal between homologous cortical areas, it is higher for dominant cortical areas, it depends on age. In order to do it we estimated the NCD between functionally homologous primary somatosensory areas (S1) activities, testing the above-mentioned properties. Methods: Twenty-eight healthy, right-handed volunteers participated in the study. We recorded brain magnetic activity in the left and right Rolandic regions by a 28-channel magnetoencephalographic (MEG) system. We recorded rest activity for 3 min in each hemisphere. MEG activity was also collected during the electrical stimulation of the contralateral median nerve at the wrist delivered via surface disks. Elicited electric pulses were 0.2 ms in duration and 631 ms of inter-stimulus interval. Left and right median nerves were separately stimulated, totaling about 200 artifact-free trials for each. We used the Functional Source Separation (FSS) [4,5] algorithm to identify functionally homologous areas in the two hemispheres devoted to the somatosensory hand representation (FS_S1). Therefore, we calculated NCD between the left and right FS_S1s at rest. NCD is a parameter-free, quasi-universal similarity measure, computed from the lengths of compressed data files, singly and in pairwise concatenation. In other terms, NCD defines that two objects are similar if we can significantly "compress" one given the information of the other. We compared the similarity between the left and right homologous areas in single subjects and across the whole group. In particular, we compared the similarity of the activities in the two hemispheres of the same subject, with that in the same or in the opposite hemisphere of different subjects in the group of people. Results: NCD was minimal (maximal functional connectivity) between the neuronal activities of hemispheric functionally homologous areas in the same subject, i.e the NCD between the left and right FS_S1 of the same person was smaller than across different subjects (p<10 -7 consistently). NCD was smaller within the left dominant hemisphere than within the non dominant right one (p=3o10-7), suggesting that more skilled cortical areas express more tuned neuronal activities. Finally, it became more variable in older than younger people (p=.01), indicating that it is sensitive to proprioceptive and sensorimotor skills degradation typical of aging. Conclusions: NCD displayed an excellent ability in quantifying the similarity among neuronal activities, catching the maximal similarity expected for functionally homologous cortical areas of the two hemispheres. It was also sensitive to dominant- and age-dependent properties of somatosensory representation activities. This ability to catch key features of neuronal activity's dynamics indicates NCD as a good candidate for studies of brain functional connectivity, able to overcome the limitations intrinsic to the classical Fourier or autoregressive estimates in assessing the dynamics of two-nodes functional conections.

The brain is a connected network, requiring complex-system measures to describe its organization principles. The normalized compression distance (NCD) [1] is a parameter -free, quasi universal similarity measure that estimates the information shared by two signals comparing the compression length of one signal given the other. Here, we aim at testing whether this new measure is a suitable quantifier of the functional connectivity between cortical regions. In particular, we tested whether NCD between homologous hemispheric regions is smaller (higher connectivity) in the same person than across different people, if it is smaller in the dominant hemisphere and if it depends on age. We used the Functional Source Separation (FSS) [2] algorithm on magnetoencephalographic (MEG) data in order to identify functionally homologous areas in the two hemispheres devoted to the somatosensory contra-lateral hand representation (FS_S1) in 28 healthy people. Therefore, we calculated NCD between the left and right FS_S1s activities at rest. We found that NCD 1) between left and right FS_S1s of the same person was smaller than across different people (p<10-7consistently) 2) was smaller within the left dominant hemisphere than within the non dominant right one (p=3*10 7) and 3) became more variable in older than younger people (p=.01). This preliminary work shows that NCD, which measures the similarity of neuronal source activities via their compression sizes, displays an excellent ability in quantifying the similarity among neuronal activities, catching the maximal similarity expected for functionally homologous cortical areas of the two hemispheres. Thus, NCD seems a good candidate for two-nodes functional connectivity measure in resting state, able to overcome the limitations intrinsic to the classical Fourier or autoregressive estimates in assessing dynamics properties of the brain connectivity.