Identification of redundant and synergetic circuits in triplets of electrophysiological data

Abstract

Abstract.Objective. Neural systems are comprised of interacting units, and relevant information regarding their function or malfunction can be inferred by analyzing the statistical dependencies between the activity of each unit. Whilst correlations and mutual information are commonly used to characterize these dependencies, our objective here is to extend interactions to triplets of variables to better detect and characterize dynamic information transfer. Approach. Our approach relies on the measure of interaction information (II). The sign of II provides information as to the extent to which the interaction of variables in triplets is redundant (R) or synergetic (S). Three variables are said to be redundant when a third variable, say Z, added to a pair of variables (X,Y), diminishes the information shared between X and Y.Similarly, the interaction in the triplet is said to be synergetic when conditioning on Z enhances the information shared between X and Y with respect to the unconditioned state. Here, based on this approach, we calculated the R and S status for triplets of electrophysiological data recorded from drug-resistant patients with mesial temporal lobe epilepsy in order to study the spatial organization and dynamics of R and S close to the epileptogenic zone (the area responsible for seizure propagation). Main results.In terms of spatial organization, our results show that R matched the epileptogenic zone while S was distributed more in the surrounding area. In relation to dynamics, R made the largest contribution to high frequency bands (14-100Hz), whilst S was expressed more strongly at lower frequencies (1-7Hz). Thus, applying interaction information to such clinical data reveals new aspects of epileptogenic structure in terms of the nature (redundancy vs. synergy) and dynamics (fast vs. slow rhythms) of the interactions. Significance. We expect this methodology, robust and simple, can reveal new aspects beyond pair-interactions in networks of interacting units in other setups with multi-recording data sets (and thus, not necessarily in epilepsy, the pathology we have approached here).

A spatially resolved network spike in model neuronal cultures reveals nucleation centers, circular traveling waves and drifting spiral waves

“…Despite some theoretical advances [25, 38–45], a theory for the origin of the nucleation centers of network spikes, enabling prediction of the number and locations of primary nucleation centers before carrying out the dynamic simulations, is currently absent. We have excluded the influence of fluctuations of the spatial distribution density of neurons by placing the neurons strictly periodically in the nodes of a square lattice - the nucleation centers still occurred (Fig.…”

Temporal information partitioning: Characterizing synergy, uniqueness, and redundancy in interacting environmental variables

“…Section 3 focuses on the application of information partitioning to time-series data sets. While the information partitioning approach has been applied to time-series data in neural and cardiovascular applications [Erramuzpe et al, 2015;Faes et al, 2015], these have assumed either Gaussianity or simple fixed binning pdf estimation techniques, and use the prevalent approach based on minimum mutual information. When working with environmental time series data, we need robust methods to handle noise or observational errors, atom-at-zero effects for mixed distributions and characterization of statistical significance of measures.…”

Interaction information along lifespan of the resting brain dynamics reveals a major redundant role of the default mode network

“…Interaction information (II) quantifies in triplets of variables the amount of redundant (positive interaction) or synergetic (negative interaction) information contained in the triplet [1,2]. While the mutual information (MI) shared between two variables is always positive or zero (for the case of independent variables), II can be either positive or negative, respectively, unveiling redundancy (R) or synergy (S).…”

“…The presence of synergetic effects is well-known to occur in sociological and psychological modeling, where (very often) there are some variables that increase the prediction power on different ones [5]. On the other hand, redundancy have been addressed before in gene regulatory networks [6,7] and electrophysiological data in patients with epilepsy [2] or with deficit of consciousness [8], but, the pattern of triplet interactions in functional magnetic resonance imaging is not yet well-understood. By using a different methodology based on Granger causality influence, the authors in [9] found that R regions occurred mainly due to voxel-contiguity and inter-hemispheric symmetry, while S occurred mainly between non-homologous region pairs in contra-lateral hemispheres.…”