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Neural systems are comprised of interacting units, and relevant informationregarding their function or malfunction can be inferred by analyzing thestatistical dependencies between the activity of each unit. Whilst correlationsand mutual information are commonly used to characterize these dependencies,our objective here is to extend interactions to triplets of variables to betterdetect and characterize dynamic information transfer. Our approach relies onthe measure of interaction information (II). The sign of II providesinformation as to the extent to which the interaction of variables in tripletsis redundant (R) or synergetic (S). Here, based on this approach, we calculatedthe R and S status for triplets of electrophysiological data recorded fromdrug-resistant patients with mesial temporal lobe epilepsy in order to studythe spatial organization and dynamics of R and S close to the epileptogeniczone (the area responsible for seizure propagation). In terms of spatialorganization, our results show that R matched the epileptogenic zone while Swas distributed more in the surrounding area. In relation to dynamics, R madethe largest contribution to high frequency bands (14-100Hz), whilst S wasexpressed more strongly at lower frequencies (1-7Hz). Thus, applyinginteraction information to such clinical data reveals new aspects ofepileptogenic structure in terms of the nature (redundancy vs. synergy) anddynamics (fast vs. slow rhythms) of the interactions. We expect thismethodology, robust and simple, can reveal new aspects beyond pair-interactionsin networks of interacting units in other setups with multi-recording data sets(and thus, not necessarily in epilepsy, the pathology we have approached here).

Identification of redundant and synergetic circuits in triplets of electrophysiological data