A symbolic dynamics approach to Epileptic Chronnectomics: Employing strings to predict crisis onset

Treating brain as a complex system is currently among the most popular approaches that are used to understand its function and explain encephalopathies. The networked epileptic brain has already been studied through various neuroimaging modalities based on estimates of functional connectivity. Here, we suggest that additional insights to epileptogenesis can be gained with an appropriate description on the network (re)organization dynamics as these are reflected in surface EEG measurements. Our approach commences with a pattern analytic step that turns a time series of connectivity patterns into a symbolic time series. The condensed dynamics are then analyzed by means of Markov chain modeling and a motif detection algorithm. Both descriptions yield novel dynamic characteristics that can capture the progression towards an epileptic crisis. Conceptually, our work adds to the emerging field of “chronnectomics”. Empirically, the obtained results based on actual experimental data are very encouraging and deserve further consideration.