Evolving networks in the human epileptic brain

Abstract

Network theory provides novel concepts that promise an improved characterization of interacting dynamical systems. Within this framework, evolving networks can be considered as being composed of nodes, representing systems, and of time-varying edges, representing interactions between these systems. This approach is highly attractive to further our understanding of the physiological and pathophysiological dynamics in human brain networks. Indeed, there is growing evidence that the epileptic process can be regarded as a large-scale network phenomenon. We here review methodologies for inferring networks from empirical time series and for a characterization of these evolving networks. We summarize recent findings derived from studies that investigate human epileptic brain networks evolving on timescales ranging from few seconds to weeks. We point to possible pitfalls and open issues, and discuss future perspectives.Comment: In press (Physica D

Increased gamma and decreased fast ripple connections of epileptic tissue: A high‐frequency directed network approach

“…At the mesoscale, the coordinated activity across several centimeters of neocortex explains the signs and symptoms during focal seizures . At the macroscale, disrupted whole brain interactions explain the observed cognitive and behavioral symptoms not necessarily associated with the location of the epileptic focus …”

“…9,10 At the macroscale, disrupted whole brain interactions explain the observed cognitive and behavioral symptoms not necessarily associated with the location of the epileptic focus. [11][12][13] Brain networks can be defined at the level of synchronicity between brain areas, named functional connectivity. 10,14,15 Functional connectivity can be computed in numerous ways; each approach quantifies different aspects of (changes in) neuronal interactions.…”

Coupling functions: Universal insights into dynamical interaction mechanisms

“…The phase was then extracted from each filtered time series, using, for example, the Hilbert transform or the synchrosqueezed wavelet transform. During this preprocessing procedure, particular care was taken to minimize overlap between the spectra of the intervals (Lehnertz et al, 2014): overlaps of consecutive frequency intervals result in overestimation of the corresponding phase-to-phase coupling. Dynamical Bayesian inference was then used to reconstruct the coupling functions from the multivariate five-phase oscillators.…”

Construction of complex networks from time series based on the cross correlation interval

“…McCullough et al have recently proposed the ordinal pattern partition networks that are formed from time series by symbolizing the data into ordinal patterns [15]. The above mentioned methods have been used in different fields including medicine [16][17][18], astronomy [19], financial analysis [20] and geophysics [21].…”