Altered Resting State Brain Dynamics in Temporal Lobe Epilepsy Can Be Observed in Spectral Power, Functional Connectivity and Graph Theory Metrics

Abstract

Despite a wealth of EEG epilepsy data that accumulated for over half a century, our ability to understand brain dynamics associated with epilepsy remains limited. Using EEG data from 15 controls and 9 left temporal lobe epilepsy (LTLE) patients, in this study we characterize how the dynamics of the healthy brain differ from the “dynamically balanced” state of the brain of epilepsy patients treated with anti-epileptic drugs in the context of resting state. We show that such differences can be observed in band power, synchronization and network measures, as well as deviations from the small world network (SWN) architecture of the healthy brain. The θ (4–7 Hz) and high α (10–13 Hz) bands showed the biggest deviations from healthy controls across various measures. In particular, patients demonstrated significantly higher power and synchronization than controls in the θ band, but lower synchronization and power in the high α band. Furthermore, differences between controls and patients in graph theory metrics revealed deviations from a SWN architecture. In the θ band epilepsy patients showed deviations toward an orderly network, while in the high α band they deviated toward a random network. These findings show that, despite the focal nature of LTLE, the epileptic brain differs in its global network characteristics from the healthy brain. To our knowledge, this is the only study to encompass power, connectivity and graph theory metrics to investigate the reorganization of resting state functional networks in LTLE patients.

Reproducibility of Graph-Theoretic Brain Network Metrics: A Systematic Review

“…Previous studies have suggested the use of graph metrics in clinical trials (Petrella, 2011) and as diagnostic tools (Quraan et al, 2013;Schoonheim et al, 2013). There is clear appeal to this approach.…”

“…Studies have demonstrated significant differences in metrics derived from graphs of brain networks between diseased and healthy groups as well as in normal development (Supekar et al, 2009), for example, in multiple sclerosis (He et al, 2009), Alzheimer's (Buckner et al, 2009;Stam et al, 2009), Parkinson's (Göttlich et al, 2013), epilepsy (Quraan et al, 2013), and body dysmorphic disorder (Arienzo et al, 2013) [for reviews see Bassett and Bullmore (2009);Menon (2011);Wang et al (2010)], and have offered various interpretations of these findings. With this wave of positive results, some authors have suggested the use of graph metrics as surrogate markers in clinical trials (Petrella, 2011) and even suggested that they have potential as diagnostic tools (Quraan et al, 2013;Schoonheim et al, 2013). However, such applications Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom.…”

Automatic Lateralization of Temporal Lobe Epilepsy Based on MEG Network Features Using Support Vector Machines

“…Studies have shown that asymmetric and slow activity of the delta band (1-4 Hz) can reliably lateralize to the epileptogenic hemisphere [14,[60][61][62]. Indeed, significant differences in delta band activity were found between TLE patients and controls in network analyses [31,63,64]. Thus, we only selected one frequency band, the delta band, as our frequency band of interest.…”

Slow Activity in Focal Epilepsy During Sleep and Wakefulness

“…Widespread PSD differences between patients with epilepsy and controls have been already described. 37,38 They are possibly related to the medications [39][40][41] or to the disease itself. An increase of power can be produced by a larger number of neurons oscillating synchronously, higher synchrony or both.…”