Indications for network regularization during absence seizures: weighted and unweighted graph theoretical analyses.
Previous studies with intracranial recordings suggested that a more random spatial structure of functional brain networks could be related to seizure generation. Here, we studied whether similar network changes in weighted and unweighted networks can be found in generalized absence seizures recorded with surface EEG. We retrospectively selected EEG recordings of eleven children with absence seizures. The functional neural networks were characterized by calculating both coherence and synchronization likelihood (SL) between 21 EEG signals that were either broad band filtered (1-48 Hz) or filtered in different frequency bands. From both weighted and unweighted networks the clustering coefficient (C) and path length (L) were computed and compared to 500 random networks. We compared the ictal with the pre-ictal network structure. During absence seizures there was an increase of synchronization in all frequency bands, seen most clearly in the SL-based networks, and the functional network topology changed towards a more ordered pattern, with an increase of C/C-s and L/L-s. This study supports the hypothesis of functional neural network changes during absence seizures. The network became more regularized in weighted and unweighted analyses, when compared to the more randomized pre-ictal network configuration.
Ponten
S C
SC
Department of Clinical Neurophysiology, VU University Medical Center, Amsterdam, The Netherlands. sc.ponten@vumc.nl
Douw
L
L
Bartolomei
F
F
Reijneveld
J C
JC
Stam
C J
CJ
D001931:Brain Mapping; D002648:Child; D002675:Child, Preschool; D004569:Electroencephalography / Q000379:methods; D004832:Epilepsy, Absence / Q000473:pathology* / Q000503:physiopathology*; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008959:Models, Neurological; D009415:Nerve Net / Q000503:physiopathology*; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000503:physiopathology; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D014894:Weights and Measures