Application of adaptive nonlinear Granger causality: disclosing network changes before and after absence seizure onset in a genetic rat model.
BACKGROUND
Advanced methods of signal analysis of the preictal and ictal activity dynamics characterizing absence epilepsy in humans with absences and in genetic animal models have revealed new and unknown electroencephalographic characteristics, that has led to new insights and theories.


METHODS
Taking into account that some network associations can be considered as nonlinear, an adaptive nonlinear Granger causality approach was developed and applied to analyze cortico-cortical, cortico-thalamic and intrathalamic network interactions from local field potentials (LFPs). The outcomes of adaptive nonlinear models, constructed based on the properties of electroencephalographic signal and on statistical criteria to optimize the number of coefficients in the models, were compared with the outcomes of linear Granger causality.


RESULTS
The nonlinear adaptive method showed statistically significant preictal changes in Granger causality in almost all pairs of channels, as well as ictal changes in cortico-cortical, cortico-thalamic and intrathalamic networks. Current results suggest rearrangement of interactions in the thalamo-cortical network accompanied the transition from preictal to ictal phase.


CONCLUSIONS
The linear method revealed no preictal and less ictal changes in causality.


CONCLUSIONS
Achieved results suggest that this proposed adaptive nonlinear method is more sensitive than the linear one to dynamics of network properties. Since changes in coupling were found before the seizure-related increase of LFP signal amplitude and also based on some additional tests it seems likely that they were not spurious and could not result from signal to noise ratio change.
Saratov State University, 83, Astrakhanskaya str., Saratov, Russia. Electronic address: bobrichek@mail.ru.
Marina V
MV
Sysoeva
Institute of Higher Nervous Activity and Neurophysiology RAS, 5A, Butlerova str., Moscow, Russia. Electronic address: jenia-s@mail.ru.
Evgenia
E
Sitnikova
Saratov State University, 83, Astrakhanskaya str., Saratov, Russia. Electronic address: ivssci@gmail.com.
Ilya V
IV
Sysoev
Saratov State University, 83, Astrakhanskaya str., Saratov, Russia; Saratov Branch of Institute of Radioengineering and Electronics of RAS, 38, Zelenaya str., Saratov, Russia. Electronic address: bezruchkobp@gmail.com.
Boris P
BP
Bezruchko
Biological Psychology, Donders Centre for Cognition, Radboud University Nijmegen, Montessorilaan, 3, 6525 HR, Nijmegen, The Netherlands. Electronic address: g.vanluijtelaar@donders.ru.nl.
Gilles
G
van Luijtelaar
D000465:Algorithms; D000818:Animals; D002540:Cerebral Cortex / Q000503:physiopathology*; D004195:Disease Models, Animal; D004567:Electrodes, Implanted; D004832:Epilepsy, Absence / Q000503:physiopathology*; D016014:Linear Models; D008297:Male; D009434:Neural Pathways / Q000503:physiopathology; D017711:Nonlinear Dynamics; D051381:Rats; D012640:Seizures / Q000503:physiopathology*; D012815:Signal Processing, Computer-Assisted; D013788:Thalamus / Q000503:physiopathology*; D013997:Time Factors
Absence epilepsy; Adaptive modeling; Granger causality; Network analysis; WAG/Rij rats