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Dynamic network properties of the interictal brain determine whether seizures appear focal or generalised

MPS-Authors

Woldman,  Wessel
Centre for Biomedical Modelling and Analysis (CBMA), University of Exeter, United Kingdom;
EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, United Kingdom;
Methods and Development Group Brain Networks, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

Terry,  John R.
Centre for Biomedical Modelling and Analysis (CBMA), University of Exeter, United Kingdom;
EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, United Kingdom;
Methods and Development Group Brain Networks, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Woldman_2020.pdf
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Citation

Woldman, W., Schmidt, H., Abela, E., Chowdhury, F. A., Pawley, A. D., Jewell, S., et al. (2020). Dynamic network properties of the interictal brain determine whether seizures appear focal or generalised. Scientific Reports, 10: 7043. doi:10.1038/s41598-020-63430-9.


Cite as: http://hdl.handle.net/21.11116/0000-0006-7820-F
Abstract
Current explanatory concepts suggest seizures emerge from ongoing dynamics of brain networks. It is unclear how brain network properties determine focal or generalised seizure onset, or how network properties can be described in a clinically-useful manner. Understanding network properties would cast light on seizure-generating mechanisms and allow to quantify to which extent a seizure is focal or generalised. Functional brain networks were estimated in segments of scalp-EEG without interictal discharges (68 people with epilepsy, 38 controls). Simplified brain dynamics were simulated using a computer model. We introduce: Critical Coupling (Cc), the ability of a network to generate seizures; Onset Index (OI), the tendency of a region to generate seizures; and Participation Index (PI), the tendency of a region to become involved in seizures. Cc was lower in both patient groups compared with controls. OI and PI were more variable in focal-onset than generalised-onset cases. In focal cases, the regions with highest OI and PI corresponded to the side of seizure onset. Properties of interictal functional networks from scalp EEG can be estimated using a computer model and used to predict seizure likelihood and onset patterns. This may offer potential to enhance diagnosis through quantification of seizure type using inter-ictal recordings.