Stability of Brain Networks in Epilepsy
Poster No:
1189
Submission Type:
Abstract Submission
Authors:
Jieru Liao1, Joseph Lizier1
Institutions:
1School of Computer Science and Centre for Complex Systems, The University of Sydney, Sydney, New South Wales
First Author:
Jieru Liao
School of Computer Science and Centre for Complex Systems, The University of Sydney
Sydney, New South Wales
School of Computer Science and Centre for Complex Systems, The University of Sydney
Sydney, New South Wales
Co-Author:
Joseph Lizier
School of Computer Science and Centre for Complex Systems, The University of Sydney
Sydney, New South Wales
School of Computer Science and Centre for Complex Systems, The University of Sydney
Sydney, New South Wales
Introduction:
Epilepsy is a prevalent neurological disorder affecting millions of individuals worldwide[1], and is increasingly regarded as a network-level phenomenon in which abnormal synchrony and instability emerge across interconnected brain regions[2][3]. Network neuroscience approaches, capitalizing on their ability to model multivariate relationships, have not only offered new insights into how the underlying network structure to various seizure states[4], but have also outperformed traditional single-variable biomarkers (e.g., high-frequency oscillations, HFO) in identifying potential epileptogenic zones and characterizing pre-seizure network dynamics[5]. However, many existing analyses emphasize functional connectivity and/or indirect measures of network instability, resulting in a limited mechanistic understanding of epileptic dynamics[6]. To address these limitations, this study analysesemploys dynamics in generative models of epilepsy and introduces a direct measure of how network structure influences instability.
Methods:
We employed a one-dimensional Epileptor model from The Virtual Brain platform to simulate brain dynamics before and after seizures[7]. We utilized weighted connectivity matrices from these simulations, and specifically model the dynamics of fluctuations around a stable state. We developed a novel mathematical approach to measure instability of the (linearized) dynamics based on the network structure[8][9](see figure 1).This including a new measure to quantify each node's "driving" and "driven" impact on overall network instability, offering a more direct alternative to characterising instability as compared to traditional graph metrics like node controllability and centrality. To further characterize epileptogenic progression from a structural standpoint, our approach differentiates and explains instabilities caused by focal versus distributed seizures.
·Convergence walks of instability
Results:
Our findings demonstrate that network-based instability measures can define distinct epileptic states (healthy, non-propagating seizure states, and propagating seizure states) through the variation of node excitability and global coupling parameters (see figure 2 (a)). This approach may provide early warning indicators and system transitions towards the critical boundary between normal and seizure conditions. Moreover, our new mathematics identifies key nodes with strong influence in driving the network away from the stable-state (see figure 2 (b)). And crucially, this influence correlates to their susceptibility to drive seizures as their excitability increases, underlining the potential of this method to identify epileptogenic nodes. Finally, we compared the results to null network models with various aspects of the structure homogenized, which validated the role of network structure in determining the emergence and diffusion of instability.
·Phase diagram and epileptogenic nodes
Conclusions:
This new instability measure not only serves as a potential biomarker for epileptic seizures but also enhances the precision in localizing epileptogenic zones. This remains to be explored in empirical data. Furthermore, quantifying the disruptive impact of individual nodes on the network opens avenues for more targeted surgical and neuromodulation strategies, such as deep brain stimulation and responsive neurostimulation, improving treatment outcomes for patients with epilepsy.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Methods Development 2
Keywords:
Computational Neuroscience
Epilepsy
Other - Networks, Stability, Neural Mass Model
1|2Indicates the priority used for review
Abstract Information
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[2] Stam, C. J. (2014). Modern network science of neurological disorders. Nature Reviews Neuroscience, 15(10), 683-695.
[3] Jiruska, P., De Curtis, M., Jefferys, J. G., Schevon, C. A., Schiff, S. J., & Schindler, K. (2013). Synchronization and desynchronization in epilepsy: controversies and hypotheses. The Journal of physiology, 591(4), 787-797.
[4] Khambhati, A. N., Davis, K. A., Lucas, T. H., Litt, B., & Bassett, D. S. (2016). Virtual cortical resection reveals push-pull network control preceding seizure evolution. Neuron, 91(5), 1170-1182.
[5] Li, A., Huynh, C., Fitzgerald, Z., Cajigas, I., Brusko, D., Jagid, J., ... & Sarma, S. V. (2021). Neural fragility as an EEG marker of the seizure onset zone. Nature neuroscience, 24(10), 1465-1474.
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[7] Proix, T., Bartolomei, F., Guye, M., & Jirsa, V. K. (2017). Individual brain structure and modelling predict seizure propagation. Brain, 140(3), 641-654.
[8] Barnett, L., Buckley, C. L., & Bullock, S. (2009). Neural complexity and structural connectivity. Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 79(5), 051914.
[9] Lizier, J. T., Bauer, F., Atay, F. M., & Jost, J. (2023). Analytic relationship of relative synchronizability to network structure and motifs. Proceedings of the National Academy of Sciences, 120(37), e2303332120.
[10] Moosavi, S. A., Jirsa, V. K., & Truccolo, W. (2022). Critical dynamics in the spread of focal epileptic seizures: Network connectivity, neural excitability and phase transitions. Plos one, 17(8), e0272902.