P264 Paths to depolarization block: modeling neuron dynamics during spreading depolarization events
Maria Luisa Saggio*1, Damien Depannemaecker1, Roustem Khazipov2,3, Daria Vinokurova2, Azat Nasretdinov2, Viktor Jirsa1, Christophe Bernard1
1 Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
2Laboratory of Neurobiology, Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, 420008, Russia
3Aix-Marseille University, INMED, INSERM, Marseille, 13273, France
*Email: maria-luisa.saggio@univ-amu.fr
Introduction
Spreading Depolarization (SD) is a pathological state of the brain involved in several brain diseases, including epilepsy and migraine. It consists of a slowly propagating wave of nearly complete depolarization of neurons, classically associated with a depression of cortical activity. Recent findings challenge this homology [1]: during SD events, which only partially propagate from the cortical surface to depth, neuronal activity may be suppressed, unchanged or elevated. In layers invaded by SD, neurons lose their ability to fire entering Depolarization Block (DB) and far from the SD neurons maintain their membrane potential. However, neurons in between unexpectedly displayed patterns of prolonged sustained firing.
Methods
In the present work [2], we build a phenomenological model, incorporating some key features observed during DB in this dataset (current-clamp patch-clamp recordings from L5 pyramidal neurons in the rat somatosensory cortex during evoked SDs), that can predict the new patterns observed. We model the L5 neuron as an excitable system close to a SNIC bifurcation [3], using the normal form of the unfolding of the degenerate Takens-Bogdanov singularity for the fast dynamics [4], a minimal yet dynamically rich dynamical system. The fast subsystem is modulated by the dynamics of two slow variables, implementing homeostatic and non-homeostatic reactions to inputs.
Results
The model’s bifurcation diagram provides a map for neural activity that includes baseline behavior, sustained oscillations, and DB. We identify five qualitatively different scenarios for the transition from healthy activity to DB, through specific sequences of bifurcations. These scenarios encompass and expand on the mechanisms for DB present in the modeling literature, account for the novel patterns observed in our dataset,and allow us to understand them from a unified perspective. Time series in our dataset are consistent with the scenarios, however, the presence of bistability, distinguishing some of the scenarios, cannot be inferred by our analysis. We further use the model to investigate mechanisms for the return to baseline.
Discussion
Understanding how brain circuits enter and exit SD is important to designing strategies aimed at preventing or stopping it. In this work, we use modeling to gain mechanistic insights into the ways a neuron can transition to DB or different patterns of sustained oscillatory activity during SD events, as observed in our dataset. While our work provides a unified perspective to understanding the modeling of DB, ambiguities remain in the data analysis. These ambiguities could be solved by scenario-dependent theoretical predictions, for example for the effect of stimulation, for further experimental testing.
Acknowledgements
Funded by the Russian Science Foundation grant № 24-75- 10054 to AN (https://rscf.ru/en/project/24-75-10054/) and the European Union grant № 101147319 to MS, DD and VJ.
References
[1] Nasretdinov, A., Vinokurova, D., Lemale, C. L., Burkhanova-Zakirova, G., Chernova, K., Makarova, J., ... & Khazipov, R. (2023). Diversity of cortical activity changes beyond depression during spreading depolarizations. Nature Communications, 14(1), 7729.
[2] Saggio et al (In preparation)
[3] Izhikevich, E. M. (2007). Dynamical systems in neuroscience. MIT press.
[4] Dumortier, F., Roussarie, R., & Sotomayor, J. (1991). Generic 3-parameter families of planar vector-fields, unfoldings of saddle, focus and elliptic-singularities with nilpotent linear parts.
