Poster No:
1626
Submission Type:
Abstract Submission
Authors:
Marisa Saggio1, Roustem Khazipov2, Daria Vinokurova3, Azat Nasretdinov3, Viktor Jirsa1, Christophe Bernard1
Institutions:
1Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France, 2Aix-Marseille University, INMED, INSERM, Marseille, France, 3Laboratory of Neurobiology, Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
First Author:
Marisa Saggio
Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst
Marseille, France
Co-Author(s):
Daria Vinokurova
Laboratory of Neurobiology, Institute of Fundamental Medicine and Biology, Kazan Federal University
Kazan, Russia
Azat Nasretdinov
Laboratory of Neurobiology, Institute of Fundamental Medicine and Biology, Kazan Federal University
Kazan, Russia
Viktor Jirsa
Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst
Marseille, France
Introduction:
Spreading Depolarization (SD) is a pathological state of the brain involved in several brain diseases, including epilepsy and migraine. SD consists of a slowly propagating wave of nearly complete depolarization of neurons, classically associated with a depression of cortical activity. This homology between SD and spreading depression has been recently challenged (Nasretdinov, 2023): during SD events, which only partially propagate from the cortical surface to depth, neuronal activity may be suppressed, unchanged or elevated depending on the distance to the SD stop depth. These patterns can be explained by analysing the activity of single neurons. 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. In the present work (Saggio, in preparation), we build a phenomenological model, incorporating some key features observed during DB in this dataset, that is able to predict the new patterns observed. We use the model to identify different paths to DB and further analyze our dataset to distinguish among the proposed scenarios.
Methods:
Data: Current-clamp patch-clamp recordings from 10 L5 pyramidal neurons in the rat somatosensory cortex during SDs evoked by distant application of 1M KCl (Nasretdinov, 2023).
Modeling: Generic model for an excitable system close to a SNIC bifurcation, based on the normal form of the degenerate Takens-Bogdanov singularity (Dumortier, 1991).
Bifurcations profiles: based on the behavior of the baseline and specific trends for the frequency and amplitude (Izhikevich, 2007).
Results:
The model's bifurcation diagram provides a map for neural activity that includes DB together with the patterns observed for intermediate values of depolarization. In the map we identified five 'paths' from healthy activity to DB, qualitatively different in terms of the underlying dynamics. Four of the paths involve a SNIC bifurcation to transition from the healthy to sustained oscillations. For strong levels of depolarization, the neuron settles into a new fixed point, the DB state. This latter transition can be due to a supercritical Hopf or a Fold Limit Cycle bifurcation. Both can occur with or without bistability between the healthy and pathological state, giving four possible scenarios. These scenarios encompass the mechanisms for DB present in the modeling literature and allow us to understand them from a unified perspective. We add an additional scenario predicted by our model in which the transition to DB relies on movement in state space rather than on a bifurcation. We analyzed whole cell recordings from L5 pyramidal neurons to identify bifurcations' profiles. We identified neurons whose timeseries are consistent with a supercritical Hopf bifurcation transition to DB and others with a Fold Limit Cycle bifurcation. The presence of bistability instead, cannot be inferred by this analysis. Different scenarios, however, lead to different predictions, in terms of how the system can be manipulated to restore the healthy state (e.g. through stimulation), that can be experimentally tested in future work
Conclusions:
Understanding how brain circuits enter and exit SD is important to design strategies aimed at preventing or stopping it. In this work we use modeling to gain mechanistic insights on the ways a neuron can transition to DB or to different patterns of sustained oscillatory activity during SD events, as observed in our dataset. While our work provides a unified perspective to understanding modeling of DB, ambiguities remain in the data analysis. We propose scenario-dependent theoretical predictions, for example for the effect of stimulation, for further experimental testing.
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.
Modeling and Analysis Methods:
Other Methods 1
Physiology, Metabolism and Neurotransmission:
Physiology, Metabolism and Neurotransmission Other 2
Keywords:
Computational Neuroscience
Cortical Layers
Data analysis
DISORDERS
ELECTROPHYSIOLOGY
Modeling
Neuron
Single unit recording
Other - Depolarization block; Bifurcations
1|2Indicates the priority used for review
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Provide references using APA citation style.
Dumortier, F. (1991). Generic 3-parameter families of planar vector-fields, unfoldings of saddle, focus and elliptic-singularities with nilpotent linear parts.
Izhikevich, E. M. (2007). Dynamical systems in neuroscience. MIT press.
Nasretdinov, A. (2023). Diversity of cortical activity changes beyond depression during spreading depolarizations. Nature Communications, 14(1), 7729
Saggio, M. L. (In preparation). Paths to depolarisation block: modelling neuron dynamics during spreading depolarisation events
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