CNS*2026 Halifax

Introduction

Spreading depolarizations are waves of brief neuronal hyperexcitability followed by prolonged depolarization that propagates through grey matter at a rate of 1–9mm/min. Such waves are associated with multiple neurological disorders, including epilepsy, ischemic stroke, and migraine aura. Neurons are susceptible to SD due to their high energy demand, particularly for restoring ion concentrations after action potentials (APs). Here, we model SD to identify different factors that influence neurons and neuronal populations' vulnerability.


Methods
We build on our prior in vitro model of spreading depolarization[1], with connectivity from Potjans-Diesmann cortical microcircuit (PDCM)[2,3] and O2 sources based on capillaries in human V1. The model was developed in NetPyNE using NEURON/RxD to account for ion concentrations and homeostatic mechanisms, including Na+/K+-ATPase, NKCC1, KCC2, and dynamic volume changes. A 2.0 x 2.3 cm cross-section of the human cortical plate in V1 with immunostaining for CD34 was used to determine capillary locations. Optuna was used to determine both single-cell parameters and synaptic weights to achieve firing rates, synchrony, and irregularity comparable to those of the original PDCM.


Results
We simulated 13,000 neurons representing ~1 mm³ of cortex (layers 2-6). We monitored intracellular and extracellular ion concentrations (Na⁺, K⁺, Cl^-) and O2. O2 was supplied by 918 capillaries (density: 199.6/cm²; cross-sectional area: 16.7±11.9μm²) identified by immunohistochemistry. SD could occur spontaneously when reducing available O₂ to simulate hypoxic SD, or by elevating extracellular K⁺. Preliminary results suggest that susceptibility to SD varied with layer, with layer IV being the most vulnerable and layer II/III the most resilient. Network connectivity did not directly relate to a neuron’s vulnerability to SD, but those that fired at higher frequencies were more vulnerable.

Discussion

This model explores the roles of network connectivity, neuronal density, neuronal activity, and heterogeneity of O₂ supply that affect the susceptibility of SD of individual neurons and cortical layers. Our model also suggests that the distribution of capillaries affects neurons' ability to maintain homeostasis and physiological firing. Neurons closer to capillaries are better able to sustain their activity when O₂ is reduced.


References
1. Kelley, C., Newton, A. J. H., Hrabetova, S., McDougal, R. A., & Lytton, W. W. (2022). Multiscale Computer Modeling of Spreading Depolarization in Brain Slices. eNeuro, 9(4). https://doi.org/10.1523/ENEURO.0082-22.2022
2. Potjans, T. C., & Diesmann, M. (2012). The Cell-Type Specific Cortical Microcircuit: Relating Structure and Activity in a Full-Scale Spiking Network Model. Cerebral Cortex , 24(3), 785–806. https://doi.org/10.1093/cercor/bhs358
3. Romaro, C., Najman, F. A., Lytton, W. W., Roque, A. C., & Dura-Bernal, S. (2021). NetPyNE Implementation and Scaling of the Potjans-Diesmann Cortical Microcircuit Model. Neural Computation, 33(7), 1993–2032. https://doi.org/10.1162/neco_a_01400





Acknowledgement
This research was funded by the National Institute of Mental Health, National Institutes of Health, grant number R01 MH086638, with HPC time from NIH S10 award, 1S10OD032417-01, and the Yale Center for Research Computing McClearly cluster. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.