A computational study on the role of glutamate and NMDA receptors on cortical spreading depression using a multidomain electrodiffusion model

Abstract

Cortical spreading depression (SD) is a spreading disruption of ionic homeostasis in the brain during which neurons experience complete and prolonged depolarizations. SD is the basis of migraine aura and is increasingly associated with many other brain pathologies. Here, we study the role of glutamate and NMDA receptor dynamics in the context of an ionic electrodiffusion model. We perform simulations in one (1D) and two (2D) spatial dimension. Our 1D simulations reproduce the “inverted saddle” shape of the extracellular voltage signal for the first time. Our simulations suggest that SD propagation depends on two overlapping mechanisms; one dependent on extracellular glutamate diffusion and NMDA receptors and the other dependent on extracellular potassium diffusion and persistent sodium channel conductance. In 2D simulations, we study the dynamics of spiral waves. We study the properties of the spiral waves in relation to the planar 1D wave, and also compute the energy expenditure associated with the recurrent SD spirals.

An electrodiffusive neuron-extracellular-glia model with somatodendritic interactions

“…The edNEG model was highly inspired by the previous tri-domain continuum model by Tuttle et al 2019 [27], which is the most advanced of the spatial SD models. It includes neurons, glia, and extracellular space (Fig 7A), and it accounts for cellular swelling, a number of (slow) membrane mechanisms, and electrodiffusive ion concentration dynamics.…”

“…It includes neurons, glia, and extracellular space (Fig 7A), and it accounts for cellular swelling, a number of (slow) membrane mechanisms, and electrodiffusive ion concentration dynamics. Unlike other local models of ion concentration dynamics in tissue (see, e.g., [13,44,60,71]), the edNEG model was based on the same kind of electrodiffusive formalism as the model by Tuttle et al 2019 [27], and should in that regard be compatible with the tri-domain continuum framework used there ( Fig 7A).…”

“…On the much larger spatial scale of brain tissue, spreading depression (SD) and a number of related pathological conditions are associated with dramatic shifts in the K + concentration and giant DC-like voltage gradients in the ECS, which may be as large as several tens of millimolar and millivolts, respectively [19][20][21][22][23][24]. The pathophysiology of SD is believed to largely depend on the dynamics of extracellular K + [20,23,25,26], which in turn is likely to involve numerous processes such as neuronal re-uptake, electrodiffusion through the ECS, and glial spatial buffering processes [27].…”

An electrodiffusive, ion conserving Pinsky-Rinzel model with homeostatic mechanisms

“…A particular context in which we anticipate the edPR model to be 507 useful is that of simulating spreading depression. Previous spatial, electrodiffusive, and 508 biophysically consistent models of spreading depression have targeted the problem at a 509 large-scale tissue-level, using a mean-field approach [30,77,78]. These models were 510 inspired by the bi-domain model [79], which has been successfully applied in simulations 511 of cardiac tissue [80,81].…”

“…In this 522 context, the edPR model could enter as a module in a, let us say, bi-times-two-domain 523 model, where each point in (xy) space contains a set of (i) somatic intracellular 524 variables, (ii) somatic extracellular variables, (iii) dendritic intracellular variables and 525 (iv) dendritic extracellular variables, and thus accounts for the differences between the 526 higher and lower layers. We should note that the state of the art models of spreading 527 depression are not bi-domain models but rather tri-domain models, as they also include 528 a glial domain to account especially for the work done by astrocytes in K + 529 buffering [30,77,78]. Hence, to use the edPR model to expand the current spreading 530 depression models, a natural first step would be to include a glial (astrocytic) 531 compartment in it, so that it eventually could be implemented as a 532 tri-times-two-domain model.…”