Abstract
Development of credible clinically-relevant brain simulations has been slowed due to a focus on electrophysiology in computational neuroscience, neglecting the multiscale whole-tissue modeling approach used for simulation in most other organ systems. We have now begun to extend the NEURON simulation platform in this direction by adding extracellular modeling. The extracellular medium of neural tissue is an active medium of neuromodulators, ions, inflammatory cells, oxygen, NO and other gases, with additional physiological, pharmacological and pathological agents. These extracellular agents influence, and are influenced by, cellular electrophysiology, and cellular chemophysiology—the complex internal cellular milieu of second-messenger signaling and cascades. NEURON's extracellular reaction-diffusion is supported by an intuitive Python-based where/who/what command sequence, derived from that used for intracellular reaction diffusion, to support coarse-grained macroscopic extracellular models. This simulation specification separates the expression of the conceptual model and parameters from the underlying numerical methods. In the volume-averaging approach used, the macroscopic model of tissue is characterized by free volume fraction—the proportion of space in which species are able to diffuse, and tortuosity—the average increase in path length due to obstacles. These tissue characteristics can be defined within particular spatial regions, enabling the modeler to account for regional differences, due either to intrinsic organization, particularly gray vs. white matter, or to pathology such as edema. We illustrate simulation development using spreading depression, a pathological phenomenon thought to play roles in migraine, epilepsy and stroke. Simulation results were verified against analytic results and against the extracellular portion of the simulation run under FiPy. The creation of this NEURON interface provides a pathway for interoperability that can be used to automatically export this class of models into complex intracellular/extracellular simulations and future cross-simulator standardization.
Highlights
The original rxd package expanded multiscale modeling in NEURON from the electrophysiological scales of neurites, cells and networks into chemophysiological scales of spines, subcellular organelles, interactomics, metabolomics, proteomics. This further development of the module into the domain of extracellular space considerably extends the scope of chemophysiology into the vast distances of interneuronal space
Computational performance for this large-scale problem is improved by the use of multi-threading parallelization of DGADI algorithm for diffusion, multiprocessor parallelization for electrophysiology, and JIT compilation of reactions
Large neural simulations have typically been neuronal networks which focus exclusively on the electrical activity of neurons and their mutual influence via chemical and electrical synapses. Such neuronal network simulations have effectively operated in a vacuum, omitting the effects of nonsynaptic neuromodulators, neuromodulatory gases, ions, glia, metabolites, etc
Summary
Computational neuroscience has had an historical focus on electrophysiology, with consequent neglect of the accompanying chemophysiology that directly underlies neural function, and of the brain as a complex organ within which neuronal networks are embedded (De Schutter, 2008) This neglect is of particular importance as we try to adapt our models for understanding of brain disease, many of which are associated with changes in extracellular concentrations of ions, metabolites, transmitters, or toxins in various parts of the brain (Mulugeta et al, 2018). As with cells of other solid organs, neurons exist in a highly active medium, influenced by bioactive chemicals whose concentrations change rapidly through: (1) passive diffusion, (2) active deposit and clearance from other cells, and (3) binding or other reactions with extracellular species (Syková and Nicholson, 2008) These important tissue-level chemophysiological influences have been neglected by computational neuroscience for a variety of reasons, including the aforementioned focus on electrophysiology. The total amount of a substance of interest will be conserved within the simulation, despite moving in and out of subcellular compartments, or in and out of cells, via currents, active transport, or vesicular release
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