An electrodiffusive neuron-extracellular-glia model for exploring the genesis of slow potentials in the brain

Uncertainty quantification and sensitivity analysis of neuron models with ion concentration dynamics

This paper provides a comprehensive and computationally efficient case study for uncertainty quantification (UQ) and global sensitivity analysis (GSA) in a neuron model incorporating ion concentration dynamics. We address how challenges with UQ and GSA in this context can be approached and solved, including challenges related to computational cost, parameters affecting the system's resting state, and the presence of both fast and slow dynamics. Specifically, we analyze the electrodiffusive neuron-extracellular-glia (edNEG) model, which captures electrical potentials, ion concentrations (Na+, K+, Ca2+, and Cl-), and volume changes across six compartments. Our methodology includes a UQ procedure assessing the model's reliability and susceptibility to input uncertainty and a variance-based GSA identifying the most influential input parameters. To mitigate computational costs, we employ surrogate modeling techniques, optimized using efficient numerical integration methods. We propose a strategy for isolating parameters affecting the resting state and analyze the edNEG model dynamics under both physiological and pathological conditions. The influence of uncertain parameters on model outputs, particularly during spiking dynamics, is systematically explored. Rapid dynamics of membrane potentials necessitate a focus on informative spiking features, while slower variations in ion concentrations allow a meaningful study at each time point. Our study offers valuable guidelines for future UQ and GSA investigations on neuron models with ion concentration dynamics, contributing to the broader application of such models in computational neuroscience.

Physiological paradigm for assessing reward prediction and extinction using cortical direct current potential responses in rats

Anticipating positive outcomes is a core cognitive function in the process of reward prediction. However, no neurophysiological method objectively assesses reward prediction in basic medical research. In the present study, we established a physiological paradigm using cortical direct current (DC) potential responses in rats to assess reward prediction. This paradigm consisted of five daily 1-h sessions with two tones, wherein the rewarded tone was followed by electrical stimulation of the medial forebrain bundle (MFB) scheduled at 1000 ms later, whereas the unrewarded tone was not. On day 1, both tones induced a negative DC shift immediately after auditory responses, persisting up to MFB stimulation. This negative shift progressively increased and peaked on day 4. Starting from day 3, the negative shift from 600 to 1000 ms was significantly larger following the rewarded tone than that following the unrewarded tone. This negative DC shift was particularly prominent in the frontal cortex, suggesting its crucial role in discriminative reward prediction. During the extinction sessions, the shift diminished significantly on extinction day 1. These findings suggest that cortical DC potential is related to reward prediction and could be a valuable tool for evaluating animal models of depression, providing a testing system for anhedonia.

Computational modeling of neuron-glia signaling interactions to unravel cellular and neural circuit functioning

Glial cells have been shown to be vital for various brain functions, including homeostasis, information processing, and cognition. Over the past 30 years, various signaling interactions between neuronal and glial cells have been shown to underlie these functions. This review summarizes the interactions, particularly between neurons and astrocytes, which are types of glial cells. Some of the interactions remain controversial in part due to the nature of experimental methods and preparations used. Based on the accumulated data, computational models of the neuron-astrocyte interactions have been developed to explain the complex functions of astrocytes in neural circuits and to test conflicting hypotheses. This review presents the most significant recent models, modeling methods and simulation tools for neuron-astrocyte interactions. In the future, we will especially need more experimental research on awake animals in vivo and new computational models of neuron-glia interactions to advance our understanding of cellular dynamics and the functioning of neural circuits in different brain regions.

Finite-element modeling of neuromodulation via controlled delivery of potassium ions using conductive polymer-coated microelectrodes

Objective. The controlled delivery of potassium is an interesting neuromodulation modality, being potassium ions involved in shaping neuron excitability, synaptic transmission, network synchronization, and playing a key role in pathological conditions like epilepsy and spreading depression. Despite many successful examples of pre-clinical devices able to influence the extracellular potassium concentration, computational frameworks capturing the corresponding impact on neuronal activity are still missing.Approach. We present a finite-element model describing a PEDOT:PSS-coated microelectrode (herein, simplyionic actuator) able to release potassium and thus modulate the activity of a cortical neuron in anin-vitro-like setting. The dynamics of ions in the ionic actuator, the neural membrane, and the cellular fluids are solved self-consistently.Main results. We showcase the capability of the model to describe on a physical basis the modulation of the intrinsic excitability of the cell and of the synaptic transmission following the electro-ionic stimulation produced by the actuator. We consider three case studies for the ionic actuator with different levels of selectivity to potassium: ideal selectivity, no selectivity, and selectivity achieved by embedding ionophores in the polymer.Significance. This work is the first step toward a comprehensive computational framework aimed to investigate novel neuromodulation devices targeting specific ionic species, as well as to optimize their design and performance, in terms of the induced modulation of neural activity.

Stretch-activated ion channel TMEM63B associates with developmental and epileptic encephalopathies and progressive neurodegeneration

By converting physical forces into electrical signals or triggering intracellular cascades, stretch-activated ion channels allow the cell to respond to osmotic and mechanical stress. Knowledge of the pathophysiological mechanisms underlying associations of stretch-activated ion channels with human disease is limited. Here, we describe 17 unrelated individuals with severe early-onset developmental and epileptic encephalopathy (DEE), intellectual disability, and severe motor and cortical visual impairment associated with progressive neurodegenerative brain changes carrying ten distinct heterozygous variants of TMEM63B, encoding for a highly conserved stretch-activated ion channel. The variants occurred de novo in 16/17 individuals for whom parental DNA was available and either missense, including the recurrent p.Val44Met in 7/17 individuals, or in-frame, all affecting conserved residues located in transmembrane regions of the protein. In 12 individuals, hematological abnormalities co-occurred, such as macrocytosis and hemolysis, requiring blood transfusions in some. We modeled six variants (p.Val44Met, p.Arg433His, p.Thr481Asn, p.Gly580Ser, p.Arg660Thr, and p.Phe697Leu), each affecting a distinct transmembrane domain of the channel, in transfected Neuro2a cells and demonstrated inward leak cation currents across the mutated channel even in isotonic conditions, while the response to hypo-osmotic challenge was impaired, as were the Ca2+ transients generated under hypo-osmotic stimulation. Ectopic expression of the p.Val44Met and p.Gly580Cys variants in Drosophila resulted in early death. TMEM63B-associated DEE represents a recognizable clinicopathological entity in which altered cation conductivity results in a severe neurological phenotype with progressive brain damage and early-onset epilepsy associated with hematological abnormalities in most individuals.

Temporal dynamics of Na/K pump mediated memory traces: insights from conductance-based models of Drosophila neurons

Sodium potassium ATPases (Na/K pumps) mediate long-lasting, dynamic cellular memories that can last tens of seconds. The mechanisms controlling the dynamics of this type of cellular memory are not well understood and can be counterintuitive. Here, we use computational modeling to examine how Na/K pumps and the ion concentration dynamics they influence shape cellular excitability. In a Drosophila larval motor neuron model, we incorporate a Na/K pump, a dynamic intracellular Na⁺ concentration, and a dynamic Na⁺ reversal potential. We probe neuronal excitability with a variety of stimuli, including step currents, ramp currents, and zap currents, then monitor the sub- and suprathreshold voltage responses on a range of time scales. We find that the interactions of a Na⁺-dependent pump current with a dynamic Na⁺ concentration and reversal potential endow the neuron with rich response properties that are absent when the role of the pump is reduced to the maintenance of constant ion concentration gradients. In particular, these dynamic pump-Na⁺ interactions contribute to spike rate adaptation and result in long-lasting excitability changes after spiking and even after sub-threshold voltage fluctuations on multiple time scales. We further show that modulation of pump properties can profoundly alter a neuron's spontaneous activity and response to stimuli by providing a mechanism for bursting oscillations. Our work has implications for experimental studies and computational modeling of the role of Na/K pumps in neuronal activity, information processing in neural circuits, and the neural control of animal behavior.

Advances in the Electrophysiological Recordings of Long-Term Potentiation

Understanding neuronal firing patterns and long-term potentiation (LTP) induction in studying learning, memory, and neurological diseases is critical. However, recently, despite the rapid advancement in neuroscience, we are still constrained by the experimental design, detection tools for exploring the mechanisms and pathways involved in LTP induction, and detection ability of neuronal action potentiation signals. This review will reiterate LTP-related electrophysiological recordings in the mammalian brain for nearly 50 years and explain how excitatory and inhibitory neural LTP results have been detected and described by field- and single-cell potentials, respectively. Furthermore, we focus on describing the classic model of LTP of inhibition and discuss the inhibitory neuron activity when excitatory neurons are activated to induce LTP. Finally, we propose recording excitatory and inhibitory neurons under the same experimental conditions by combining various electrophysiological technologies and novel design suggestions for future research. We discussed different types of synaptic plasticity, and the potential of astrocytes to induce LTP also deserves to be explored in the future.

Analysis of Network Models with Neuron-Astrocyte Interactions

Neural networks, composed of many neurons and governed by complex interactions between them, are a widely accepted formalism for modeling and exploring global dynamics and emergent properties in brain systems. In the past decades, experimental evidence of computationally relevant neuron-astrocyte interactions, as well as the astrocytic modulation of global neural dynamics, have accumulated. These findings motivated advances in computational glioscience and inspired several models integrating mechanisms of neuron-astrocyte interactions into the standard neural network formalism. These models were developed to study, for example, synchronization, information transfer, synaptic plasticity, and hyperexcitability, as well as classification tasks and hardware implementations. We here focus on network models of at least two neurons interacting bidirectionally with at least two astrocytes that include explicitly modeled astrocytic calcium dynamics. In this study, we analyze the evolution of these models and the biophysical, biochemical, cellular, and network mechanisms used to construct them. Based on our analysis, we propose how to systematically describe and categorize interaction schemes between cells in neuron-astrocyte networks. We additionally study the models in view of the existing experimental data and present future perspectives. Our analysis is an important first step towards understanding astrocytic contribution to brain functions. However, more advances are needed to collect comprehensive data about astrocyte morphology and physiology in vivo and to better integrate them in data-driven computational models. Broadening the discussion about theoretical approaches and expanding the computational tools is necessary to better understand astrocytes' roles in brain functions.

Lactate supply overtakes glucose when neural computational and cognitive loads scale up

Neural computational power is determined by neuroenergetics, but how and which energy substrates are allocated to various forms of memory engram is unclear. To solve this question, we asked whether neuronal fueling by glucose or lactate scales differently upon increasing neural computation and cognitive loads. Here, using electrophysiology, two-photon imaging, cognitive tasks, and mathematical modeling, we show that both glucose and lactate are involved in engram formation, with lactate supporting long-term synaptic plasticity evoked by high-stimulation load activity patterns and high attentional load in cognitive tasks and glucose being sufficient for less demanding neural computation and learning tasks. Indeed, we show that lactate is mandatory for demanding neural computation, such as theta-burst stimulation, while glucose is sufficient for lighter forms of activity-dependent long-term potentiation (LTP), such as spike timing-dependent plasticity (STDP). We find that subtle variations of spike number or frequency in STDP are sufficient to shift the on-demand fueling from glucose to lactate. Finally, we demonstrate that lactate is necessary for a cognitive task requiring high attentional load, such as the object-in-place task, and for the corresponding in vivo hippocampal LTP expression but is not needed for a less demanding task, such as a simple novel object recognition. Overall, these results demonstrate that glucose and lactate metabolism are differentially engaged in neuronal fueling depending on the complexity of the activity-dependent plasticity and behavior.

Reaction-diffusion models in weighted and directed connectomes

Connectomes represent comprehensive descriptions of neural connections in a nervous system to better understand and model central brain function and peripheral processing of afferent and efferent neural signals. Connectomes can be considered as a distinctive and necessary structural component alongside glial, vascular, neurochemical, and metabolic networks of the nervous systems of higher organisms that are required for the control of body functions and interaction with the environment. They are carriers of functional epiphenomena such as planning behavior and cognition, which are based on the processing of highly dynamic neural signaling patterns. In this study, we examine more detailed connectomes with edge weighting and orientation properties, in which reciprocal neuronal connections are also considered. Diffusion processes are a further necessary condition for generating dynamic bioelectric patterns in connectomes. Based on our high-precision connectome data, we investigate different diffusion-reaction models to study the propagation of dynamic concentration patterns in control and lesioned connectomes. Therefore, differential equations for modeling diffusion were combined with well-known reaction terms to allow the use of connection weights, connectivity orientation and spatial distances.

Three reaction-diffusion systems Gray-Scott, Gierer-Meinhardt and Mimura-Murray were investigated. For this purpose, implicit solvers were implemented in a numerically stable reaction-diffusion system within the framework of neuroVIISAS. The implemented reaction-diffusion systems were applied to a subconnectome which shapes the mechanosensitive pathway that is strongly affected in the multiple sclerosis demyelination disease. It was found that demyelination modeling by connectivity weight modulation changes the oscillations of the target region, i.e. the primary somatosensory cortex, of the mechanosensitive pathway.

In conclusion, a new application of reaction-diffusion systems to weighted and directed connectomes has been realized. Because the implementation were performed in the neuroVIISAS framework many possibilities for the study of dynamic reaction-diffusion processes in empirical connectomes as well as specific randomized network models are available now.

Superconducting Bio-Inspired Au-Nanowire-Based Neurons

High-performance modeling of neurophysiological processes is an urgent task that requires new approaches to information processing. In this context, two- and three-junction superconducting quantum interferometers with Josephson weak links based on gold nanowires are fabricated and investigated experimentally. The studied cells are proposed for the implementation of bio-inspired neurons—high-performance, energy-efficient, and compact elements of neuromorphic processor. The operation modes of an advanced artificial neuron capable of generating the burst firing activation patterns are explored theoretically. A comparison with the Izhikevich mathematical model of biological neurons is carried out.

Validating a Computational Framework for Ionic Electrodiffusion with Cortical Spreading Depression as a Case Study

Cortical spreading depression (CSD) is a wave of pronounced depolarization of brain tissue accompanied by substantial shifts in ionic concentrations and cellular swelling. Here, we validate a computational framework for modeling electrical potentials, ionic movement, and cellular swelling in brain tissue during CSD. We consider different model variations representing wild-type (WT) or knock-out/knock-down mice and systematically compare the numerical results with reports from a selection of experimental studies. We find that the data for several CSD hallmarks obtained computationally, including wave propagation speed, direct current shift duration, peak in extracellular K⁺ concentration as well as a pronounced shrinkage of extracellular space (ECS) are well in line with what has previously been observed experimentally. Further, we assess how key model parameters including cellular diffusivity, structural ratios, membrane water and/or K⁺ permeabilities affect the set of CSD characteristics.