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Multiple sclerosis is a disease caused by demyelination of nerve fibers. In order to determine the loss of signal with the percentage of demyelination, we need to develop models that can simulate this effect. Existing time-based models does not provide a method to determine the influences of demyelination based on simulation results. Our goal is to...
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Background
Multiple sclerosis (MS) is an autoimmune, neuroinflammatory disease, with an unclear etiology. However, T cells play a central role in the pathogenesis by crossing the blood–brain-barrier, leading to inflammation of the central nervous system and demyelination of the protective sheath surrounding the nerve fibers. MS has a complex inheri...
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... So far, there exist no computational models that shape the effects of demyelination at the level of networks and connectomes. Most modeling research of multiple sclerosis is performed at the cellular level [71,137,138,[140][141][142][143][144], statistically [145] or in MRI studies [146,147]. We have mentioned above the comparison and application of changes in connection weights with the courses of neurological deficits of CIS of multiple sclerosis. ...
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.
... In the case of seizures, the understanding of the drug-induced firing response may allow further analysis on the impact of high-frequency firing on the neural tissue as well as how to desynchronize or slow it down. Frequency-domain analysis has been performed on top of linear models of the Hodgkin-Huxley (HH) formalism to investigate not only the transmission of information through the use of subthreshold electrical stimulation (Khodaei and Pierobon, 2016) but also the influence of axonal demyelination on the propagation of action potentials (Chaubey and Goodwin, 2016). Although Hodgkin-Huxley is not the only neuron model available in the literature, it is one of the most plausible models for computational neuroscience (Long and Fang, 2010). ...
High-frequency firing activity can be induced either naturally in a healthy brain as a result of the processing of sensory stimuli or as an uncontrolled synchronous activity characterizing epileptic seizures. As part of this work, we investigate how logic circuits that are engineered in neurons can be used to design spike filters, attenuating high-frequency activity in a neuronal network that can be used to minimize the effects of neurodegenerative disorders such as epilepsy. We propose a reconfigurable filter design built from small neuronal networks that behave as digital logic circuits. We developed a mathematical framework to obtain a transfer function derived from a linearization process of the Hodgkin-Huxley model. Our results suggest that individual gates working as the output of the logic circuits can be used as a reconfigurable filtering technique. Also, as part of the analysis, the analytical model showed similar levels of attenuation in the frequency domain when compared to computational simulations by fine-tuning the synaptic weight. The proposed approach can potentially lead to precise and tunable treatments for neurological conditions that are inspired by communication theory.
... MS is the disease which results primarily from demyelination of axons. My recent paper (Chaubey and Goodwin, 2016) is a computational modeling study which makes it possible to make quantitative predictions about the degree to which electrical signals will move more slowly through axonal pathways as a function of how much of their myelin sheath they have lost as a result of MS (using data from diffusion tensor imaging (DTI), and other modalities). The paper identifies a new biomarker for network failure in MS that should improve our ability to predict and track loss of sensory, motor and cognitive function in the disease and a better way to measure the efficacy of new treatments. ...
Multiple Sclerosis (MS) is a major cause of neurological disability in adults and has an annual cost of approximately $28 billion in the United States. MS is a very complex disorder as demyelination can happen in a variety of locations throughout the brain; therefore, this disease is never the same in two patients making it very hard to predict disease progression. A modeling approach which combines clinical, biological and imaging measures to help treat and fight this disorder is needed. In this paper, I will outline MS as a very heterogeneous disorder, review some potential solutions from the literature, demonstrate the need for a biomarker and will discuss how computational modeling combined with biological, clinical and imaging data can help link disparate observations and decipher complex mechanisms whose solutions are not amenable to simple reductionism.
... Therefore, the length value corresponding to the threshold potential = 0.60774 = 2.725 • 0 is the maximum transmission length. It is very important to be able to estimate this parameter, as it has been shown that the Ranvier internodal distance (the length of the Schwann cells) and the number of Ranvier nodes influence the transmission velocity of the neuronal signals [26], [27]. ...
... These parameters play an important role in the modelling of saltatory conduction of the neuronal signals in the myelinated axons, between Ranvier nodes. Study on axonal conduction, even with linear models is of great importance for the treatment of degenerative pathologies such as multiple sclerosis which is a disease caused by demyelination of nerve fibers [26]. The results reported in our paper therefore establish the basis for more complex tasks, such as considering nonlinear parameters and two-and threedimensional spatial models, by modelling the extracellular media. ...
Starting from the Electro-Quasi-Static field equations a new 1D-RC transmission line model of axons is extracted. Based on this bio-physical model mathematical and numerical models are proposed and used to compute the main transmission parameters such as: neural signal attenuation, maximal transmission length (the admissible distance between Ranvier nodes = length of Schwann cells) and the nerve conduction velocity. It is predicted the dependence of this velocity versus the axon diameter, in both myelinated and unmyelinated cases, matching the results of measurements presented in literature.
In light of extensive work that has created a wide range of techniques for predicting the course of multiple sclerosis (MS) disease, this paper attempts to provide an overview of these approaches and put forth an alternative way to predict the disease progression. For this purpose, the existing methods for estimating and predicting the course of the disease have been categorized into clinical, radiological, biological, and computational or artificial intelligence-based markers. Weighing the weaknesses and strengths of these prognostic groups is a profound method that is yet in need and works directly at the level of diseased connectivity. Therefore, we propose using the computational models in combination with established connectomes as a predictive tool for MS disease trajectories. The fundamental conduction-based Hodgkin-Huxley model emerged as promising from examining these studies. The advantage of the Hodgkin-Huxley model is that certain properties of connectomes, such as neuronal connection weights, spatial distances, and adjustments of signal transmission rates, can be taken into account. It is precisely these properties that are particularly altered in MS and that have strong implications for processing, transmission, and interactions of neuronal signaling patterns.
The Hodgkin-Huxley (HH) equations as a point-neuron model are used for signal propagation inside a small network. The objective is to change the conduction parameter of the neuron model, replicate the changes in myelin properties in MS and observe the dynamics of the signal propagation across the network. The model is initially validated for different lengths, conduction values, and connection weights through three nodal connections. Later, these individual factors are incorporated into a small network and simulated to mimic the condition of MS. The signal propagation pattern is observed after inducing changes in conduction parameters at certain nodes in the network and compared against a control model pattern obtained before the changes are applied to the network. The signal propagation pattern varies as expected by adapting to the input conditions. Similarly, when the model is applied to a connectome, the pattern changes could give an insight into disease progression. This approach has opened up a new path to explore the progression of the disease in MS. The work is in its preliminary state, but with a future vision to apply this method in a connectome, providing a better clinical tool.
The 2021 Nobel Prize in Physiology or Medicine was recently awarded to David Julius and Ardem Patapoutian “for their discoveries of receptors for temperature and touch”. It is well established that touch receptors (PIEZO) provide sensory inputs to inform the brain about objects in our environment. However, exactly how the transient ion transport activity of touch receptors could stimulate action potential firing seems still not entirely clear. In this article, the latest transmembrane-electrostatically localized protons/cations charges (TELC) theory is employed to better understand neural stimulation and action potential that we have recently identified as the voltage contributed by TELC at a neural liquid-membrane interface in a neural cell. The TELC density at the resting membrane potential of −70 mV is now calculated to be 3900 (protons + cations) per μm2 on extracellular membrane surface. At the stimulation threshold level (−55 mV), the TELC density is calculated to be 3100 (protons + cations) per μm2. Accordingly, the neural stimulation by touch can now be better understood by analyzing PIEZO ion conduction and TELC activity. The response time from PIEZO channel ion conduction activities to reduce the TELC density to the stimulation level of 3100 TELC per μm2 for action potential firing was calculated for the first time. The activities of a single or a few PIEZO channels may be sufficient to generate a “graded potential” to trigger an action potential spike firing. With a high number (200~300) of PIEZO channels activated by touch, it can generate the required “graded potential” to reach the stimulation threshold level (−55 mV) within 0.3 ms. Real-time action potential ( V t ) with PIEZO mechanically-activated stimulation by touch is now, mathematically explained through a novel integral equation ( V t =-1/ C ∫ 0 t I ( t ) dt + V 0 ) of the net time-dependent transmembrane ion current I ( t ), which is fundamentally important to neuroscience.
Employing the transmembrane electrostatic proton localization theory with a new membrane potential equation, neural resting and action potential is now much better understood as the voltage contributed by the localized protons/cations at a neural liquid- membrane interface. Accordingly, the neural resting/action potential is essentially a protonic/cationic membrane capacitor behavior. It is now understood with a newly formulated action potential equation: when action potential is below zero (negative number), the localized protons/cations charge density at the liquid-membrane interface along the periplasmic side are above zero (positive number); when the action potential is above zero, the concentration of the localized protons and localized non-proton cations is below zero, indicating a "depolarization" state. The nonlinear curve of the localized protons/cations charge density in the real-time domain of an action potential spike appears as an inverse mirror image to the action potential. The newly formulated action potential equation provides biophysical insights for neuron electrophysiology, which may represent a complementary development to the classic Goldman-Hodgkin-Katz equation. Using the action potential equation, the biological significance of axon myelination is now also elucidated as to provide protonic insulation and prevent any ions both inside and outside the neuron from interfering with the action potential signal, so that the action potential can quickly propagate along the axon with minimal (e.g., 40-times less) energy requirement.
