Low-Dimensional Models of “Neuro-Glio-Vascular Unit” for Describing Neural Dynamics under Normal and Energy-Starved Conditions

et al. 2019

Preprint

10The neurovascular coupling is mostly considered as a master-slave relationship between 11 the neurons and the cerebral vessels: the neurons demand energy which the vessels supply in 12 the form of glucose and oxygen. In the recent past both theoretical and experimental studies 13 have suggested that the neurovascular coupling is a bidirectional system, a loop that includes a 14 feedback signal from the vessels influencing neural firing and plasticity. An integrated model 15 of bidirectionally connected neural network and vascular network is hence required to 16 understand the relationship between the informational and metabolic aspects of neural 17 dynamics . In this study, we present a computational model of the bidirectional neurovascular 18 system in the whisker barrel cortex and study the effect of such coupling on neural activity and 19 plasticity as manifest in the map formation. In this model, a biologically plausible self-20 organizing network model of rate coded, dynamic neurons is nourished by a network of vessels 21 modeled using the biophysical properties of blood vessels. The neural layer which is designed 22 to simulate the whisker barrel cortex of rat transmits the vasodilatory signals to the vessels. 23The feedback from the vessels is in the form of available oxygen for oxidative metabolism 24 whose end result is the ATP necessary to fuel neural firing. The model captures the effect of 25 the feedback from the vascular network on the neuronal map formation in the whisker barrel 26 model under normal and pathological (Hypoxia and Hypoxia-Ischemia) conditions.27 28 29 30 Author Summary 31 32Although neurovascular coupling is typically depicted as a unidirectional influence 33 originating from the neurons and acting on the cerebral vessels, in reality it forms a 34 bidirectional system, consisting of neuronal energy demand signals transmitted to the vessels, 35 and a feedback of metabolic substrates from the vessels, that influence the neural firing and 36 plasticity. We present a computational model of the bidirectional neurovascular coupling in the 37 whisker barrel cortex of rats and study the effect of such coupling on neural activity and 38 plasticity as manifest in the map formation. The model consists of a biologically plausible self-39 organizing dynamic, neural network model and a biophysical vascular network model that 40 nourishes the neural network in the form of oxygen necessary for neural oxidative metabolism. 41The model reproduces the spatio-temporal hemodynamic responses observed in rat whisker 42 barrel cortex. It also demonstrates the essentiality of the vascular feedback on the whisker 43 barrel map formation under normal and pathological conditions. al(9), desynchronized vascular rhythms were found to improve the efficiency of 106 learning in a neural network trained as an autoencoder. Among the aforementioned set of 107 models, there are either detailed biophysical models at the single unit level or abstract models 108 at a network level. There is a need to create detail...

Section: Resultssupporting

confidence: 93%

Section: Resultssupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

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A Network Architecture for Bidirectional Neurovascular Coupling in Rat Whisker Barrel Cortex

Kumar

Khot²,

et al. 2019

Preprint

“…With the help of computational models of neurovascular coupling, our group had earlier explored the effect of rhythms of energy delivery from the cerebrovascular system on neural function (Gandrakota et al, 2010;Chander and Chakravarthy, 2012;Chhabria and Chakravarthy, 2016;Philips et al, 2016). Recently, we proposed a preliminary computational spiking network model of STN-mediated excitotoxicity in SNc with a slightly abstract treatment of apoptosis (Muddapu and Chakravarthy, 2017).…”

Section: Energy Basis Of Neurodegenerationmentioning

confidence: 99%

A computational model of loss of dopaminergic cells in Parkinson’s disease due to glutamate-induced excitotoxicity

Muddapu

Mandali

et al. 2018

Preprint

Parkinson's disease (PD) is a neurodegenerative disease associated with progressive and inexorable loss of dopaminergic cells in Substantia Nigra pars compacta (SNc). A full understanding of the underlying pathogenesis of this cell loss is unavailable, though a number of mechanisms have been indicated in the literature. A couple of these mechanisms, however, show potential for the development of radical and promising PD therapeutics. One of these mechanisms is the peculiar metabolic vulnerability of SNc cells by virtue of their excessive energy demands; the other is the excitotoxicity caused by excessive glutamate release onto SNc by an overactive Subthalamic Nucleus (STN). To investigate the latter hypothesis computationally, we developed a spiking neuron network model of the SNc-STN-GPe system. In the model, prolonged stimulation of SNc cells by an overactive STN leads to an increase in a 'stress' variable; when the stress in a SNc neuron exceeds a stress threshold the neuron dies. The model shows that the interaction between SNc and STN involves a positive feedback due to which, an initial loss of SNc cells that crosses a threshold causes a runaway effect that leads to an inexorable loss of SNc cells, strongly resembling the process of neurodegeneration. The model further suggests a link between the two aforementioned PD mechanisms: metabolic vulnerability and glutamate excitotoxicity. Our simulation results show that the excitotoxic cause of SNc cell loss in PD might be initiated by weak excitotoxicity mediated by energy deficit, followed by strong excitotoxicity, mediated by a disinhibited STN. A variety of conventional therapies are simulated in the model to test their efficacy in slowing down or arresting SNc cell loss. Among the current therapeutics,

“…From our previous computational studies (Chander and Chakravarthy, 2012;Chhabria and Chakravarthy, 2016), we were able to show that cortical neurons at lower ATP levels exhibit a bursting type of firing patterns. In the present model, we are able to show that SNc neurons exhibit a bursting type of firing pattern under low glucose and oxygen levels ( Figure 3B).…”

Section: Discussionmentioning

confidence: 95%

A Multi-Scale Computational Model of Excitotoxic Loss of Dopaminergic Cells in Parkinson’s Disease

Muddapu

2020

Preprint

Self Cite

Parkinson's disease (PD) is a neurodegenerative disorder caused by loss of dopaminergic neurons in Substantia Nigra pars compacta (SNc). Although the exact cause of the cell death is not clear, the hypothesis that metabolic deficiency is a key facor has been gaining attention in the recent years. In the present study, we investigate this hypothesis using a multi-scale computational model of the subsystem of the basal ganglia comprising Subthalamic Nucleus (STN), Globus Pallidus externa (GPe) and SNc. The model is a multiscale model in that interactions among the three nuclei are simulated using more abstract Izhikevich neuron models, while the molecular pathways involved in cell death of SNc neurons are simulated in terms of detailed chemical kinetics. Simulation results obtained from the proposed model showed that energy deficiencies occurring at cellular and network levels could precipitate the excitotoxic loss of SNc neurons in PD. At the subcellular level, the models show how calcium elevation leads to apoptosis of SNc neurons. The therapeutic effects of several neuroprotective interventions are also simulated in the model. From neuroprotective studies, it was clear that glutamate inhibition and apoptotic signal blocker therapies were able to halt the progression of SNc cell loss when compared to other therapeutic interventions, which only slows down the progression of SNc cell loss. Deficiency Izhikevich (Spiking) Neuronal Model (STN, GPe) The Izhikevich neuronal models are capable of exhibiting biologically realistic firing patterns, at a relatively low computational expense (Izhikevich, 2003). The proposed model of HEM consists of GPe and STN neurons are modeled as Izhikevich spiking neuron model, where the Izhikevich parameters are adopted from the literature (Mandali et al., 2015;Michmizos and Nikita, 2011). Based on the anatomical data of rat basal ganglia, the neuronal population sizes in the model are selected (Arbuthnott and Wickens, 2007;Oorschot, 1996).The external bias current ( ) was adjusted to match the firing rate of nuclei with published data (Tripathy et al., 2015).