Biology subjects

Muddapu, V. R.

Publications and source records attributed to Muddapu, V. R..

7 recordsLinked to original sources

bioRxiv2022

A Generalized Reinforcement Learning-Based Deep Neural Network (GRL-DNN) Agent Model for Diverse Cognitive Constructs

Human cognition is characterized by a wide range of capabilities including goal-oriented selective attention, distractor suppression, decision making, response inhibition, and working memory. Much research has focused on studying these individual components of cognition in isolation, whereas in several translational applications for cognitive impairment, multiple cognitive functions are altered in a given individual. Hence it is important to study multiple cognitive abilities in the same subject or, in computational terms, model them using a single model. To this end, we propose a unified, reinforcement learning-based agent model comprising of systems for representation, memory, value computation and exploration. We successfully modelled the aforementioned cognitive tasks and show how individual performance can be mapped to model meta-parameters. This model has the potential to serve as a proxy for cognitively impaired conditions, and can be used as a clinical testbench on which therapeutic interventions can be simulated first before delivering to human subjects.

neuroscience↗

bioRxiv2022

Reconstruction and simulation of thalamoreticular microcircuitry

Thalamoreticular circuitry is known to play a key role in attention, cognition and the generation of sleep spindles, and is implicated in numerous brain disorders, but the cellular and synaptic mechanisms remain intractable. Therefore, we developed the first detailed computational model of mouse thalamus and thalamic reticular nucleus microcircuitry that captures morphological and biophysical properties of [~]14,000 neurons connected via [~]6M synapses, and recreates biological synaptic and gap junction connectivity. Simulations recapitulate multiple independent network-level experimental findings across different brain states, providing a novel unifying cellular and synaptic account of spontaneous and evoked activity in both wakefulness and sleep. Furthermore, we found that: 1.) inhibitory rebound produces frequency-selective enhancement of thalamic responses during wakefulness, in addition to its role in spindle generation; 2.) thalamic interactions generate the characteristic waxing and waning of spindle oscillations; and 3.) changes in thalamic excitability (e.g. due to neuromodulation) control spindle frequency and occurrence. The model is openly available and provides a new tool to interpret spindle oscillations and test hypotheses of thalamoreticular circuit function and dysfunction across different network states in health and disease.

neuroscience↗

bioRxiv2021

A Multiscale, Systems-level, Neuropharmacological Model of Cortico-Basal Ganglia System for Arm Reaching under Normal, Parkinsonian and Levodopa Medication Conditions

The root cause of Parkinsons disease (PD) is the death of dopaminergic neurons in Substantia Nigra pars compacta (SNc). The exact cause of this cell death is still not known. Loss of SNc cells manifest as the cardinal symptoms of PD, including tremor, rigidity, bradykinesia, and postural imbalance. To investigate the PD condition in detail and understand the link between loss of cells in SNc and PD symptoms, it is important to have an integrated multiscale computational model that can replicate the symptoms at the behavioural level by evoking the key cellular and molecular underlying mechanisms that contribute to the pathology. In line with this objective, we present a multiscale integrated model of cortico-basal ganglia motor circuitry for arm reaching task, incorporating a detailed biophysical model of SNc dopaminergic neuron. Earlier researchers have shown that fluctuations in dopamine (DA) signals are analogous to reward/punishment signals, thereby prompting application of concepts from reinforcement learning (RL) to modelling the basal ganglia system. In our model, we replace the abstract representations of reward with the realistic variable of extracellular DA released by a network of SNc cells and incorporate it with the RL-based behavioural model, which simulates the arm reaching task. Our results showed that as SNc cell loss increases, the percentage success rate to reach the target decreases, and average time to reach the target increases. With levodopa (L-DOPA) medication, both the success rate and the average time to reach the target improved significantly. The proposed model also exhibits how differential dopaminergic axonal degeneration in basal ganglia results in various cardinal symptoms of PD as manifest in reaching movements. From the model results, we were able to show the side effects of L-DOPA mediation, such as wearing off and peak dosage dyskinesias. Moreover, from the results, we were able to predict the optimum dosage for varying degrees of cell loss and L-DOPA medication. The proposed model has a potential clinical application where drug dosage can be optimized as per patient characteristics. We conclude that our model presents a realistic and efficient way of simulating the PD pathology conditions and the effect of levodopa medication, thereby giving a reliable indicator towards the optimization of the drug dosage.

neuroscience↗

bioRxiv2020

A Computational Model of Levodopa-Induced Toxicity in Substantia Nigra Pars Compacta in Parkinson's Disease

BackgroundParkinsons disease (PD) is caused by the progressive loss of dopaminergic cells in substantia nigra pars compacta (SNc). The root cause of this cell loss in PD is still not decisively elucidated. A recent line of thinking traces the cause of PD neurodegeneration to metabolic deficiency. Due to exceptionally high energy demand, SNc neurons exhibit a higher basal metabolic rate and higher oxygen consumption rate, which results in oxidative stress. Recently, we have suggested that the excitotoxic loss of SNc cells might be due to energy deficiency occurring at different levels of neural hierarchy. Levodopa (LDOPA), a precursor of dopamine, which is used as a symptom-relieving treatment for PD, leads to outcomes that are both positive and negative. Several researchers suggested that LDOPA might be harmful to SNc cells due to oxidative stress. The role of LDOPA in the course of PD pathogenesis is still debatable. New MethodWe hypothesize that energy deficiency can lead to LDOPA-induced toxicity (LIT) in two ways: by promoting dopamine-induced oxidative stress and by exacerbating excitotoxicity in SNc. We present a multiscale computational model of SNc-striatum system, which will help us in understanding the mechanism behind neurodegeneration postulated above and provides insights for developing disease-modifying therapeutics. ResultsIt was observed that SNc terminals are more vulnerable to energy deficiency than SNc somas. During LDOPA therapy, it was observed that higher LDOPA dosage results in increased loss of somas and terminals in SNc. It was also observed that co-administration of LDOPA and glutathione (antioxidant) evades LDOPA-induced toxicity in SNc neurons. Comparison with Existing MethodsOur proposed multiscale model of SNc-striatum system is first of its kind, where SNc neuron was modelled at biophysical level, and striatal neurons were modelled at spiking level. ConclusionsWe show that our proposed model was able to capture LDOPA-induced toxicity in SNc, caused by energy deficiency.

neuroscience↗

bioRxiv2020

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

Parkinsons 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.

neuroscience↗

bioRxiv2020

INFLUENCE OF ENERGY DEFICIENCY ON THE MOLECULAR PROCESSES OF SUBSTANTIA NIGRA PARS COMPACTA CELL FOR UNDERSTANDING PARKINSONIAN NEURODEGENERATION: A COMPREHENSIVE BIOPHYSICAL COMPUTATIONAL MODEL

Parkinsons disease (PD) is the second most prominent neurodegenerative disease around the world. Although it is known that PD is caused by the loss of dopaminergic cells in substantia nigra pars compacta (SNc), the decisive cause of this inexorable cell loss is not clearly elucidated. We hypothesize that "Energy deficiency at a sub-cellular/cellular/systems level can be a common underlying cause for SNc cell loss in PD." Here, we propose a comprehensive computational model of SNc cell which helps us to understand the pathophysiology of neurodegeneration at subcellular level in PD. The proposed model incorporates a rich vein of molecular dynamics related to SNc neurons such as ion channels, active pumps, ion exchangers, dopamine turnover processes, energy metabolism pathways, calcium buffering mechanisms, alpha-synuclein aggregation, Lewy body formation, reactive oxygen species (ROS) production, levodopa uptake, and apoptotic pathways. The proposed model was developed and calibrated based on experimental data. The influx of glucose and oxygen into the model was controlled, and the consequential ATP variations were observed. Apart from this, the dynamics of other molecular players (alpha-synuclein, ROS, calcium, and dopamine) known to play an important role in PD pathogenesis are also studied. The aim of the study was to see how deficits in supply of energy substrates (glucose and oxygen) lead to a deficit in ATP, and furthermore, deficits in ATP are the common factor underlying the pathological molecular-level changes including alpha-synuclein aggregation, ROS formation, calcium elevation, and dopamine dysfunction. The model suggests that hypoglycemia plays a more crucial role in leading to ATP deficits than hypoxia. We believe that the proposed model provides an integrated modelling framework to understand the neurodegenerative processes underlying PD.

neuroscience↗

bioRxiv2018

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

Parkinsons 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, glutamate inhibition, dopamine restoration, subthalamotomy and deep brain stimulation showed superior neuroprotective effects in the proposed model.

neuroscience↗