Biology subjects

Chakravarthy, V. S.

Publications and source records attributed to Chakravarthy, V. S..

At least 19 recordsLinked to original sources

bioRxiv2026

Impaired value estimation over space explains turn-induced freezing of gait in Parkinson's disease: A cortico-basal ganglia-inspired reinforcement learning model

Parkinson's disease (PD) gait is characterized by reduced walking speed, short shuffling steps, increased double stance time, and increased step time variability. The most debilitating symptom in PD is freezing of gait (FOG), a context-dependent locomotion arrest, mostly occurring while encountering turns, obstacles, and narrow pathways. FOG is more pronounced during turning, a complex task that challenges motor planning and execution. Here, we simulate this phenomenon using a computational model inspired by the cortico-basal ganglia circuitry, based on reinforcement learning (RL) principles. In the model, the agent was trained to learn a value profile while navigating a path that involved turning. To simulate PD conditions, we clamped the temporal difference (TD) error, typically associated with dopaminergic signaling. Our model results show that compared to healthy controls (HC), PD non-freezers and PD freezers exhibit a reduction in turning velocity and step length, alongside an attenuated compensatory step-width widening at the sharpest turn, more pronounced in PD freezers, who additionally show increased step time variability. These kinematic impairments worsen during tighter turns. Ultimately, this model implicates FOG as a problem of impaired estimation of value over space and could potentially represent a generalized deficit in gait impairments in PD.

neuroscience↗

bioRxiv2024

A unified model of hippocampal spatial and object cells involving bidirectionally coupled Lateral and Medial Entorhinal Cortical layers

Popularly referred to as the GPS of the brain, the hippocampus has a variety of neurons that encode spatial properties of the environment. These spatial cells of the hippocampus may be broadly placed under two categories - those that encode spatial locations (e.g. place cells, grid cells etc) and those that encode spatial objects (eg. Object-sensitive cells. Object-trace cells etc). There are computational models that explain emergence of specific types of spatial cells, but it is challenging to construct integrative models that can demonstrate the emergence of the complete range of spatial cells both space and object type. We present a simple, unified computational model that explains the emergence of a wide variety of object- and spatially-sensitive neurons in the hippocampus. The model is essentially a deep neural network that combines visual and path integration information. The visual information is received by a part of the model that is analogous to Lateral Entorhinal Cortex (LEC) and path integration information is received by a layer analogous to Medial Entorhinal Cortex (MEC). In order to arrive at a consistent estimate of position, LEC and MEC in the model are connected laterally using a Graph Neural Network. The model is trained to predict position, orientation and reward of a simulated agent. The agent explores a box-like environment with colored walls and objects on the floor and is rewarded based on its encounters with objects. The model demonstrates the emergence of the following 7 types of spatial and object cells - place, grid, border, object, object-sensitive, object-vector and, object-trace cells. The model findings compare favorably with a large body of experimental literature on hippocampal spatial cells.

neuroscience↗

bioRxiv2024

Electroencephalogram (EEG) Classification using a bio-inspired Deep Oscillatory Neural Network

In this paper, we propose two models of oscillatory neural networks - the Deep Oscillatory Neural Network (DONN) and a convolutional variation of it named Oscillatory Convolutional Neural Network (OCNN) - and apply the models to a variety of problems involving the classification and prediction of Electroencephalogram (EEG) signals. Deep neural networks applied to signal processing problems will have to incorporate various architectural features to remember the history of the input signals e.g., loops between the layers, "gated" neurons, and tapped delay lines. But real brains have rich dynamics expressed in terms of frequency bands like alpha, beta, gamma, delta, etc. To incorporate this aspect of brain dynamics in a Recurrent Neural Network (RNN) we propose to use nonlinear oscillators as dynamic neuron models in the hidden layers. The two oscillatory deep neural networks proposed are applied to the following EEG classification and prediction problems: Prediction of nearby EEG channels, classification of single-channel EEG data (healthy vs. epileptic, different stages of sleep stage classification), and multi-channel EEG data (Epileptic vs. Normal, Left vs. right-hand Motor imagery movement, and healthy vs. Claustrophobic EEG).

bioengineering↗

bioRxiv2024

A Computational Model of Deep Brain Stimulation for Parkinsons Disease Tremor

Parkinsons Disease (PD) is a progressive neurological disorder that is typically characterized by a range of motor dysfunctions and its impact extends beyond physical abnormalities into emotional well-being and cognitive symptoms. The loss of dopaminergic neurons in the Substantia nigra pars compacta (SNc) leads to an array of dysfunctions in the functioning of Basal Ganglia (BG) circuitry that manifests into PD. While active research is being carried out in finding the root cause of SNc cell deaths, various therapeutic techniques are prevalent to manage the symptoms of PD. The most common approach in managing the symptoms is replenishing the lost dopamine in the form of taking dopaminergic medications such as Levodopa amidst its long-term complications. Another commonly used intervention for PD is deep brain stimulation (DBS), which is a invasive technique where an electrode is surgically inserted into the skull and a high frequency current of appropriate characteristics is delivered to the brain region. DBS is most commonly used when levodopa medication efficacy reduces and also in combination with levodopa medication that will help reducing the required dosage of medication prolonging the therapeutic effect. DBS is also a go to option when motor complications such as dyskinesias emerge as a side effect of medication. Several studies have also reported that though DBS is found to be effective in suppressing severe motor symptoms such as tremor and rigidity, it has adverse effect on cognitive capabilities. Henceforth it is important to understand the exact mechanism of DBS in alleviating the motor symptoms. A computational model of DBS stimulation for motor symptoms will offer great insights in understanding the mechanisms underlying the DBS and in this line in our current study we model a cortico-basal ganglia circuitry of arm reaching where we simulate healthy controls (HC) and PD symptoms as well as the DBS effect on the PD tremor. With DBS current characteristics of 220 pA, 130 Hz and 100 microseconds pulse-width we were able to see maximum therapeutic effect using our model. This model can be extended to accommodate cognitive dynamics in future so as to study the impact of DBS on cognitive symptoms and optimizing the parameters to get optimal performance effect across modalities.

neuroscience↗

bioRxiv2022

ASM-3D: An attentional search model fashioned after what andwhere/how pathways for target search in 3D environment

We propose a biologically inspired attentional search model for target search in a 3D environment, which has two separate channels for object classification, analogous to the "what" pathway in the human visual system, and for prediction of the next location of the camera, analogous to the "where" pathway. We generated 3D Cluttered Cube datasets that consist of an image on one vertical face, and clutter images on the other faces. The camera goes around each cube on a circular orbit centered on the cube and determines the identity of the image and the face on which it is located. The images pasted on the cube faces were drawn from three: MNIST handwriting digit, QuickDraw, and RGB MNIST handwriting digit datasets. The attentional input of 3 concentric cropped windows resembling the high-resolution central fovea and low-resolution periphery of the retina, flows through a Classifier Network and a Camera Motion Network. The Classifier Network classifies the current view into one of the classes or clutter. The Camera Motion Network predicts the cameras next position on the orbit (varying the azimuthal angle or {theta}). Here the camera performs one of three actions: move right, move left, or dont move. The Camera-Position Network adds the cameras current{theta} information into the higher features level of the Classifier Network and the Camera Motion Network. The Camera Motion Network is trained using Q-learning where the reward is 1 if the classifier network gives the correct classification, otherwise 0. Total loss is computed by adding the mean square loss of temporal difference and cross entropy loss. Then the total loss is backpropagated using Adam optimizer. Results on two grayscale image datasets and one RGB image dataset show that the proposed model is successfully able to discover the desired search pattern to find the target face on the cube, and also classify the target face accurately.

neuroscience↗

bioRxiv2022

Modeling Hippocampal Spatial Cells in Rodents navigating in 3D environments

Studies on the neural correlates of navigation in 3D environments are plagued by several unresolved issues. For example, experimental studies show markedly different place cell responses in rats and bats, both navigating in 3D environments. In an effort to understand this divergence, we propose a deep autoencoder network to model the place cells and grid cells in a simulated agent navigating in a 3D environment. We also explore the possibility of a vital role that Head Direction (HD) tuning plays in determining the isotropic or anisotropic nature of the observed place fields in different species. The input layer to the autoencoder network model is the HD layer which encodes the agents HD in terms of azimuth ({theta}) and pitch angles ({phi}). The output of this layer is given as input to the Path Integration (PI) layer, which integrates velocity information into the phase of oscillating neural activity. The output of the PI layer is modulated and passed through a low pass filter to make it purely a function of space before passing it to an autoencoder. The bottleneck layer of the autoencoder model encodes the spatial cell like responses. Both grid cell and place cell like responses are observed. The proposed model is verified using two experimental studies with two 3D environments in each. This model paves the way for a holistic approach of using deep networks to model spatial cells in 3D navigation.

neuroscience↗

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

The Influence of Neural activity and Neural Cytoarchitecture on Cerebrovascular Arborization: A Computational Model

Normal functioning of the brain relies on a continual and efficient delivery of energy by a vast network of cerebral blood vessels. The bidirectional coupling between neurons and blood vessels consists of vasodilatory energy demand signals from neurons to blood vessels, and the retrograde flow of energy substrates from the vessels to neurons, which fuel neural firing, growth and other housekeeping activities in the neurons. Recent works indicate that, in addition to the functional coupling observed in the adult brain, the interdependence between the neural and vascular networks begins at the embryonic stage, and continues into subsequent developmental stages. The proposed Vascular Arborization Model (VAM) captures the effect of neural cytoarchitecture and neural activity on vascular arborization. The VAM describes three important stages of vascular tree growth: (i) The prenatal growth phase, where the vascular arborization depends on the cytoarchitecture of neurons and non-neural cells, (ii) the post-natal growth phase during which the further arborization of the vasculature depends on neural activity in addition to neural cytoarchitecture, and (iii) the settling phase, where the fully grown vascular tree repositions its vascular branch points or nodes to ensure minimum path length and wire length. The vasculature growth depicted by VAM captures structural characteristics like vascular volume density, radii, mean distance to proximal neurons in the cortex. VAM-grown vasculature agrees with the experimental observation that the neural densities do not covary with the vascular density along the depth of the cortex but predicts a high correlation between neural areal density and microvascular density when compared over a global scale (across animals and regions). To explore the influence of neural activity on vascular arborization, the VAM was used to grow the vasculature in neonatal rat whisker barrel cortex under two conditions: (i) Control, where the whiskers were intact and (ii) Lesioned, where one row of whiskers was cauterized. The model captures a significant reduction in vascular branch density in lesioned animals compared to control animals, concurring with experimental observation.

neuroscience↗

bioRxiv2022

Modeling the tonotopic map using a two-dimensional array of neural oscillators

We present a model of a tonotopic map known as the Oscillatory Tonotopic Self-Organizing Map (OTSOM). It is a 2-dimensional, self-organizing array of Hopf oscillators, capable of performing a Fourier-like decomposition of the input signal. While the rows in the map encode the input phase, the columns encode frequency. Although Hopf oscillators exhibit resonance to a sinusoidal signal when there is a frequency match, there is no obvious way to also achieve phase tuning. We propose a simple method by which a pair of Hopf oscillators, unilaterally coupled through a coupling scheme termed as modified power coupling, can exhibit tuning to the phase offset of sinusoidal forcing input. The training of OTSOM is performed in 2 stages: while the frequency tuning is adapted in stage 1, phase tuning is adapted in stage 2. Earlier tonotopic map models have modeled frequency as an abstract parameter unconnected to any oscillation. By contrast, in OTSOM, frequency tuning emerges as a natural outcome of an underlying resonant process. The OTSOM model can be regarded as an approximation of the tonotopic map found in the primary auditory cortices of mammals, particularly exemplified in the studies of echolocating bats.

neuroscience↗

bioRxiv2021

The development of bi-directionally coupled self-organizing neurovascular networks captures orientation-selective neural and hemodynamic cortical responses

The network of neurons in the brain is considered the primary substrate of information processing. Despite growing evidence on the possible role of cerebral blood flow in information processing, the cerebrovascular network is generally viewed as an irrigation system that ensures a timely supply of oxygen, glucose, and nutrients to the neural tissue. However, a recent study has shown that cerebral microvessels, like neurons, also exhibit tuned responses to sensory stimuli. Tuned neural responses to sensory stimuli are certainly enhanced with experience-dependent Hebbian plasticity and other forms of learning. Hence it is possible that the densely interconnected microvascular network might also be subject to some form of plasticity or competitive learning rules during early postnatal development such that its fine-scale structure becomes optimized for metabolic delivery to a given neural micro-architecture. To explore the possibility of adaptive lateral interactions and tuned responses in cerebral microvessels, we modeled the cortical neurovascular network by interconnecting two laterally connected self-organizing networks (Laterally Interconnected Synergetically Self-Organizing Map - LISSOM). The afferent and lateral connections of the LISSOM were defined by trainable weights. By varying the topology of lateral connectivity in the vascular network layer, we observed that the partial correspondence of feature selectivity between neural and hemodynamic responses could be explained by lateral coupling across local blood vessels such that the central domain receives an excitatory drive of more blood flow and a more distal surrounding region where blood flow is reduced. Critically, our simulations suggest a new role for feedback from the vascular to the neural network because the radius of vascular perfusion seems to determine whether the cortical neural map develops into a clustered and columnar vs. salt-and-pepper organization.

neuroscience↗

bioRxiv2021

The Flip-flop neuron - A memory efficient alternative for solving challenging sequence processing and decision making problems

Sequential decision making tasks that require information integration over extended durations of time are challenging for several reasons including the problem of vanishing gradients, long training times and significant memory requirements. To this end we propose a neuron model fashioned after the JK flip-flops in digital systems. A flip-flop is a sequential device that can store state information of the previous history. We incorporate the JK flip-flop neuron into several deep network architectures and apply the networks to difficult sequence processing problems. The proposed architectures include flip-flop neural networks (FFNNs), bidirectional flip-flop neural networks (BiFFNNs), convolutional flip-flop neural networks (ConvFFNNs), and bidirectional convolutional flip-flop neural networks (BiConvFFNNs). Learning rules of proposed architectures have also been derived. We have considered the most popular benchmark sequential tasks like signal generation, sentiment analysis, handwriting generation, text generation, video frame prediction, lung volume prediction, and action recognition to evaluate the proposed networks. Finally, we compare the results of our networks with the results from analogous networks with Long Short-Term Memory (LSTM) neurons on the same sequential tasks. Our results show that the JK flip-flop networks outperform the LSTM networks significantly or marginally on all the tasks, with only half of the trainable parameters.

neuroscience↗

bioRxiv2021

Artificial Neurovascular Network (ANVN) to Study the Accuracy Vs. Efficiency Trade-Off in an Energy Dependent Neural Network

AbstractArtificial feedforward neural networks perform a wide variety of classification and function approximation tasks with high accuracy. Unlike their artificial counterparts, biological neural networks require a supply of adequate energy delivered to single neurons by a network of cerebral microvessels. Since energy is a limited resource, a natural question is whether the cerebrovascular network is capable of ensuring maximum performance of the neural network while consuming minimum energy? Should the cerebrovascular network also be trained, along with the neural network, to achieve such an optimum? In order to answer the above questions in a simplified modeling setting, we constructed an Artificial Neurovascular Network (ANVN) comprising a multilayered perceptron (MLP) connected to a vascular tree structure. The root node of the vascular tree structure is connected to an energy source, and the terminal nodes of the vascular tree supply energy to the hidden neurons of the MLP. The energy delivered by the terminal vascular nodes to the hidden neurons determines the biases of the hidden neurons. The "weights" on the branches of the vascular tree depict the energy distribution from the parent node to the child nodes. The vascular weights are updated by a kind of "backpropagation" of the energy demand error generated by the hidden neurons. We observed that higher performance was achieved at lower energy levels when the vascular network was also trained along with the neural network. This indicates that the vascular network needs to be trained to ensure efficient neural performance. We observed that below a certain network size, the energetic dynamics of the network in the per capita energy consumption vs. classification accuracy space approaches a fixed-point attractor for various initial conditions. Once the number of hidden neurons increases beyond a threshold, the fixed point appears to vanish, giving place to a line of attractors. The model also showed that when there is a limited resource, the energy consumption of neurons is strongly correlated to their individual contribution to the networks performance. Author summaryThe limited availability of resources contributed to a significant role in shaping evolution. The brain is also no different. It is known to have tremendous computing power at a significantly lower cost than artificial computing systems. The artificial neural networks aim typically at minimizing output error and maximizing accuracy. A biological network like the brain has an added constraint of energy availability, which might force it to choose an optimal solution that provides the best possible accuracy while consuming minimum energy. The intricate vascular network which ensures adequate energy to the brain might be a systematically trained layout rather than a hard-wired anatomical structure. Through this work, we intend to explore how the artificial neural network would behave if it were made dependent on an energy supply network and how the training of the energy supply network would influence the performance of the neural network. Our model concluded that training of a vascular energy network is highly desirable, and when the size of the neural network is small, the energy consumed by each neuron is a direct readout on its contribution to the network performance.

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 Complex-valued Oscillatory Neural Network for Storage and Retrieval of Multichannel Electroencephalogram Signals

Recurrent neural networks with associative memory properties are typically based on fixed-point dynamics, which is fundamentally distinct from the oscillatory dynamics of the brain. There have been proposals for oscillatory associative memories, but here too, in the majority of cases, only binary patterns are stored as oscillatory states in the network. Oscillatory neural network models typically operate at a single/common frequency. At multiple frequencies, even a pair of oscillators with real coupling exhibits rich dynamics of Arnold tongues, not easily harnessed to achieve reliable memory storage and retrieval. Since real brain dynamics comprises of a wide range of spectral components, there is a need for oscillatory neural network models that operate at multiple frequencies. We propose an oscillatory neural network that can model multiple time series simultaneously by performing a Fourier-like decomposition of the signals. We show that these enhanced properties of a network of Hopf oscillators become possible by operating in the complex-variable domain. In this model, the single neural oscillator is modeled as a Hopf oscillator, with adaptive frequency and dynamics described over the complex domain. We propose a novel form of coupling, dubbed "power coupling," between complex Hopf oscillators. With power coupling, expressed naturally only in the complex-variable domain, it is possible to achieve stable (normalized) phase relationships in a network of multifrequency oscillators. Network connections are trained either by Hebb-like learning or by delta rule, adapted to the complex domain. The network is capable of modeling N-channel Electroencephalogram time series with high accuracy and shows the potential as an effective model of large-scale brain dynamics.

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↗

bioRxiv2019

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

The neurovascular coupling is mostly considered as a master-slave relationship between the neurons and the cerebral vessels: the neurons demand energy which the vessels supply in the form of glucose and oxygen. In the recent past both theoretical and experimental studies have suggested that the neurovascular coupling is a bidirectional system, a loop that includes a feedback signal from the vessels influencing neural firing and plasticity. An integrated model of bidirectionally connected neural network and vascular network is hence required to understand the relationship between the informational and metabolic aspects of neural dynamics. In this study, we present a computational model of the bidirectional neurovascular system in the whisker barrel cortex and study the effect of such coupling on neural activity and plasticity as manifest in the map formation. In this model, a biologically plausible self-organizing network model of rate coded, dynamic neurons is nourished by a network of vessels modeled using the biophysical properties of blood vessels. The neural layer which is designed to simulate the whisker barrel cortex of rat transmits the vasodilatory signals to the vessels. The feedback from the vessels is in the form of available oxygen for oxidative metabolism whose end result is the ATP necessary to fuel neural firing. The model captures the effect of the feedback from the vascular network on the neuronal map formation in the whisker barrel model under normal and pathological (Hypoxia and Hypoxia-Ischemia) conditions.\n\nAuthor SummaryAlthough neurovascular coupling is typically depicted as a unidirectional influence originating from the neurons and acting on the cerebral vessels, in reality it forms a bidirectional system, consisting of neuronal energy demand signals transmitted to the vessels, and a feedback of metabolic substrates from the vessels, that influence the neural firing and plasticity. We present a computational model of the bidirectional neurovascular coupling in the whisker barrel cortex of rats and study the effect of such coupling on neural activity and plasticity as manifest in the map formation. The model consists of a biologically plausible self-organizing dynamic, neural network model and a biophysical vascular network model that nourishes the neural network in the form of oxygen necessary for neural oxidative metabolism. The model reproduces the spatio-temporal hemodynamic responses observed in rat whisker barrel cortex. It also demonstrates the essentiality of the vascular feedback on the whisker barrel map formation under normal and pathological conditions.

neuroscience↗