Biology subjects

Ramaswamy, S.

Publications and source records attributed to Ramaswamy, S..

4 recordsLinked to original sources

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↗

bioRxiv2018

Objective Classification of Neocortical Pyramidal Cells

A consensus on the number of morphologically different types of pyramidal cells (PCs) in the neocortex has not yet been reached, despite over a century of anatomical studies. This is because of a lack of agreement on the subjective classifications of neuron types, which is based on expert analyses of neuronal morphologies: the shapes of somata, dendrites, and axons. Even for neurons that are visually different to non-experts, there is no common ground to consistently distinguish morphological types. We found that objective classification is possible with methods from algebraic topology, and that the dendritic arbor is sufficient for reliable identification of distinct types of PCs. We also provide a solution for the more challenging problem of whether two similar neurons belong to different types or to a continuum of the same type. Using this scheme, we objectively identify seventeen types of PCs in the rat somatosensory cortex. Our topological classification does not require expert input, is stable, and helps settle the long-standing debate on whether cell-types are discrete or continuous morphological variations of each other.

neuroscience↗

bioRxiv2018

Data-driven modeling of cholinergic modulation of neural microcircuits: bridging neurons, synapses, and network states

1Neuromodulators, such as acetylcholine (ACh), control information processing in neural microcircuits by regulating neuronal and synaptic physiology. Computational models and simulations enable predictions on the potential role of ACh in reconfiguring network states. As a prelude into investigating how the cellular and synaptic effects of ACh collectively influence emergent network dynamics, we developed a data-driven framework incorporating phenomenological models of the anatomy and physiology of cholinergic modulation of the neocortex. The first-draft models were integrated into a biologically detailed tissue model of neocortical microcircuitry to predict how ACh affects different types of neurons and synapses, and consequently alters global network states. Preliminary simulations not only corroborate the long-standing notion that ACh desynchronizes network activity, but also reveal a potentially finegrained control over a spectrum of neocortical states. We show that low levels of ACh, such as those during sleep, drive microcircuit activity into slow oscillations and network synchrony, whereas high ACh concentrations, such as those during wakefulness, govern fast oscillations and network asynchrony. In addition, network states modulated by ACh levels shape spike-time cross-correlations across distinct neuronal populations in strikingly different ways. These effects are likely due to the differential regulation of neurons and synapses caused by increasing levels of ACh that enhances cellular excitability by increasing neuronal activity and decreases the efficacy of local synaptic transmission by altering neurotransmitter release probability. We conclude by discussing future directions to refine the biological accuracy of the framework, which will extend its utility and foster the development of hypotheses to investigate the role of neuromodulation in neural information processing.

neuroscience↗

bioRxiv2017

Inferring critical points of ecosystem transitions from spatial data

Ecosystems can undergo abrupt transitions from one state to an alternative stable state when the driver crosses a threshold or a critical point. Dynamical systems theory suggests that systems take long to recover from perturbations near such transitions. This leads to characteristic changes in the dynamics of the system, which can be used as early warning signals of imminent transitions. However, these signals are qualitative and cannot quantify the critical points. Here, we propose a method to estimate critical points quantitatively from spatial data. We employ a spatial model of vegetation that shows a transition from vegetated to bare state. We show that the critical point can be estimated as the ecosystem state and the driver values at which spatial variance and autocorrelation are maximum. We demonstrate the validity of this method by analysing spatial data from regions of Africa and Australia that exhibit alternative vegetation biomes.

ecology↗