Biology subjects

Komarov, M.

Publications and source records attributed to Komarov, M..

4 recordsLinked to original sources

bioRxiv2017

Selective Recruitment of Cortical Neurons by Electrical Stimulation

Despite its critical importance in experimental and clinical neuroscience, at present there is no systematic method to predict which neural elements will be activated by a given stimulation regime. Here we develop a novel approach to model the effect of cortical stimulation on spiking probability of neurons in a volume of tissue, by applying an analytical estimate of stimulation-induced activation of different cell types across cortical layers. We utilize the morphology and properties of axonal arborization profiles obtained from publicly available anatomical reconstructions of the twelve main categories of neocortical neurons to derive the dependence of activation probability on cell type, layer and distance from the source. We then propagate this activity through the local network incorporating connectivity, synaptic and cellular properties. Our work predicts that (a) intracranial cortical stimulation induces selective activation across cell types and layers; (b) superficial anodal stimulation is more effective than cathodal at cell activation; (c) cortical surface stimulation focally activates layer I axons, and (d) an optimal stimulation intensity exists capable of eliciting cell activation lasting beyond the end of stimulation.

neuroscience↗

bioRxiv2017

Simulating human sleep spindle MEG and EEG from ion channel and circuit level dynamics

BackgroundAlthough they form a unitary phenomenon, the relationship between extracranial M/EEG and transmembrane ion flows is understood only as a general principle rather than as a well-articulated and quantified causal chain.\n\nMethodWe present an integrated multiscale model, consisting of a neural simulation of thalamus and cortex during stage N2 sleep and a biophysical model projecting cortical current densities to M/EEG fields. Sleep spindles were generated through the interactions of local and distant network connections and intrinsic currents within thalamocortical circuits. 32,652 cortical neurons were mapped onto the cortical surface reconstructed from subjects MRI, interconnected based on geodesic distances, and scaled-up to current dipole densities based on laminar recordings in humans. MRIs were used to generate a quasi-static electromagnetic model enabling simulated cortical activity to be projected to the M/EEG sensors.\n\nResultsThe simulated M/EEG spindles were similar in amplitude and topography to empirical examples in the same subjects. Simulated spindles with more core-dominant activity were more MEG weighted.\n\nComparison with Existing MethodsPrevious models lacked either spindle-generating thalamic neural dynamics or whole head biophysical modeling; the framework presented here is the first to simultaneously capture these disparate scales simultaneously.\n\nConclusionsThis multiscale model provides a platform for the principled quantitative integration of existing information relevant to the generation of sleep spindles, and allows the implications of future findings to be explored. It provides a proof of principle for a methodological framework allowing large-scale integrative brain oscillations to be understood in terms of their underlying channels and synapses.

neuroscience↗

bioRxiv2017

Computational model of brainstem circuit for state dependent control of hypoglossal motoneurons

In patients with obstructive sleep apnea (OSA) the pharyngeal muscles become relaxed during sleep, which leads to a partial or complete closure of upper airway. Empirical studies suggest that withdrawal of noradrenergic and serotonergic drives importantly contribute to depression of hypoglossal motoneurons during rapid eye-movement (REM) sleep and, therefore, may contribute to OSA pathophysiology; however, specific cellular and synaptic mechanisms remain unknown. It was recently suggested that, in order to explain experimental observations, the neuronal network for monoaminergic control of excitability of hypoglossal motoneurons has to include excitatory and inhibitory perihypoglossal interneurons that would mediate noradrenergic and serotonergic drives to the motoneurons. In this study, we applied a biophysical network model to validate the rationality of the proposed circuit and to investigate the dynamics of its neuronal populations during REM sleep-induced withdrawal of noradrenergic and serotonergic drives. The state-dependent activity of the model hypoglossal motoneurons during simulated REM sleep with or without a virtual application of noradrenergic and serotonergic drugs was in qualitative agreement with in vivo data. The study predicts the dynamics of the perihypoglossal interneurons during these conditions and corroborates the hypothesis that the excitatory interneurons may integrate both noradrenergic and serotonergic drives. The latter drive has to be mediated by the inhibitory interneurons. The study suggests that perihypoglossal interneurons may serve as novel potential targets for pharmacological treatment of OSA.

neuroscience↗

bioRxiv2017

Differential roles of sleep spindles and sleep slow oscillations in memory consolidation

Sleep plays an important role in consolidation of recent memories. However, the mechanisms of consolidation remain poorly understood. In this study, using a realistic computational model of the thalamocortical network, we demonstrated that sleep spindles (the hallmark of N2 stage sleep) and slow oscillations (the hallmark of N3 stage sleep) both facilitate spike sequence replay as necessary for consolidation. When multiple memories were trained, the local nature of spike sequence replay during spindles allowed replay of the memories independently, while during slow oscillations replay of the weak memory was competing to the strong memory replay. This led to the weak memory extinction unless when sleep spindles (N2 sleep) preceded slow oscillations (N3 sleep), as observed during natural sleep. Our study presents a mechanistic explanation for the role of sleep rhythms in memory consolidation and proposes a testable hypothesis how the natural structure of sleep stages provides an optimal environment to consolidate memories.\n\nSignificant StatementNumerous studies suggest importance of NREM sleep rhythms - spindles and slow oscillations - in sleep related memory consolidation. However, synaptic mechanisms behind the role of these rhythms in memory and learning are still unknown. Our new study predicts that sleep replay - the neuronal substrate of memory consolidation - is organized within the sleep spindles and coordinated by the Down to Up state transitions of the slow oscillation. For multiple competing memories, slow oscillations facilitated only strongest memory replay, while sleep spindles allowed a consolidation of the multiple competing memories independently. Our study predicts how the basic structure of the natural sleep stages provides an optimal environment for consolidation of multiple memories.

neuroscience↗