Biology subjects

Krishnan, G. P.

Publications and source records attributed to Krishnan, G. P..

8 recordsLinked to original sources

bioRxiv2023

Emergent effects of synaptic connectivity on cortical sleep slow wave amplitude, density and propagation in a large-scale thalamocortical network model of the human brain

Slow-wave sleep (SWS), characterized by slow oscillations (SO, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the understanding of how global and local SO emerges from micro-scale neuron dynamics and network connectivity remains unclear. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and slow-wave sleep, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. Increase of the overall synaptic strength led to synchronized global SO, while decrease of synaptic connectivity produced only local slow-waves that would not propagate beyond local area. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease. Author SummarySlow Wave Sleep (SWS) is a primary brain state displayed during Non-Rapid Eye Movement (NREM) sleep. While previously thought of as homogenous waves of activity that sweep across the entire brain, modern research has suggested a more nuanced pattern of activity that can vary between local and global slow wave activity. However, understanding how these states emerge from small scale neuronal dynamics and network connectivity remains unclear. We developed a biophysically realistic model of the human brain capable of generating SWS-like behavior, and investigated the role of connectivity in the spatio-temporal dynamics of these slow waves. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network behavior - specifically, models with relatively weaker long-range connectivity resulted in mixed states of global and local slow waves. These results were compared to human data, and we found that models producing mixed states provided the best match to the network behavior and functional connectivity of human subject data. These findings shed light on how the spatio-temporal properties of SWS emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.

neuroscience↗

bioRxiv2020

N-glycosylation network construction and analysis to modify glycans on the spike S glycoprotein of SARS-CoV-2.

Background The spike S-protein of SARS-CoV-2 is N-glycosylated. The N-glycan structure and composition of this glycoprotein influence how the virus interacts with host cells.Objective To identify a putative N-glycan biosynthesis pathway of SARS-CoV-2 (HEK293 cell recombinant) from previously published mass spectrometric studies, and to identify what effect blocking some enzymes has on the overall glycoprotein profile. Finally, our goal was to provide the biosynthesis network, and glycans in easy-to-use format for further glycoinformatics work.Methods We reconstructed the glycosylation network based on previously published empirical data using GNAT, a glycosylation network analysis tool. Our compilation of the network tool had 23 glycosyltransferase and glucosidase enzymes, and could infer the pathway of glycosylation machinery based on glycans identified in the virus spike protein. Once the glycan biosynthesis pathway was generated, we simulated the effect of blocking specific enzymes - Mannosidase-II and alpha-1,6-fucosyltransferase to see how they would affect the biosynthesis network.Results Of the 23 enzymes, a total of 12 were involved in glycosylation of SARS-CoV-2 - Man-Ia, MGAT1, MGAT2, MGAT4, MGAT5, B4GalT, B4GalT, Man II, SiaT, ST3GalI, ST3GalVI and FucT8. Blocking enzymes resulted in a substantially modified glycan profile of the protein.Conclusions A network analysis of N-glycan biosynthesis of SARS-CoV-2 spike protein shows an elaborate enzymatic pathway with several intermediate glycans, along with the ones identified by mass spectrometric studies. Variations in the final N-glycan profile of the virus, given its site-specific microheterogeneity, could be a factor in the host response to the infection and response to antibodies. Here we provide all the resources generated - the glycans derived from mass spectrometry and intermediate glycans in glycoCT xml format, and the biosynthesis network for future drug and vaccine development work.Competing Interest StatementThe authors have declared no competing interest.View Full Text

physiology↗

bioRxiv2019

Phenotyping women based on dietary macronutrients, physical activity and body weight using machine-learning tools

BackgroundNutritional phenotyping is a promising approach to achieve personalized nutrition. While conventional statistical approaches havent enabled personalizing well yet, machine-learning tools may offer solutions that havent been evaluated yet.\n\nObjectiveThe primary aim of this study was to use energy balance components - input (dietary energy intake and macronutrient composition), output (physical activity) to predict energy stores (body weight) as a way to evaluate their ability to identify potential phenotypes based on these parameters.\n\nMethodsWe obtained data from the Womens Health Initiative -Observational Study (WHI-OS) from BioLINCC. We chose dietary macronutrients - carbohydrate, protein, fats, fiber, sugars & physical activity variables - energy expended from mild, moderate and vigorous intensity activity h/wk to predict current body weight either numerically (as kg of body weight) or categorically (as BMI categories). Several machine-learning tools were used for this prediction - k-nearest neighbors (kNN), decision trees, neural networks (NN), Support Vector Machine (SVM) regressions and Random Forest. Further, predictive ability was refined using cluster analysis, in an effort to identify putative phenotypes.\n\nResultsFor the numerical predictions, kNN performed best (Mean Approximate Error (MAE) of 2.71kg, R2 of 0.92, Root mean square error (RMSE) of 4.96kg). For categorical prediction, ensemble trees (with nearest neighbor learner) performed best (93.8% accuracy). K-means cluster analysis identified 11 clusters suggestive of phenotypes, based on significantly improved predictive accuracy. Within clusters, individual macronutrient gain and loss modeling identified that some clusters were strongly predicted by dietary carbohydrate while others by dietary fat.\n\nConclusionsMachine-learning tools in nutritional epidemiology could be used to identify putative phenotypes.

epidemiology↗

bioRxiv2018

Timing between cortical slow oscillations and heart rate bursts during sleep predicts perceptual speed, but not offline consolidation

Central and autonomic nervous system activity are coupled during sleep. Cortical slow oscillations (SOs, <1Hz) coincide with brief bursts in heart rate (HR), but the functional consequence of this coupling in cognition remains elusive. We measured SO-HR temporal coupling (i.e., the peak-to-peak interval between downstate of SO event and HR burst) during a daytime nap, and asked whether this SO-HR timing measure was associated with perceptual speed and learning on a texture discrimination task, by testing subjects before and after a nap. The coherence of SO-HR events during sleep strongly correlated with an individuals perceptual speed in the morning and evening test sessions, but not with their change in performance after the nap (i.e., consolidation). We confirmed this result in two additional experimental visits, and also discovered that this association was visit-specific, indicating a reliable state (not trait) marker. Thus, we introduce a novel physiological index that may be a useful marker of state-dependent processing speed of an individual.\n\nSignificance StatementStudies show that autonomic and central nervous system activity is coupled. For example, increases in heart rate follow cortical slow oscillations during sleep. However, the functional significance of this coupling for cognition is not understood. In three experimental visits, we show that the timing between these sleep events (the peak-to-peak delay between the slow oscillation and the heart rate burst) is highly correlated with waking perceptual processing speed. This reliable individual difference measure may be a useful marker of generalized processing speed.

neuroscience↗

bioRxiv2017

Thalamocortical and Intracortical Laminar Connectivity Determines Sleep Spindle Properties

Spindle oscillations are brief oscillatory activity during non-rapid eye movement (NREM) sleep. Spindle density and synchronization properties are different in MEG versus EEG recordings in humans and also vary with learning performance, suggesting spindle involvement in memory consolidation. Using computational models, we identified network mechanisms that may explain differences in spindle properties across cortical structures. First, we report that differences in spindle occurrence between MEG and EEG data may arise from the properties of the core vs. matrix thalamocortical systems. The matrix system, projecting superficially, has wider thalamocortical fanout compared to the core system, projecting to the middle layers, and requires the recruitment of a larger population of neurons to initiate a spindle. Our model demonstrates that this property is sufficient to explain lower spindle density and higher spatial synchrony of spindles in the superficial cortical layers, as observed in the EEG signal. In contrast, spindles in the core system occurred more frequently but less synchronously, as observed in the MEG recordings. Futhermore, consistent with human recordings, in the model, spindles occurred independently in the core system but matrix system spindles commonly co-occurred with core one. We found that the intracortical excitatory connections from layer III/IV to layer V promote spindle propagation from the core to the matrix system, leading to widespread spindle activity. Our study predicts that plasticity of the intra and inter cortical connectivity can potentially be a mechanism for increasing in spindle density as observed during learning.\n\nAuthor summaryThe density of sleep spindles has been shown to correlate with memory consolidation. Further, sleep spindles occur more often in human MEG than EEG. We developed thalamocortical network model that is capable of spontaneous generation of spindles across cortical layers and that captures the essential statistical features of spindles observed in experiments. We predict that differences in thalamo-cortical connectivity, known from anatomical studies, lead to the differences in the spindle properties between EEG and MEG as observed in human recordings. Further, we predict that the intracortical connectivity between cortical layers, a property influenced by sleep preceding learning, increases spindle density. Results from our study highlight the role of cortical and thalamic projections on the occurrence and properties of spindles.

neuroscience↗

bioRxiv2017

Origin of Slow Spontaneous Resting-State Neuronal Fluctuations in Brain Networks

Resting or baseline state low frequency (0.01-0.2 Hz) brain activity has been observed in fMRI, EEG and LFP recordings. These fluctuations were found to be correlated across brain regions, and are thought to reflect neuronal activity fluctuations between functionally connected areas of the brain. However, the origin of these infra-slow fluctuations remains unknown. Here, using a detailed computational model of the brain network, we show that spontaneous infra-slow (< 0.05 Hz) fluctuations could originate due to the ion concentration dynamics. The computational model implemented dynamics for intra and extracellular K+ and Na+ and intracellular Cl- ions, Na+/K+ exchange pump, and KCC2 co-transporter. In the network model representing resting awake-like brain state, we observed slow fluctuations in the extracellular K+ concentration, Na+/K+ pump activation, firing rate of neurons and local field potentials. Holding K+ concentration constant prevented generation of these fluctuations. The amplitude and peak frequency of this activity were modulated by Na+/K+ pump, AMPA/GABA synaptic currents and glial properties. Further, in a large-scale network with long-range connections based on CoCoMac connectivity data, the infra-slow fluctuations became synchronized among remote clusters similar to the resting-state networks observed in vivo. Overall, our study proposes that ion concentration dynamics mediated by neuronal and glial activity may contribute to the generation of very slow spontaneous fluctuations of brain activity that are observed as the resting-state fluctuations in fMRI and EEG recordings.

neuroscience↗

bioRxiv2017

Role of KCC2-Dependent Potassium Efflux in 4-Aminopyridine-Induced Epileptiform Synchronization

A balance between excitation and inhibition is required to maintain stable brain network dynamics. Traditionally, seizure activity is believed to arise from the breakdown of this delicate balance in favor of excitation with loss of inhibition. Surprisingly, recent experimental evidence suggests that this conventional view may be untrue, and that inhibition plays a prominent role in the development of epileptiform synchronization. Here, we explored the role of the co-transporter KCC2 in the onset of inhibitory network-induced seizures. Our experiments in acute mouse brain slices of either sex revealed that optogenetic stimulation of either parvalbumin- or somatostatin-expressing interneurons induced ictal discharges in rodent entorhinal cortex during 4-aminopyridine application. These data point to a proconvulsive role of GABAA receptor signaling that is independent of the inhibitory input location (i.e., dendritic vs. somatic). Further, we developed a biophysically realistic network model implementing complex dynamics of the ion concentrations to explore the mechanisms leading to inhibitory network-induced seizures. In agreement with experimental results, we found that stimulation of inhibitory interneurons induced seizure-like activity in a network with reduced potassium A-current. Model predicted that interneuron stimulation triggered interneuron firing that was accompanied by an increase in intracellular chloride and a subsequent KCC2-dependent gradual accumulation of extracellular potassium promoting epileptiform ictal activity. When the KCC2 activity was reduced, stimulation of the interneurons was no longer able to induce ictal events. Overall, our study provides evidence for a proconvulsive role of GABAA receptor signaling that depends on the involvement of the KCC2 co-transporter.

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↗