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Krishnan, G.

Publications and source records attributed to Krishnan, G..

3 recordsLinked to original sources

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

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

Coupling of autonomic and central events during sleep boosts declarative memory consolidation

While anatomical pathways between forebrain cognitive and brainstem autonomic nervous centers are well defined, autonomic-central interactions during sleep and their contribution to waking performance are not understood. Here, we analyzed simultaneous central activity via electroencephalography (EEG) and autonomic heart beat-to-beat intervals (RR intervals) from electrocardiography (ECG) during wake and daytime sleep. We identified bursts of ECG activity that lasted 4-5 seconds and predominated in non-rapid-eye-movement sleep (NREM). Using event-based analysis of NREM sleep, we found an increase in delta (0.5-4Hz) and sigma (12-15Hz) power and an elevated density of slow oscillations (0.5-1Hz) about 5 secs prior to peak of the heart rate burst, as well as a surge in vagal activity, assessed by high-frequency (HF) component of RR intervals. Using regression framework, we show that these Autonomic/Central Events (ACE) positively predicted post-nap improvement in a declarative memory task after controlling for the effects of spindles and slow oscillations from sleep periods without ACE. No such relation was found between memory performance and a control nap. Additionally, NREM ACE negatively correlated with REM sleep and learning in a non-declarative memory task. These results provide the first evidence that coordinated autonomic and central events play a significant role in declarative memory consolidation.

neuroscience