scholarly journals Attractor competition enriches cortical dynamics during awakening from anesthesia

2019 ◽  
Author(s):  
Núria Tort-Colet ◽  
Cristiano Capone ◽  
María V. Sanchez-Vives ◽  
Maurizio Mattia
Keyword(s):  
Animal Species ◽  
Slow Wave Sleep ◽  
Abrupt Onset ◽  
Rate Model ◽  
Cortical Dynamics ◽  
Slow Oscillations ◽  
Low Degree ◽  
Open Window ◽  
Maximum Regularity ◽  
Population Rate

AbstractCortical slow oscillations (≲ 1 Hz) are a hallmark of slow-wave sleep and deep anesthesia across animal species. They arise from spatiotemporal patterns of activity with low degree of complexity, eventually increasing as wakefulness is approached and cognitive functions emerge. The arousal process is then an open window on the widely unknown mechanisms underlying the emergence of the dynamical richness of awake cortical networks. Here, we investigated the changes in the network dynamics as anesthesia fades out and wakefulness is approached in layer 5 neuronal assemblies of the rat visual cortex. Far from being a continuum, this transition displays both gradual and abrupt activity changes. Starting from deep anesthesia, slow oscillations increase their frequency eventually expressing maximum regularity. This stage is followed by the abrupt onset of an infra-slow (~ 0.2 Hz) alternation between sleep-like oscillations and activated states. A population rate model reproduces this transition driven by an increased excitability that brings it to periodically cross a critical point. We conclude that dynamical richness emerges as a competition between two metastable attractor states whose existence is here experimentally confirmed.

scholarly journals Selection of stimulus parameters for enhancing slow wave sleep events with a Neural-field theory thalamocortical computational model

2021 ◽  
Author(s):  
Felipe A. Torres ◽  
Patricio Orio ◽  
María-José Escobar
Keyword(s):  
Computational Model ◽  
Slow Wave ◽  
Memory Consolidation ◽  
Closed Loop ◽  
Slow Wave Sleep ◽  
Brain Activity ◽  
Sensory Stimulation ◽  
Sleep Spindles ◽  
Slow Oscillations ◽  
Closed Loop Stimulation

AbstractSlow-wave sleep cortical brain activity, conformed by slow-oscillations and sleep spindles, plays a key role in memory consolidation. The increase of the power of the slow-wave events, obtained by auditory sensory stimulation, positively correlates to memory consolidation performance. However, little is known about the experimental protocol maximizing this effect, which could be induced by the power of slow-oscillation, the number of sleep spindles, or the timing of both events’ co-occurrence. Using a mean-field model of thalamocortical activity, we studied the effect of several stimulation protocols, varying the pulse shape, duration, amplitude, and frequency, as well as a target-phase using a closed-loop approach. We evaluated the effect of these parameters on slow-oscillations (SO) and sleep-spindles (SP), considering: (i) the power at the frequency bands of interest, (ii) the number of SO and SP, (iii) co-occurrences between SO and SP, and (iv) synchronization of SP with the up-peak of the SO. The first three targets are maximized using a decreasing ramp pulse with a pulse duration of 50 ms. Also, we observed a reduction in the number of SO when increasing the stimulus energy by rising its amplitude. To assess the target-phase parameter, we applied closed-loop stimulation at 0º, 45º, and 90º of the phase of the narrow-band filtered ongoing activity, at 0.85 Hz as central frequency. The 0º stimulation produces better results in the power and number of SO and SP than the rhythmic or aleatory stimulation. On the other hand, stimulating at 45º or 90º change the timing distribution of spindles centers but with fewer co-occurrences than rhythmic and 0º phase. Finally, we propose the application of closed-loop stimulation at the rising zero-cross point using pulses with a decreasing ramp shape and 50 ms of duration for future experimental work.Author summaryDuring the non-REM (NREM) phase of sleep, events that are known as slow oscillations (SO) and spindles (SP) can be detected by EEG. These events have been associated with the consolidation of declarative memories and learning. Thus, there is an ongoing interest in promoting them during sleep by non-invasive manipulations such as sensory stimulation. In this paper, we used a computational model of brain activity that generates SO and SP, to investigate which type of sensory stimulus –shape, amplitude, duration, periodicity– would be optimal for increasing the events’ frequency and their co-occurrence. We found that a decreasing ramp of 50 ms duration is the most effective. The effectiveness increases when the stimulus pulse is delivered in a closed-loop configuration triggering the pulse at a target phase of the ongoing SO activity. A desirable secondary effect is to promote SPs at the rising phase of the SO oscillation.

scholarly journals The state of somatosensory cortex during neuromodulation

2012 ◽  
Vol 108 (4) ◽  
pp. 1010-1024 ◽  
Author(s):  
Morgana Favero ◽  
Gladis Varghese ◽  
Manuel A. Castro-Alamancos
Keyword(s):  
Somatosensory Cortex ◽  
Slow Wave Sleep ◽  
Slow Oscillations ◽  
Synaptic Inputs ◽  
Sensory Inputs ◽  
Activated State ◽  
Cellular Excitability ◽  
Inhibitory Conductance ◽  
State Changes ◽  
Cholinergic Activation

During behavioral quiescence, such as slow-wave sleep and anesthesia, the neocortex is in a deactivated state characterized by the presence of slow oscillations. During arousal, slow oscillations are absent and the neocortex is in an activated state that greatly impacts information processing. Neuromodulators acting in neocortex are believed to mediate these state changes, but the mechanisms are poorly understood. We investigated the actions of noradrenergic and cholinergic activation on slow oscillations, cellular excitability, and synaptic inputs in thalamocortical slices of somatosensory cortex. The results show that neuromodulation abolishes slow oscillations, dampens the excitability of principal cells, and rebalances excitatory and inhibitory synaptic inputs in thalamocortical-recipient layers IV–III. Sensory cortex is much more selective about the inputs that can drive it. The source of neuromodulation is critically important in determining this selectivity. Cholinergic activation suppresses the excitatory and inhibitory conductances driven by thalamocortical and intracortical inputs. Noradrenergic activation suppresses the excitatory conductance driven by intracortical inputs but not by thalamocortical inputs and enhances the inhibitory conductance driven by thalamocortical inputs but not by intracortical inputs. Thus noradrenergic activation emphasizes thalamocortical (sensory) inputs relative to intracortical inputs, while cholinergic activation suppresses both.

scholarly journals Spatiotemporal Patterns of Adaptation-Induced Slow Oscillations in a Whole-Brain Model of Slow-Wave Sleep

2022 ◽  
Vol 15 ◽  
Author(s):  
Caglar Cakan ◽  
Cristiana Dimulescu ◽  
Liliia Khakimova ◽  
Daniela Obst ◽  
Agnes Flöel ◽  
...  
Keyword(s):  
Slow Wave ◽  
Traveling Waves ◽  
Large Scale ◽  
Slow Wave Sleep ◽  
Brain Area ◽  
Optimization Approach ◽  
Whole Brain ◽  
Slow Oscillations ◽  
Lower Amplitude ◽  
The Brain

During slow-wave sleep, the brain is in a self-organized regime in which slow oscillations (SOs) between up- and down-states travel across the cortex. While an isolated piece of cortex can produce SOs, the brain-wide propagation of these oscillations are thought to be mediated by the long-range axonal connections. We address the mechanism of how SOs emerge and recruit large parts of the brain using a whole-brain model constructed from empirical connectivity data in which SOs are induced independently in each brain area by a local adaptation mechanism. Using an evolutionary optimization approach, good fits to human resting-state fMRI data and sleep EEG data are found at values of the adaptation strength close to a bifurcation where the model produces a balance between local and global SOs with realistic spatiotemporal statistics. Local oscillations are more frequent, last shorter, and have a lower amplitude. Global oscillations spread as waves of silence across the undirected brain graph, traveling from anterior to posterior regions. These traveling waves are caused by heterogeneities in the brain network in which the connection strengths between brain areas determine which areas transition to a down-state first, and thus initiate traveling waves across the cortex. Our results demonstrate the utility of whole-brain models for explaining the origin of large-scale cortical oscillations and how they are shaped by the connectome.

scholarly journals Selection of stimulus parameters for enhancing slow wave sleep events with a neural-field theory thalamocortical model

2021 ◽  
Vol 17 (7) ◽  
pp. e1008758
Author(s):  
Felipe A. Torres ◽  
Patricio Orio ◽  
María-José Escobar
Keyword(s):  
Slow Wave ◽  
Memory Consolidation ◽  
Closed Loop ◽  
Slow Wave Sleep ◽  
Brain Activity ◽  
Sleep Spindles ◽  
Mean Field Model ◽  
Cross Point ◽  
Slow Oscillations ◽  
Closed Loop Stimulation

Slow-wave sleep cortical brain activity, conformed by slow-oscillations and sleep spindles, plays a key role in memory consolidation. The increase of the power of the slow-wave events, obtained by auditory sensory stimulation, positively correlates to memory consolidation performance. However, little is known about the experimental protocol maximizing this effect, which could be induced by the power of slow-oscillation, the number of sleep spindles, or the timing of both events’ co-occurrence. Using a mean-field model of thalamocortical activity, we studied the effect of several stimulation protocols, varying the pulse shape, duration, amplitude, and frequency, as well as a target-phase using a closed-loop approach. We evaluated the effect of these parameters on slow-oscillations (SO) and sleep-spindles (SP), considering: (i) the power at the frequency bands of interest, (ii) the number of SO and SP, (iii) co-occurrences between SO and SP, and (iv) synchronization of SP with the up-peak of the SO. The first three targets are maximized using a decreasing ramp pulse with a pulse duration of 50 ms. Also, we observed a reduction in the number of SO when increasing the stimulus energy by rising its amplitude. To assess the target-phase parameter, we applied closed-loop stimulation at 0°, 45°, and 90° of the phase of the narrow-band filtered ongoing activity, at 0.85 Hz as central frequency. The 0° stimulation produces better results in the power and number of SO and SP than the rhythmic or random stimulation. On the other hand, stimulating at 45° or 90° change the timing distribution of spindles centers but with fewer co-occurrences than rhythmic and 0° phase. Finally, we propose the application of closed-loop stimulation at the rising zero-cross point using pulses with a decreasing ramp shape and 50 ms of duration for future experimental work.

scholarly journals UP-DOWN cortical dynamics reflect state transitions in a bistable network

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Daniel Jercog ◽  
Alex Roxin ◽  
Peter Barthó ◽  
Artur Luczak ◽  
Albert Compte ◽  
...  
Keyword(s):  
High Amplitude ◽  
State Transitions ◽  
Rate Model ◽  
Neuronal Circuits ◽  
Population Activity ◽  
Cortical Dynamics ◽  
Spiking Network ◽  
Bistable Regime ◽  
The Brain ◽  
Low Rate

In the idling brain, neuronal circuits transition between periods of sustained firing (UP state) and quiescence (DOWN state), a pattern the mechanisms of which remain unclear. Here we analyzed spontaneous cortical population activity from anesthetized rats and found that UP and DOWN durations were highly variable and that population rates showed no significant decay during UP periods. We built a network rate model with excitatory (E) and inhibitory (I) populations exhibiting a novel bistable regime between a quiescent and an inhibition-stabilized state of arbitrarily low rate. Fluctuations triggered state transitions, while adaptation in E cells paradoxically caused a marginal decay of E-rate but a marked decay of I-rate in UP periods, a prediction that we validated experimentally. A spiking network implementation further predicted that DOWN-to-UP transitions must be caused by synchronous high-amplitude events. Our findings provide evidence of bistable cortical networks that exhibit non-rhythmic state transitions when the brain rests.

scholarly journals Hippocampal Sharp Wave-Ripples Linked to Slow Oscillations in Rat Slow-Wave Sleep

2006 ◽  
Vol 96 (1) ◽  
pp. 62-70 ◽  
Author(s):  
Matthias Mölle ◽  
Oxana Yeshenko ◽  
Lisa Marshall ◽  
Susan J. Sara ◽  
Jan Born
Keyword(s):  
Slow Wave ◽  
Slow Wave Sleep ◽  
Temporal Dynamics ◽  
Slow Oscillation ◽  
Network Activity ◽  
Event Correlation ◽  
Slow Oscillations ◽  
Sharp Wave ◽  
Neuronal Network Activity ◽  
Up And Down States

Slow oscillations originating in the prefrontal neocortex during slow-wave sleep (SWS) group neuronal network activity and thereby presumably support the consolidation of memories. Here, we investigated whether the grouping influence of slow oscillations extends to hippocampal sharp wave-ripple (SPW) activity thought to underlie memory replay processes during SWS. The prefrontal surface EEG and multiunit activity (MUA), along with hippocampal local field potentials (LFP) from CA1, were recorded in rats during sleep. Average spindle and ripple activity and event correlation histograms of SPWs were calculated, time-locked to half-waves of slow oscillations. Results confirm decreased prefrontal MUA and spindle activity during EEG slow oscillation negativity and increases in this activity during subsequent positivity. A remarkably close temporal link was revealed between slow oscillations and hippocampal activity, with ripple activity and SPWs being also distinctly decreased during negative half-waves and increased during slow oscillation positivity. Fine-grained analyses of temporal dynamics revealed for the slow oscillation a phase delay of approximately 90 ms with reference to up and down states of prefrontal MUA, and of only approximately 60 ms with reference to changes in SPWs, indicating that up and down states in prefrontal MUA precede corresponding changes in hippocampal SPWs by approximately 30 ms. Results support the notion that the depolarizing surface-positive phase of the slow oscillation and the associated up state of prefrontal excitation promotes hippocampal SPWs via efferent pathways. The preceding disfacilitation of hippocampal events temporally coupled to the negative slow oscillation half-wave appears to serve a synchronizing role in this neocorticohippocampal interplay.

scholarly journals UP-DOWN cortical dynamics reflect state transitions in a bistable balanced network

2016 ◽  
Author(s):  
Daniel Jercog ◽  
Alex Roxin ◽  
Peter Barthó ◽  
Artur Luczak ◽  
Albert Compte ◽  
...  
Keyword(s):  
Somatosensory Cortex ◽  
State Transitions ◽  
Quiescent State ◽  
Population Activity ◽  
Cortical Dynamics ◽  
Underlying Mechanisms ◽  
The Brain ◽  
Population Rate ◽  
Low Rate ◽  
Balanced Network

AbstractIn the idling brain, neuronal circuits often exhibit transitions between periods of sustained firing (UP state) and quiescence (DOWN state). Although these dynamics occur across multiple areas and behavioral conditions, the underlying mechanisms remain unclear. Here we analyze spontaneous population activity from the somatosensory cortex of urethane-anesthetized rats. We find that UP and DOWN periods are variable (i.e. non-rhythmic) and that the population rate shows no significant decay during UP periods. We build a network model of excitatory (E) and inhibitory (I) neurons that exhibits a new bistability between a quiescent state and a balanced state of arbitrarily low rate. Fluctuating inputs trigger state transitions. Adaptation in E cells paradoxically causes a marginal decay of E-rate but a marked decay of I-rate, a signature of balanced bistability that we validate experimentally. Our findings provide evidence of a bistable balanced network that exhibits non-rhythmic state transitions when the brain rests.

scholarly journals High-voltage, diffuse delta rhythms coincide with wakeful consciousness and complexity in Angelman syndrome

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Joel Frohlich ◽  
Lynne M Bird ◽  
John Dell’Italia ◽  
Micah A Johnson ◽  
Joerg F Hipp ◽  
...  
Keyword(s):  
High Voltage ◽  
Angelman Syndrome ◽  
Cortical Neurons ◽  
Slow Wave Sleep ◽  
Epileptic Seizures ◽  
High Amplitude ◽  
Relative Level ◽  
Eeg Activity ◽  
Cortical Dynamics ◽  
Volitional Behavior

Abstract Abundant evidence from slow wave sleep, anesthesia, coma, and epileptic seizures links high-voltage, slow electroencephalogram (EEG) activity to loss of consciousness. This well-established correlation is challenged by the observation that children with Angelman syndrome (AS), while fully awake and displaying volitional behavior, display a hypersynchronous delta (1–4 Hz) frequency EEG phenotype typical of unconsciousness. Because the trough of the delta oscillation is associated with down-states in which cortical neurons are silenced, the presence of volitional behavior and wakefulness in AS amidst diffuse delta rhythms presents a paradox. Moreover, high-voltage, slow EEG activity is generally assumed to lack complexity, yet many theories view functional brain complexity as necessary for consciousness. Here, we use abnormal cortical dynamics in AS to assess whether EEG complexity may scale with the relative level of consciousness despite a background of hypersynchronous delta activity. As characterized by multiscale metrics, EEGs from 35 children with AS feature significantly greater complexity during wakefulness compared with sleep, even when comparing the most pathological segments of wakeful EEG to the segments of sleep EEG least likely to contain conscious mentation and when factoring out delta power differences across states. These findings (i) warn against reverse inferring an absence of consciousness solely on the basis of high-amplitude EEG delta oscillations, (ii) corroborate rare observations of preserved consciousness under hypersynchronization in other conditions, (iii) identify biomarkers of consciousness that have been validated under conditions of abnormal cortical dynamics, and (iv) lend credence to theories linking consciousness with complexity.

scholarly journals High-Resolution Spectral Sleep Analysis Reveals a Novel Association Between Slow Oscillations and Memory Retention in Elderly Adults

2021 ◽  
Vol 12 ◽  
Author(s):  
Makoto Kawai ◽  
Logan D. Schneider ◽  
Omer Linkovski ◽  
Josh T. Jordan ◽  
Rosy Karna ◽  
...  
Keyword(s):  
Older Adults ◽  
Slow Wave ◽  
Verbal Memory ◽  
Healthy Aging ◽  
Slow Wave Sleep ◽  
Wave Activity ◽  
Slow Wave Activity ◽  
Medium Effect ◽  
Memory Retention ◽  
Slow Oscillations

Objective: In recognition of the mixed associations between traditionally scored slow wave sleep and memory, we sought to explore the relationships between slow wave sleep, electroencephalographic (EEG) power spectra during sleep and overnight verbal memory retention in older adults.Design, Setting, Participants, and Measurements: Participants were 101 adults without dementia (52% female, mean age 70.3 years). Delayed verbal memory was first tested in the evening prior to overnight polysomnography (PSG). The following morning, subjects were asked to recall as many items as possible from the same List (overnight memory retention; OMR). Partial correlation analyses examined the associations of delayed verbal memory and OMR with slow wave sleep (SWS) and two physiologic EEG slow wave activity (SWA) power spectral bands (0.5–1 Hz slow oscillations vs. 1–4 Hz delta activity).Results: In subjects displaying SWS, SWS was associated with enhanced delayed verbal memory, but not with OMR. Interestingly, among participants that did not show SWS, OMR was significantly associated with a higher slow oscillation relative power, during NREM sleep in the first ultradian cycle, with medium effect size.Conclusions: These findings suggest a complex relationship between SWS and memory and illustrate that even in the absence of scorable SWS, older adults demonstrate substantial slow wave activity. Further, these slow oscillations (0.5–1 Hz), in the first ultradian cycle, are positively associated with OMR, but only in those without SWS. Our findings raise the possibility that precise features of slow wave activity play key roles in maintaining memory function in healthy aging. Further, our results underscore that conventional methods of sleep evaluation may not be sufficiently sensitive to detect associations between SWA and memory in older adults.

scholarly journals Up and Down States During Slow Oscillations in Slow-Wave Sleep and Different Levels of Anesthesia

2021 ◽  
Vol 15 ◽  
Author(s):  
Melody Torao-Angosto ◽  
Arnau Manasanch ◽  
Maurizio Mattia ◽  
Maria V. Sanchez-Vives
Keyword(s):  
Slow Wave ◽  
Small Area ◽  
Slow Wave Sleep ◽  
Dynamic Regime ◽  
Network Activity ◽  
Oscillatory Regime ◽  
Slow Oscillations ◽  
State Duration ◽  
Up And Down States ◽  
Different Levels

Slow oscillations are a pattern of synchronized network activity generated by the cerebral cortex. They consist of Up and Down states, which are periods of activity interspersed with periods of silence, respectively. However, even when this is a unique dynamic regime of transitions between Up and Down states, this pattern is not constant: there is a range of oscillatory frequencies (0.1–4 Hz), and the duration of Up vs. Down states during the cycles is variable. This opens many questions. Is there a constant relationship between the duration of Up and Down states? How much do they vary across conditions and oscillatory frequencies? Are there different sub regimes within the slow oscillations? To answer these questions, we aimed to explore a concrete aspect of slow oscillations, Up and Down state durations, across three conditions: deep anesthesia, light anesthesia, and slow-wave sleep (SWS), in the same chronically implanted rats. We found that light anesthesia and SWS have rather similar properties, occupying a small area of the Up and Down state duration space. Deeper levels of anesthesia occupy a larger region of this space, revealing that a large variety of Up and Down state durations can emerge within the slow oscillatory regime. In a network model, we investigated the network parameters that can explain the different points within our bifurcation diagram in which slow oscillations are expressed.

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