Spatiotemporal Patterns of Adaptation-Induced Slow Oscillations in a Whole-Brain Model of Slow-Wave Sleep.
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ABSTRACT:
SUBMITTER: Cakan C
PROVIDER: S-EPMC8790481 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Spatial memory, among many other brain processes, shows hemispheric lateralization. Most of the published evidence suggests that the right hippocampus plays a leading role in the manipulation of spatial information. Concurrently in the hippocampus, memory consolidation during sleep periods is one of the key steps in the formation of newly acquired spatial memory traces. One of the most characteristic oscillatory patterns in the hippocampus are sharp-wave ripple (SWR) complexes. Within this complex, fast-field oscillations or ripples have been demonstrated to be instrumental in the memory consolidation process. Since these ripples are relevant for the consolidation of memory traces associated with spatial navigation, and this process appears to be lateralized, we hypothesize that ripple events between both hippocampi would exhibit different temporal dynamics. We tested this idea by using a modified "split-hyperdrive" that allows us to record simultaneous LFPs from both right and left hippocampi of Sprague-Dawley rats during sleep. We detected individual events and found that during sleep periods these ripples exhibited a different occurrence patterns between hemispheres. Most ripple events were synchronous between intra- rather than inter-hemispherical recordings, suggesting that ripples in the hippocampus are independently generated and locally propagated within a specific hemisphere. In this study, we propose the ripples' lack of synchrony between left and right hippocampi as the putative physiological mechanism underlying lateralization of spatial memory.
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Project description: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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Project description:Recent research has found that the human sleep cycle is characterised by changes in spatiotemporal patterns of brain activity. Yet, we are still missing a mechanistic explanation of the local neuronal dynamics underlying these changes. We used whole-brain computational modelling to study the differences in global brain functional connectivity and synchrony of fMRI activity in healthy humans during wakefulness and slow-wave sleep. We applied a whole-brain model based on the normal form of a supercritical Hopf bifurcation and studied the dynamical changes when adapting the bifurcation parameter for all brain nodes to best match wakefulness and slow-wave sleep. Furthermore, we analysed differences in effective connectivity between the two states. In addition to significant changes in functional connectivity, synchrony and metastability, this analysis revealed a significant shift of the global dynamic working point of brain dynamics, from the edge of the transition between damped to sustained oscillations during wakefulness, to a stable focus during slow-wave sleep. Moreover, we identified a significant global decrease in effective interactions during slow-wave sleep. These results suggest a mechanism for the empirical functional changes observed during slow-wave sleep, namely a global shift of the brain's dynamic working point leading to increased stability and decreased effective connectivity.
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Project description:Slow oscillations, a defining characteristic of the nonrapid eye movement sleep electroencephalogram (EEG), proliferate across the scalp in highly reproducible patterns. In adults, the propagation of slow oscillations is a recognized fingerprint of brain connectivity and excitability. In this study, we (1) describe for the first time maturational features of sleep slow oscillation propagation in children (n = 23; 2-13 years) using high-density (hd) EEG and (2) examine associations between sleep slow oscillatory propagation characteristics (ie, distance, traveling speed, cortical involvement) and white matter myelin microstructure as measured with multicomponent Driven Equilibrium Single Pulse Observation of T1 and T2-magnetic resonance imaging (mcDESPOT-MRI). Results showed that with increasing age, slow oscillations propagated across longer distances (average growth of 0.2 cm per year; R(21) = 0.50, p < .05), while traveling speed and cortical involvement (ie, slow oscillation expanse) remained unchanged across childhood. Cortical involvement (R(20) = 0.44) and slow oscillation speed (R(20) = -0.47; both p < .05, corrected for age) were associated with myelin content in the superior longitudinal fascicle, the largest anterior-posterior, intrahemispheric white matter connectivity tract. Furthermore, slow oscillation distance was moderately associated with whole-brain (R(21) = 0.46, p < .05) and interhemispheric myelin content, the latter represented by callosal myelin water fraction (R(21) = 0.54, p < .01, uncorrected). Thus, we demonstrate age-related changes in slow oscillation propagation distance, as well as regional associations between brain activity during sleep and the anatomical connectivity of white matter microstructure. Our findings make an important contribution to knowledge of the brain connectome using a noninvasive and novel analytic approach. These data also have implications for understanding the emergence of neurodevelopmental disorders and the role of sleep in brain maturation trajectories.
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Project description:Study objectiveIn humans sleep slow wave activity (SWA) declines during adolescence. It has been suggested that this decline reflects the elimination of cortical synapses, but this hypothesis has never been tested directly.DesignWe focused on mouse frontal cortex and collected data from early adolescence (?postnatal day 20, P20) to adulthood (P60) of (1) SWA; (2) expression of synapsin I, a presynaptic marker; and (3) number of dendritic spines in layers I-II.SettingBasic sleep research laboratory.Patients or participantsYFP-line H mice (n = 70; P15-87, all males) and GFP-line S mice (n = 14; P17-60, 8 females) were used for EEG recording. Forty-five YFP mice (P19-119, 12 females) and 42 GFP-S mice (P20-60, 14 females) were used for in vivo 2-photon imaging and ex vivo confocal microscopy, respectively. Other YGP mice (n = 57, P10-77) were used for western blot analysis of synapsin I.InterventionsN/A.Measurements and resultsAs in humans, SWA in mice declined from early adolescence to adulthood. Synapsin I levels increased from P10 to P24, with little change afterwards. Mean spine density in apical dendrites of layer V pyramidal neurons (YFP-H) showed no change from P20 to P60. Spine number in layers I-II apical dendrites, belonging to layer III and V pyramidal neurons (GFP-S), increased slightly from P20 to P30 and decreased from P30 to P60; smaller spines decreased in number from P20 to P60, while bigger spines increased.ConclusionsIn mice, it is unlikely that the developmental decrease in SWA can be accounted for by a net pruning of cortical synapses.
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Project description:We investigated whether the benefit of slow wave sleep (SWS) for memory consolidation typically observed in healthy individuals is disrupted in people with accelerated long-term forgetting (ALF) due to epilepsy. SWS is thought to play an active role in declarative memory in healthy individuals and, furthermore, electrographic epileptiform activity is often more prevalent during SWS than during wakefulness or other sleep stages. We studied the relationship between SWS and the benefit of sleep for memory retention using a word-pair associates task. In both the ALF and the healthy control groups, sleep conferred a memory benefit. However, the relationship between the amount of SWS and sleep-related memory benefits differed significantly between the groups. In healthy participants, the amount of SWS correlated positively with sleep-related memory benefits. In stark contrast, the more SWS, the smaller the sleep-related memory benefit in the ALF group. Therefore, contrary to its role in healthy people, SWS-associated brain activity appears to be deleterious for memory in patients with ALF.
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Project description:Spontaneous synchronization over large networks is ubiquitous in nature, ranging from inanimate to biological systems. In the human brain, neuronal synchronization and de-synchronization occur during sleep, with the greatest degree of neuronal synchronization during slow wave sleep (SWS). The current sleep classification schema is based on electroencephalography and provides common criteria for clinicians and researchers to describe stages of non-rapid eye movement (NREM) sleep as well as rapid eye movement (REM) sleep. These sleep stage classifications have been based on convenient heuristic criteria, with little consideration of the accompanying normal physiological changes across those same sleep stages. To begin to resolve those inconsistencies, first focusing only on NREM sleep, we propose a simple cluster synchronization model to explain the emergence of SWS in healthy people without sleep disorders. We apply the empirical mode decomposition (EMD) analysis to quantify slow wave activity in electroencephalograms, and provide quantitative evidence to support our model. Based on this synchronization model, NREM sleep can be classified as SWS and non-SWS, such that NREM sleep can be considered as an intrinsically bistable process. Finally, we develop an automated algorithm for SWS classification. We show that this new approach can unify brain wave dynamics and their corresponding physiologic changes.
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Project description:BackgroundSlow wave sleep (SWS) plays an important role in neurophysiologic restoration. Experimentally testing the effect of SWS disruption previously required highly time-intensive and subjective methods. Our goal was to develop an automated and objective protocol to reduce SWS without affecting sleep architecture.New methodWe developed a custom Matlab™ protocol to calculate electroencephalogram spectral power every 10s live during a polysomnogram, exclude artifact, and, if measurements met criteria for SWS, deliver increasingly louder tones through earphones. Middle-aged healthy volunteers (n=10) each underwent 2 polysomnograms, one with the SWS disruption protocol and one with sham condition.ResultsThe SWS disruption protocol reduced SWS compared to sham condition, as measured by spectral power in the delta (0.5-4Hz) band, particularly in the 0.5-2Hz range (mean 20% decrease). A compensatory increase in the proportion of total spectral power in the theta (4-8Hz) and alpha (8-12Hz) bands was seen, but otherwise normal sleep features were preserved. N3 sleep decreased from 20±34 to 3±6min, otherwise there were no significant changes in total sleep time, sleep efficiency, or other macrostructural sleep characteristics.Comparison with existing methodThis novel SWS disruption protocol produces specific reductions in delta band power similar to existing methods, but has the advantage of being automated, such that SWS disruption can be performed easily in a highly standardized and operator-independent manner.ConclusionThis automated SWS disruption protocol effectively reduces SWS without impacting overall sleep architecture.
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