Biology subjects

Mednick, S. C.

Publications and source records attributed to Mednick, S. C..

14 recordsLinked to original sources

bioRxiv2024

Predictive Modeling of Sleep Slow Oscillation Emergence on the electrode manifold: Toward Personalized Closed-Loop Brain Stimulation

BackgroundSleep slow oscillations (SOs), characteristic of NREM sleep, are causally tied to cognitive outcomes and the health-promoting homeostatic functions of sleep. Due to these known benefits, brain stimulation techniques aiming to enhance SOs are being developed, with great potential to contribute to clinical interventions, as they hold promise for improving sleep functions in populations with identified SO deficits (e.g., mild cognitive impairment). SO-targeting closed-loop stimulation protocols currently strive to identify SO occurrences in real time, a computationally intensive step that can lead to reduced precision (compared to post-hoc detection). These approaches are also often limited to focusing on only one electrode location, thus inherently precluding targeting of SOs that is informed by the overall organization of SOs in space-time. Prediction of SO emergence across the electrode manifold would establish an alternative to online detection, thus greatly advancing the development of personalized and flexible brain stimulation paradigms. This study presents a computational model that predicts SO occurrences at multiple locations across a night of sleep. In combination with our previous study on optimizing brain stimulation protocols using the spatiotemporal properties of SOs, this model contributes to increasing the accuracy of SO targeting in brain stimulation applications. MethodsSOs were detected in a dataset of nighttime sleep of 22 subjects (9 females), acquired with polysomnography including 64 EEG channels. Modeling of SO occurrence was achieved for SOs in stage N3, or in a combination of stages N2 and N3 (N2&N3). We study SO emergence at progressively more refined time scales. First, the cumulative SO occurrences in successive sleep cycles were successfully fit with exponentials. Secondly, the SO timing in each individual was modeled with a renewal point process. Using an inverse Gaussian model, we estimated the probability density function of SO timing and its parameters (mean) and {lambda} (shape, representing skewness) in successive cycles. ResultsWe observed a declining trend in the SO count across sleep cycles, which we modeled using a power law relationship. The decay rate per cycle was 1.473 for N3 and 1.139 for N2&N3, with variances of the decay rates across participants being 1 and 0.53, respectively. This pattern mirrors the declining trend of slow wave activity (SWA) across sleep cycles, likely due to the inherent relationship between SWA and SO. Additionally, the SO timing model for N3 showed an increasing trend in the model parameters (, {lambda}) across cycles. The increase rate per cycle followed a power law relationship with a rate of 0.83 and an exponential relationship with a rate of 4.59, respectively. The variances of the increase rates were 0.02 for and 0.44 for {lambda} across participants. ConclusionThis study establishes a predictive model for SO occurrence during NREM sleep, providing insights into its organization in successive cycles and at different EEG channels, which is relevant to development of personalized stimulation paradigms. These findings imply that personalized model parameters can be estimated by incorporating SO information in the first sleep cycle, and hence SO timing can be predicted before its occurrence with a probability distribution, enabling more precise targeting of SOs.

bioengineering↗

bioRxiv2024

Heart Rate Variability During REM Sleep is Associated with Reduced Negative Memory Bias

Emotional memories change over time, but the mechanisms supporting this change are not well understood. Sleep has been identified as one mechanism that supports memory consolidation, with sleep selectively benefitting negative emotional consolidation at the expense of neutral memories, with specific oscillatory events linked to this process. In contrast, the consolidation of neutral and positive memories, compared to negative memories, has been associated with increased vagally-mediated vagal heart rate variability (HRV) during wakefulness. However, how HRV during sleep contributes to emotional memory consolidation remains unexplored. We investigated how sleep oscillations and vagal contributions during sleep contribute to the consolidation of neutral and negative memories. Using a double-blind, placebo-controlled, within-subject, cross-over design, we examined the impact of pharmacological vagal suppression using zolpidem on overnight emotional memory consolidation. Thirty-two participants encoded neutral and negative pictures in the morning, followed by picture recognition tests before and after a night of sleep. Zolpidem or placebo was administered in the evening before overnight sleep, and participants were monitored with electroencephalography and electrocardiography. In the placebo condition, greater overnight improvement for neutral pictures was associated with higher vagal HRV in both Non-Rapid Eye Movement Slow Wave Sleep (NREM SWS) and REM. Additionally, the emotional memory tradeoff (i.e., difference between consolidation of neutral versus negative memories) was associated with higher vagal HRV during REM, but in this case, neutral memories were remembered better than negative memories, indicating a potential role for REM vagal HRV in promoting a positive memory bias overnight. Zolpidem, on the other hand, reduced vagal HRV during SWS, increased NREM sigma power, and eliminated the positive memory bias. Lastly, we used a stepwise linear mixed effects regression to determine how NREM sigma power and vagal HRV during REM independently explained the variance in the emotional memory tradeoff effect. We found that the addition of vagal HRV in combination with sleep significantly improved the models fit. Overall, our results suggest that sleep brain oscillations and vagal signals synergistically interact in the overnight consolidation of emotional memories, with REM vagal HRV critically contributing to the positive memory bias.

neuroscience↗

bioRxiv2024

Slow Oscillations Modulate Functional Brain Changes Supporting Working Memory

Working memory (WM), the temporary mental storage and manipulation of information, is a skill that can improve with training. Sleep, and specifically slow oscillations (SOs), has been linked with WM improvement, yet it is unknown how processing during SOs modulates WM function across sleep. The current study examines how WM-related neural processing changes with sleep, and how these changes are related to activity during SOs. To do so, participants performed a WM task during fMRI before and after sleep, and the first 2.5 hrs of sleep was monitored by simultaneous EEG-fMRI. Reliable overnight changes in WM-related activity patterns were found, with reduced recruitment of the dorsal precuneus after compared to before sleep. Moreover, greater neural activation during SOs was associated with reduced overnight recruitment during WM across multiple levels of analysis. Our findings highlight the important role of SOs, elucidating how SOs may support changes in WM function across sleep.

neuroscience↗

bioRxiv2023

A Classification-Based Generative Approach to Selective Targeting of Global Slow Oscillations during Sleep

BackgroundGiven sleeps crucial role in health and cognition, numerous sleep-based brain interventions are being developed, aiming to enhance cognitive function, particularly memory consolidation, by improving sleep. Research has shown that Transcranial Alternating Current Stimulation (tACS) during sleep can enhance memory performance, especially when used in a closed-loop (cl-tACS) mode that coordinates with sleep slow oscillations (SOs, 0.5-1.5Hz). However, sleep tACS research is characterized by mixed results across individuals, which are often attributed to individual variability. Objective/HypothesisThis study targets a specific type of SOs, widespread on the electrode manifold in a short delay ("global SOs"), due to their close relationship with long-term memory consolidation. We propose a model-based approach to optimize cl-tACS paradigms, targeting global SOs not only by considering their temporal properties but also their spatial profile. MethodsWe introduce selective targeting of global SOs using a classification-based approach. We first estimate the current elicited by various stimulation paradigms, and optimize parameters to match currents found in natural sleep during a global SO. Then, we employ an ensemble classifier trained on sleep data to identify effective paradigms. Finally, the best stimulation protocol is determined based on classification performance. ResultsOur study introduces a model-driven cl-tACS approach that specifically targets global SOs, with the potential to extend to other brain dynamics. This method establishes a connection between brain dynamics and stimulation optimization. ConclusionOur research presents a novel approach to optimize cl-tACS during sleep, with a focus on targeting global SOs. This approach holds promise for improving cl-tACS not only for global SOs but also for other physiological events, benefiting both research and clinical applications in sleep and cognition.

neuroscience↗

bioRxiv2022

Slow oscillations promote long range effective communication: the key for memory consolidation in a broken down network

One of the most prominent and robust findings in cognitive neuroscience is the strengthening of memories during non-rapid eye movement (NREM) sleep, with slow oscillations (SOs, < 1Hz) playing a critical role in systems-level consolidation. However, NREM is a sleep period generally showing a breakdown in connectivity and reduction of synaptic plasticity with increasing depth: a brain state seemingly unfavorable to memory consolidation. Here, we present a novel approach to address this apparent paradox that leverages an event-related causality measure to estimate directional information flow during NREM sleep in epochs with and without SOs. Our results confirm that NREM is generally a state of dampened neural communication, but reveals that SOs provide two windows of enhanced large-scale communication before and after the SO trough. These peaks in communication are significantly higher when SOs are coupled with sleep spindles, compared with uncoupled SOs. To probe the functional relevance of these SO-selective peaks of information flow, we tested the temporal and topographic conditions that predict overnight episodic memory improvement. Our results show that global, long-range communication during SOs promote sleep-dependent systems consolidation of episodic memories. A significant correlation between peaks of information flow and memory improvement lends a predictive validity to our measurements of effective connectivity. In other words, we were able to predict memory improvement on the basis of independent electrophysiological observations during sleep. This work introduces a non-invasive approach to understanding information processing during sleep, a behavioral stage whose function, until now, has been understood only after synaptic reorganization after waking from sleep. Our findings provide a mechanism for how systems-level brain communication can occur during an otherwise low connectivity sleep state, indicating that SOs are a gating mechanism for large-scale neural communication, which is the necessary substrate for systems consolidation and long-term memory formation.

neuroscience↗

bioRxiv2021

The sleeping brain switches between working memory and long-term memory processing.

We provide evidence that human sleep is a competitive arena where cognitive domains vie for limited resources. Using pharmacology and effective connectivity analysis, we demonstrate that long-term memory and working memory are served by distinct offline neural mechanisms that are mutually antagonistic. Specifically, we administered zolpidem to increase central sigma activity and demonstrated targeted suppression of autonomic vagal activity. With effective connectivity, we determined the central activity has greater causal influence over autonomic activity, and the magnitude of this influence during sleep produced a behavioral trade-off between offline long-term and working memory processing. These findings show the first evidence of a sleep switch mechanism that toggles between central sigma-dependent long-term memory and autonomic vagal-dependent working memory processing. Significant StatementSleep facilitates both long-term episodic memory consolidation and short-term working memory functioning. However, the mechanism by which the sleeping brain performs both complex feats, and which sleep features are associated with these processes remain unclear. Using a pharmacological approach, we demonstrate that long-term and working memory are served by distinct offline neural mechanisms, and that these mechanisms are mutually antagonistic. We propose a Sleep Switch model in which the brain toggles between the two memory processes via a complex interaction at the synaptic, systems, and mechanistic level, with implications for research on cognitive disturbances observed in neurodegenerative disorders such as Alzheimers and Parkinsons disease, both of which involve the decline of sleep.

neuroscience↗

bioRxiv2020

Age-related differences in Autonomic/Central Coupling during a Daytime Nap

Age-dependent functional changes are mirrored by declines in both the central the autonomic nervous systems and have been related to pathological aging. Prior studies in our group have identified a temporal coupling of Autonomic and Central Events (ACEs) during sleep using electrocardiogram to measure heart rate and electroencephalography to measure brain rhythms, with heart rate bursts (HRBs) temporally coincided with increased slow-wave-activity (SWA, 0.5-1Hz) and sigma activity (12-15Hz), followed by parasympathetic surge (RRHF) during non-rapid eye movement (NREM) sleep. ACEs predicted working memory (WM) improvement in young adults. Given that there are paralleling age-related declines in both the ANS and CNS, the current study investigated differences in ACE activity during daytime sleep in older and younger adults and their functional impact on working memory. Compared to youngers, older adults showed lower amplitude of ACEs during NREM sleep, but no age-related difference during Wake. Furthermore, while younger adults demonstrated a parasympathetic surge after HRBs, older adults showed a different pattern, with a earlier rise and maintenance of the RRHF. Finally, we examined whether ACE predicted WM in older adults. We found that older adults with good WM show stronger coupling, whereas low WM performers had less robust ACE activity. Taken together, our results demonstrated that autonomic-central coupling declines with age, with possible links to deterioration in WM function. Given that age-related deterioration in autonomic and central nervous system activity is implicated in pathological decline, the current findings may facilitate novel insights to the cognitive neuroscience of aging.

neuroscience↗

bioRxiv2020

Age-related losses in cardiac autonomic activity during a daytime nap

In healthy, young individuals, a reduction in cardiovascular output and a shift from sympathetic to parasympathetic (vagal) dominance is observed from wake into stages of nocturnal and daytime sleep. This cardiac autonomic profile, measured by heart rate variability (HRV), has been associated with significant benefits for cardiovascular health. Aging is associated with decreased nighttime sleep quality and lower parasympathetic activity during both sleep and resting. However, it is not known whether age-related dampening of HRV extends to daytime sleep, diminishing the cardiovascular benefits of naps in the elderly. Here, we investigated this question by comparing the autonomic activity profile between young and older healthy adults during a daytime nap and a similar period of wakefulness (quiet wake; QW). For each condition, from the electrocardiogram (ECG), we obtained beat-to-beat HRV intervals (RR), root mean square of successive differences between adjacent heart-beat-intervals (RMSSD), high frequency (HF), low frequency (LF) power and total power (TP), HF normalized units (HFnu), and the LF/HF ratio. As previously reported, young subjects showed a parasympathetic dominance during NREM, compared with REM, pre-nap rest, and WASO. On the other hand, older, compared to younger, adults showed significantly lower vagally-mediated HRV (measured by RMSSD, HF, HFnu) during NREM. Interestingly, however, no age-related differences were detected during pre-nap rest or QW. Altogether, our findings suggest a sleep-specific reduction in parasympathetic modulation that is unique to NREM sleep in older adults.Impact Statement Sleep is naturally modulated by the autonomic nervous system (ANS), with greater dominance of parasympathetic over sympathetic activity during non-rapid-eye-movement (NREM) sleep. As such, sleep has been termed a “cardiovascular holiday” and has been associated with positive health outcomes. Aging, however, is linked to decreases in cardiac autonomic activity and sympathovagal imbalance. While the impact of aging on ANS activity during nocturnal sleep has received some attention, the cardiac profiles during a daytime nap, to our knowledge, have not yet been studied under the context of aging. Herein, young adults demonstrated increased parasympathetic activity during deep sleep. Older adults, however, showed less parasympathetic modulation during NREM sleep, suggesting loss of the cardiovascular holiday. Importantly, no age-related declines in parasympathetic activity were detected during wake, suggesting a sleep-specific reduction in parasympathetic modulation that is unique to NREM sleep in older adults.Competing Interest StatementThe authors have declared no competing interest.View Full Text

physiology↗

bioRxiv2020

Autonomic/Central Coupling Boosts Working Memory in Healthy Young Adults

Working memory (WM) is an executive function that can improve with training. However, the precise mechanism for this improvement is not known. Studies have shown greater WM gains after a period of sleep than a similar period of wake (Kuriyama et al. 2008a; Zinke, Noack, and Born 2018), with WM improvement correlated with slow wave activity (SWA; 0.5-1Hz) during slow wave sleep (SWS) (Sattari et al. 2019; Pugin et al. 2015; Ferrarelli et al. 2019). A different body of literature has suggested an important role for autonomic activity during wake for WM (Hansen et al. 2004; Mosley, Laborde, and Kavanagh 2018). A recent study from our group reported that the temporal coupling of autonomic and central events (ACEs) during sleep was associated with memory consolidation (Naji et al. 2019). We found that heart rate bursts (HR bursts) during non-rapid eye movement (NREM) sleep are accompanied by increases in SWA and sigma (12-15Hz) power, as well as increases in the high-frequency (HF) component of the RR interval, reflecting vagal rebound. In addition, ACEs predict long-term, episodic memory improvement. Building on these previous results, we examined whether ACEs may also contribute to gains in WM. We tested 104 young adults in an operation span task (OSPAN) in the morning and evening, with either a nap (with electroencephalography (EEG) and electrocardiography (ECG)) or wake between testing sessions. We identified HR bursts in the ECG and replicated the increases in SWA and sigma prior to peak of the HR burst, as well as vagal rebound after the peak. Furthermore, we showed sleep-dependent WM improvement, which was predicted by ACE activity. Using regression analyses, we discovered that significantly more variance in WM improvement could be explained with ACE variables than with overall sleep activity not time-locked with ECG. These results provide the first evidence that coordinated autonomic and central events play a significant role in sleep-related WM improvement and implicate the potential of autonomic interventions during sleep for cognitive enhancement.

neuroscience↗

bioRxiv2020

Autonomic activity during a daytime nap facilitates working memory improvement

Recent investigations have implicated the parasympathetic branch of the autonomic nervous system (ANS) in higher-order executive functions. These actions are purported to occur through ANSs modulation of the prefrontal cortex, with parasympathetic activity during wake associated with working memory ability (WM). Compared with wake, sleep is a period with substantially greater parasympathetic tone. Recent work has reported that sleep may also contribute to improvement in WM. Here, we examined the role of cardiac parasympathetic activity during sleep on WM improvement in healthy young adults. Participants were tested in an operation span task (OSpan) in the morning and evening, and during the inter-test period subjects either experienced a nap or wake. We measured high frequency heart rate variability (HF HRV) as an index of cardiac, parasympathetic activity during both wake and sleep. Participants showed the expected boost in parasympathetic activity during nap, compared with wake, as well as greater WM improvement after a nap compared with an equivalent period awake. Furthermore, parasympathetic activity during sleep, but not wake, was significantly correlated with WM improvement. Together these results indicate that the natural boost in parasympathetic activity during sleep has substantial benefits to gains in prefrontal executive function in young adults. We present a conceptual model illustrating the interaction between sleep, autonomic activity, and prefrontal brain function, and highlight open research questions that will facilitate understanding of the factors that contribute to executive abilities in young adults, as well as in cognitive aging. Significance StatementRecently, the neurovisceral integration model has implicated activity on the parasympathetic branch of the autonomic nervous system (ANS) during wake in executive functioning. Parasympathetic activity peaks during deep sleep, and sleep has been shown to facilitate executive functioning. Yet, the role of parasympathetic activity during sleep for executive functioning is not known. Herein, participants demonstrated increased parasympathetic activity during deep sleep, sleep-dependent WM improvement, and associations between performance gains and parasympathetic activity in sleep, not wake. Our conceptual model illustrates the interaction between sleep, autonomic activity, and prefrontal brain function that may contribute to executive abilities in young adults and to cognitive aging.

neuroscience↗

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

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↗

bioRxiv2017

Human hippocampal replay during rest prioritizes weakly-learned information and predicts memory performance

There is now extensive evidence that the hippocampus replays experiences during quiet rest periods, and that this replay benefits subsequent memory. A critical open question is how memories are prioritized for replay during these offline periods. We addressed this question in an experiment in which participants learned the features of 15 objects and then underwent fMRI scanning to track item-level replay in the hippocampus using pattern analysis during a rest period. Objects that were remembered less well were replayed more during the subsequent rest period, suggesting a prioritization process in which weaker memories--memories most vulnerable to forgetting--are selected for wake replay. Participants came back for a second session, either after a night of sleep or a day awake, and underwent another scanned rest period followed by a second memory test. In the second session, more hippocampal replay of a satellite during the rest period predicted better subsequent memory for that satellite. Only in the group with intervening sleep did rest replay predict improvement from the first to second session. Our results provide the first evidence that replay of individual memories occurs during rest in the human hippocampus and that this replay prioritizes weakly learned information, predicts subsequent memory performance, and relates to memory improvement across a delay with sleep.

neuroscience↗

bioRxiv2017

Sleep Benefits Memory For Semantic Category Structure While Preserving Exemplar-Specific Information

Semantic memory encompasses knowledge about both the properties that typify concepts (e.g. robins, like all birds, have wings) as well as the properties that individuate conceptually related items (e.g. robins, in particular, have red breasts). We investigate the impact of sleep on new semantic learning using a property inference task in which both kinds of information are initially acquired equally well. Participants learned about three categories of novel objects possessing some properties that were shared among category exemplars and others that were unique to an exemplar, with exposure frequency varying across categories. In Experiment 1, memory for shared properties improved and memory for unique properties was preserved across a night of sleep, while memory for both feature types declined over a day awake. In Experiment 2, memory for shared properties improved across a nap, but only for the lower-frequency category, suggesting a prioritization of weakly learned information early in a sleep period. The increase was significantly correlated with amount of REM, but was also observed in participants who did not enter REM, suggesting involvement of both sleep stages. The results provide the first evidence that sleep improves memory for the shared structure of object categories, while simultaneously preserving object-unique information.

neuroscience↗