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

Naji, Mohsen; McDevitt, Elizabeth A.; Bazhenov, Maxim; Mednick, Sara C.

doi:10.1101/195586

Cited by 8 publications

(15 citation statements)

References 59 publications

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“…Delta wave generation may be a protective mechanism that isolates injured regions from the rest of the brain for neural healing (Naviaux, 2019). Delta wave activity is a normal feature of deep (“slow‐wave”) sleep in which parasympathetic autonomic tone is high and associated with metabolic patterns that underpin healing and repair in both the CNS and periphery (Javaheri & Redline, 2012; Naji, Krishnan, McDevitt, Bazhenov, & Mednick, 2019; Xie et al, 2013; Yüzgeç, Prsa, Zimmermann, & Huber, 2018). In the periphery, slow‐wave sleep is required for normal protective and restorative decreases in blood pressure and the sleep‐associated increase in healthy heart rate variability (Boudreau, Yeh, Dumont, & Boivin, 2013; Javaheri & Redline, 2012).…”

Section: Discussionmentioning

confidence: 99%

Resting‐state magnetoencephalography source magnitude imaging with deep‐learning neural network for classification of symptomatic combat‐related mild traumatic brain injury

Huang

Harrington

et al. 2021

Human Brain Mapping

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Combat‐related mild traumatic brain injury (cmTBI) is a leading cause of sustained physical, cognitive, emotional, and behavioral disabilities in Veterans and active‐duty military personnel. Accurate diagnosis of cmTBI is challenging since the symptom spectrum is broad and conventional neuroimaging techniques are insensitive to the underlying neuropathology. The present study developed a novel deep‐learning neural network method, 3D‐MEGNET, and applied it to resting‐state magnetoencephalography (rs‐MEG) source‐magnitude imaging data from 59 symptomatic cmTBI individuals and 42 combat‐deployed healthy controls (HCs). Analytic models of individual frequency bands and all bands together were tested. The All‐frequency model, which combined delta‐theta (1–7 Hz), alpha (8–12 Hz), beta (15–30 Hz), and gamma (30–80 Hz) frequency bands, outperformed models based on individual bands. The optimized 3D‐MEGNET method distinguished cmTBI individuals from HCs with excellent sensitivity (99.9 ± 0.38%) and specificity (98.9 ± 1.54%). Receiver‐operator‐characteristic curve analysis showed that diagnostic accuracy was 0.99. The gamma and delta‐theta band models outperformed alpha and beta band models. Among cmTBI individuals, but not controls, hyper delta‐theta and gamma‐band activity correlated with lower performance on neuropsychological tests, whereas hypo alpha and beta‐band activity also correlated with lower neuropsychological test performance. This study provides an integrated framework for condensing large source‐imaging variable sets into optimal combinations of regions and frequencies with high diagnostic accuracy and cognitive relevance in cmTBI. The all‐frequency model offered more discriminative power than each frequency‐band model alone. This approach offers an effective path for optimal characterization of behaviorally relevant neuroimaging features in neurological and psychiatric disorders.

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Section: Discussionmentioning

confidence: 99%

Resting‐state magnetoencephalography source magnitude imaging with deep‐learning neural network for classification of symptomatic combat‐related mild traumatic brain injury

Huang

Harrington

et al. 2021

Human Brain Mapping

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“…Within each continuative and undisturbed 3-min bin, the mean and standard deviation of RR were calculated, and HR bursts were identified as the RR intervals shorter than 1.25 standard deviations below the mean. We investigated EEG changes during 20-sec windows around the HR bursts because EEG fluctuation typically returned to baseline level within 20 seconds in the previous study from our group (Naji et al, 2019). No other burst occurred in the 20 second windows around the HR bursts.…”

Section: Hr Burst Detectionmentioning

confidence: 99%

“…Additionally, ECG activity has been shown to modulate sleep spindle phase (Brandenberger et al, 2001;Lecci et al, 2017). More recently, our group evaluated changes in EEG and ECG signals that occurred during brief acceleration in heart rate during daytime nap (Naji et al, 2019). Naji et al (2019) identified cardiovascular events during NREM sleep, termed heart rate bursts (HRBs), that last 2-3 seconds and occur mostly in Stage 2 and SWS (Naji et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…More recently, our group evaluated changes in EEG and ECG signals that occurred during brief acceleration in heart rate during daytime nap (Naji et al, 2019). Naji et al (2019) identified cardiovascular events during NREM sleep, termed heart rate bursts (HRBs), that last 2-3 seconds and occur mostly in Stage 2 and SWS (Naji et al, 2019). Heart rate bursts are temporally coincident with EEG activity, including SWA and spindle/sigma activity (12-15Hz) and are followed by vagal rebound reflected in a surge in the high frequency component (HF; 0.15-0.4 Hz) of the ECG.…”

Section: Introductionmentioning

confidence: 99%

“…The increased SWA and Sigma activity before the HRBs and the vagal activity after the HR burst were termed as ACE activity (ACE-SWA, ACE-Sigma, and ACE-RRHF, respectively). Importantly, these Autonomic/Central Events (ACEs) positively predicted post-nap improvement in long-term declarative (Naji et al, 2019) and working memory (Chen et al, 2020). In fact, regression models showed a greater proportion of the sleep-dependent memory improvement was explained by the ACE activity than by the typical EEG sleep events alone.…”

Section: Introductionmentioning

confidence: 99%

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Autonomic/Central Coupling during Daytime Sleep Differs between Older and Younger People

Chen¹,

Simon²,

Sattari³

et al. 2020

Preprint

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Age-dependent functional changes are mirrored by declines in both the central nervous system (CNS) and in the autonomic nervous system (ANS) and have been related to pathological aging. Prior studies have demonstrated inter-dependence between central and autonomic events that contribute to cognition. Moreover, our group recently identified a temporal coupling of Autonomic and Central Events (ACEs) during sleep using electrocardiogram (ECG) to measure heart rate and electroencephalography (EEG) to measure sleeping brain rhythms [40]. We showed that 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. Given that there are paralleling age-related declines in both the ANS and CNS, the current study investigated how these declining systems impact ACE coupling during daytime naps in older and younger adults. Despite, lower overall EEG activity during ACE windows in older adults, both younger and older adults showed HRB-modulated increases in SWA and sigma during wake and N2. However, older adults did not show the same pattern during N3. Furthermore, while younger adults demonstrated a RRHF increase only after HRBs, older adults showed an earlier rise and maintenance of the RRHF. Taken together, our results demonstrated that ACE activity remains generally intact with age. Given that age-related deterioration in autonomic and central nervous system activity is implicated in pathological decline, the general maintenance of alignment between the two systems is intriguing and may facilitate novel insights to aging.

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A Role for Neuronal Oscillations of Sleep in Memory and Cognition

Marshall

2020

Neuronal Oscillations of Wakefulness and Sleep

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This chapter deals with oscillations of neurons and networks that are relevant for different cognitive processes, in particular for memory retention in animals and humans during sleep. This first section gives a brief insight into sleep and memory in nonmammalian species.The greatest amount of research on neuronal oscillations and plasticity has been conducted in vertebrates, more specifically on mammals. Studies on simpler invertebrate models-most notably the fruit fly and honey bee-have the advantage that the process of memory formation can more easily be dissected, from systems down to the molecular level, than for higher-order animals [1,2]. Despite well-established proof of memory and plasticity in these species, behavioral rather than electrophysiological definitions of sleep are up to now mostly employed [3,4]. Only very recently were 7-10 Hz oscillations discovered in the spontaneously sleeping fly [5]. In the olfactory nervous system of the Drosophila, several memory traces associated with short-term, intermediate, and long-term memory after conditioning with odors have meanwhile been reported [6]. For odor as well as for visual memories, a dynamic interaction between different brain regions across time reminiscent of memory in higher-order animals occurs [7,8]. Neuronal oscillations in the honey bee brain have not been published. Yet, not only was sleep found relevant for consolidating navigation memory [9], but presentation of a contextual odor during sleep enhanced subsequent retention performance [10]. Although the dependence on neuronal activity during sleep has, to the authors' knowledge, not yet been investigated, separate studies have revealed in crayfish both brain electric activity characteristic of sleep [11], as well as evidence of spatial and motor learning [12,13].

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

Cited by 8 publications

References 59 publications

Resting‐state magnetoencephalography source magnitude imaging with deep‐learning neural network for classification of symptomatic combat‐related mild traumatic brain injury

Resting‐state magnetoencephalography source magnitude imaging with deep‐learning neural network for classification of symptomatic combat‐related mild traumatic brain injury

Autonomic/Central Coupling during Daytime Sleep Differs between Older and Younger People

A Role for Neuronal Oscillations of Sleep in Memory and Cognition

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