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Abstract The function of sleep in humans has been investigated using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging recordings to provide accurate sleep scores with spatial precision. Recent studies have demonstrated that spontaneous brain oscillations and functional connectivity dissociate during nonrapid eye m...
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Models of memory consolidation posit a central role for reactivation of brain activity patterns during sleep, especially in non-Rapid Eye Movement (NREM) sleep. While such “replay” of recent waking experiences has been well-demonstrated in rodents, electrophysiological evidence of reactivation in human sleep is still largely lacking. In this intrac...
Human sleep, defined on the basis of electroencephalogram (EEG), electromyogram
(EMG) and electrooculogram (EOG), is divided into rapid eye movement (REM) sleep
and four stages of non–rapid eye movement (NREM) sleep. Collective monitoring and
recording of physiological data during sleep is called polysomnography. Sleep which
normally starts with a...
The importance of sleep in memory consolidation is well-established, with the hippocampal CA1 and CA3 subregions playing a crucial role in this process. The current working hypothesis postulates that episodic memory traces captured during waking hours are replayed in the hippocampal CA1-CA3 areas and transferred to the cortex for long-term storage...
Since their discovery, slow oscillations have been observed to group spindles during non-REM sleep. Previous studies assert that the slow oscillation downstate (DS) is preceded by slow spindles (10-12Hz), and followed by fast spindles (12-16Hz). Here, using both direct transcortical recordings in patients with intractable epilepsy (n=10, 8 female),...
Post-learning sleep facilitates negative memory consolidation and also helps preserve it over several years. It is believed, therefore, that sleep deprivation may help prevent consolidation of fearful memory. Its effect, however, on consolidation of negative/frightening memories is not known. Cued fear-conditioning (CuFC) is a widely used model to...
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... The increased morphometric similarity between right PCL and VAN may reflect abnormal sensory processing with increased sensitivity to external stimulation in individuals with insomnia. During sleep, the DMN dynamically dissociates and reconnects with SMN, playing a role in sleep/wake regulation and cognitive modulations (Wu et al. 2012). In patients with ID, these connection patterns have been found to be disrupted (Li et al. 2018;Santarnecchi et al. 2018;Wang et al. 2018). ...
Previous studies on structural covariance network (SCN) suggested that patients with insomnia disorder (ID) show abnormal structural connectivity, primarily affecting the somatomotor network (SMN) and default mode network (DMN). However, evaluating a single structural index in SCN can only reveal direct covariance relationship between two brain regions, failing to uncover synergistic changes in multiple structural features. To cover this research gap, the present study utilized novel morphometric similarity networks (MSN) to examine the morphometric similarity between cortical areas in terms of multiple sMRI parameters measured at each area. With seven T1-weighted imaging morphometric features from the Desikan-Killiany atlas, individual MSN was constructed for patients with ID (N = 87) and healthy control groups (HCs, N = 84). Two-sample t-test revealed differences in MSN between patients with ID and HCs. Correlation analyses examined associations between MSNs and sleep quality, insomnia symptom severity, and depressive symptoms severity in patients with ID. The right paracentral lobule (PCL) exhibited decreased morphometric similarity in patients with ID compared to HCs, mainly manifested by its de-differentiation (meaning loss of distinctiveness) with the SMN, DMN, and ventral attention network (VAN), as well as its decoupling with the visual network (VN). Greater PCL-based de-differentiation correlated with less severe insomnia and fewer depressive symptoms in the patients group. Additionally, patients with less depressive symptoms showed greater PCL de-differentiation from the SMN. As an important pilot step in revealing the underlying morphometric similarity alterations in insomnia disorder, the present study identified the right PCL as a hub region that is de-differentiated with other high-order networks. Our study also revealed that MSN has an important potential to capture clinical significance related to insomnia disorder.
... With fMRI, some Taiwanese colleagues tested the latter hypothesis. They investigated the variations in functional connectivity in brain networks between pre-and post-sleep wakefulness [126,133]. Comparing these two conditions, they showed a decreased connectivity within the sensory-motor network at awakening, and no alterations in cognitive networks (notably the default mode -DMN-and hippocampal networks). Their results could explain the poor motor performances and clumsiness typical of sleep inertia, but can hardly account for the cognitive impairments reported in previous studies (e.g. ...
... Specifically, in the first sleep cycle, there is a gradual disconnection observed between the medial prefrontal and parietal nodes of the DMN [70,71], aligning with the decreasing richness of mental experiences during the deepening phase of sleep. In contrast, interregional connectivity appears to be reestablished in the second half of the night when deep sleep diminishes and REM sleep becomes more abundant [72][73][74]. Considering the common neural substrates (the DMN) and phenomenological similarities between dreaming and mind-wandering [24,75], it is reasonable to speculate that the reduction of sleep states rich in oneiric activity could create a homeostatic pressure during subsequent periods of wakefulness, potentially facilitating mind-wandering. ...
Mind-wandering is a mental state in which attention shifts from the present environment or current task to internally driven, self-referent mental content. Homeostatic sleep pressure seems to facilitate mind-wandering as indicated by studies observing links between increased mind-wandering and impaired sleep. Nevertheless, previous studies mostly relied on cross-sectional measurements and self-reports. We aimed to combine the accuracy of objective sleep measures with the use of self-reports in a naturalistic setting in order to examine if objective sleep parameters predict the tendency for increased mind-wandering on the following day. We used mobile sleep EEG headbands and self-report scales over 7 consecutive nights in a group of 67 healthy participants yielding ~ 400 analyzable nights. Nights with more wakefulness and shorter REM and SWS were associated with poorer subjective sleep quality at the intra-individual level. Reduced REM and N2 sleep, as well as less intense dream experiences, predicted more mind-wandering the following day. Our micro-longitudinal study indicates that intra-individual fluctuations in the duration of specific sleep stages predict the perception of sleep quality as assessed in the morning, as well as the intensity of daytime mind-wandering the following hours. The combined application of sleep EEG assessments and self-reports over repeated assessments provides new insights into the subtle intra-individual, night-to-day associations between nighttime sleep and the next day’s subjective experiences.
... We hypothesized that this modulation would be particularly prominent in the delta, theta and sigma frequency bands (Achermann et al., 2016;Bouchard et al., 2019). During REM sleep, connectivity was expected to increase as compared to NREM, especially within the sensorimotor, attentional and default mode networks (Houldin et al., 2021;Watanabe et al., 2014;Wu et al., 2012). At the frequency level, we hypothesized that connectivity in the alpha, beta and gamma frequency bands would be greater in REM than in NREM sleep (Achermann et al., 2016;Bouchard et al., 2019). ...
... At the network level, the reduced communication between regions of sensorimotor and executive networks is thought to support the fading of sensory awareness and disengagement of executive control during sleep (Daneault et al., 2021;Larson-Prior et al., 2009;Wu et al., 2012). The decrease in FC between nodes of the DMN (particularly in the frontal regions) during NREM3 has been proposed to reflect the decrease in conscious awareness that characterizes this stage of sleep (Horovitz et al., 2009;Sämann et al., 2011;Wu et al., 2012). ...
... At the network level, the reduced communication between regions of sensorimotor and executive networks is thought to support the fading of sensory awareness and disengagement of executive control during sleep (Daneault et al., 2021;Larson-Prior et al., 2009;Wu et al., 2012). The decrease in FC between nodes of the DMN (particularly in the frontal regions) during NREM3 has been proposed to reflect the decrease in conscious awareness that characterizes this stage of sleep (Horovitz et al., 2009;Sämann et al., 2011;Wu et al., 2012). Note, however, that DMN connectivity has also been described to be maintained throughout all stages of sleep (Koike et al., 2011) or even to increase from NREM2 to NREM3 (Watanabe et al., 2014). ...
Functional connectivity (FC) during sleep has been shown to break down as non-rapid eye movement (NREM) sleep deepens before returning to a state closer to wakefulness during rapid eye movement (REM) sleep. However, the specific spatial and temporal signatures of these fluctuations in connectivity patterns remain poorly understood. This study aimed to investigate how frequency-dependent network-level FC fluctuates during nocturnal sleep in healthy young adults using high-density electroencephalography (hdEEG). Specifically, we examined source-localized FC in resting-state networks during NREM2, NREM3 and REM sleep (sleep stages scored using a semi-automatic procedure) in the first three sleep cycles of 29 participants. Our results showed that FC within and between all resting-state networks decreased from NREM2 to NREM3 sleep in multiple frequency bands and all sleep cycles. The data also highlighted a complex modulation of connectivity patterns during the transition to REM sleep whereby delta and sigma bands hosted a persistence of the connectivity breakdown in all networks. In contrast, a reconnection occurred in the default mode and the attentional networks in frequency bands characterizing their organization during wake (i.e., alpha and beta bands, respectively). Finally, all network pairs (except the visual network) showed higher gamma-band FC during REM sleep in cycle three compared to earlier sleep cycles. Altogether, our results unravel the spatial and temporal characteristics of the well-known breakdown in connectivity observed as NREM sleep deepens. They also illustrate a complex pattern of connectivity during REM sleep that is consistent with network- and frequency-specific breakdown and reconnection processes.
... The significant predominance of the FCs intra-DMN in the alert state aligns with the expected role of the default mode network compared to other executive or sensorial networks at rest (Raichle et al., 2001, Mazoyer et al., 2001. Moreover, it is consistent with the decoupling observed during NREM sleep (Horovitz et al., 2009;Samann et al., 2011;Larson-Prior et al., 2011;Spoormaker et al., 2012, Wu et al., 2012. One of the striking messages of this study is that depending on the processing methods used (Ret-PRF, GR, Zs, or Zmix), the interpretation, in terms of networks, of the effects of sleepiness can drastically change or even be reversed without knowing which is the best technique. ...
This research explores the effects of drowsiness on variability in functional connectivity (FC) during resting-state functional magnetic resonance imaging. The study utilized a cohort of students (MRi-Share) and classified individuals into drowsy (N=68), alert (N=96), and mixed/undetermined states based on observed respiratory oscillations. Five different processing methods were employed, the reference method, two correction methods based on physiological and global regression approaches, and two based on Gaussian standardizations. According to the reference methodology, the results indicate that drowsy individuals exhibit higher cortico-cortical FC than alert individuals. However, the differences between drowsy and alert states were reduced when applying correction methods based on physiological and global regression approaches. The global regression-based strategy was the most effective among these correction methods, minimizing significant FC differences to only 3.3% of the total FCs. Utilizing the Gaussian-based methods, both cortico-subcortical and intra-default mode network regions demonstrated significantly greater FCs in awake than drowsy subjects. These findings align with previous studies suggesting that, in the descent to sleep, the cortex isolates itself to facilitate the transition into deeper sleep stages while also disconnecting the default mode network. The Gaussian standardization methods and the global regression-based correction approach efficiently address the hemodynamic variations caused by the rapid alternation between the N1 stage and wakefulness. These variations contribute to the measurement of cortico-cortical pseudo connectivity observed in the reference methodology. In summary, these findings underscore the importance of considering drowsiness in rs-fMRI studies and demonstrate that there is no single optimal correction methodology for processing fMRI data.
... Based on similar hemodynamic perspectives, recent studies using functional magnetic resonance imaging (fMRI), in conjunction with EEG or polysomnography, have presented asynchronous brain-network reorganizations during SI. Our group first demonstrated that the functional connectivity pattern of the sensorimotor network (SMN) in the SI period after nocturnal sleep seemed disconnected, as if it remained disrupted in non-rapid eye movement (NREM) sleep, whereas the default-mode network (DMN) showed an intact connectivity pattern with quick recovery (10,11). Probing the nap inertia after partial sleep deprivation, Vallat et al. demonstrated a distinction in betweennetwork recovery between the participants awakened from deep sleep (NREM sleep stage 3, N3) and those awakened from light sleep (NREM sleep stage 2, N2) (12). ...
Sleep inertia (SI) is a time period during the transition from sleep to wakefulness wherein individuals perceive low vigilance with cognitive impairments; SI is generally identified by longer reaction times (RTs) in attention tasks immediately after awakening followed by a gradual RT reduction along with waking time. The sluggish recovery of vigilance in SI involves a dynamic process of brain functions, as evidenced in recent functional magnetic resonance imaging (fMRI) studies in within-network and between-network connectivity. However, these fMRI findings were generally based on the presumption of unchanged neurovascular coupling (NVC) before and after sleep, which remains an uncertain factor to be investigated. Therefore, we recruited 12 young participants to perform a psychomotor vigilance task (PVT) and a breath-hold task of cerebrovascular reactivity (CVR) before sleep and thrice after awakening (A1, A2, and A3, with 20 min intervals in between) using simultaneous electroencephalography (EEG)-fMRI recordings. If the NVC were to hold in SI, we hypothesized that time-varying consistencies could be found between the fMRI response and EEG beta power, but not in neuron-irrelevant CVR. Results showed that the reduced accuracy and increased RT in the PVT upon awakening was consistent with the temporal patterns of the PVT-induced fMRI responses (thalamus, insula, and primary motor cortex) and the EEG beta power (Pz and CP1). The neuron-irrelevant CVR did not show the same time-varying pattern among the brain regions associated with PVT. Our findings imply that the temporal dynamics of fMRI indices upon awakening are dominated by neural activities. This is the first study to explore the temporal consistencies of neurovascular components on awakening, and the discovery provides a neurophysiological basis for further neuroimaging studies regarding SI.
... During REM sleep, connectivity was expected to increase as compared to NREM, especially within the sensorimotor, attentional, and default mode networks (Wu et al., 2012;Watanabe et al., 2014;Houldin et al., 2021). At the frequency level, we hypothesized that connectivity in the alpha, beta, and gamma frequency bands would be greater in REM than in NREM sleep (Achermann et al., 2016;Bouchard et al., 2019). ...
... At the network level, the reduced communication between regions of sensorimotor and executive networks is thought to support the fading of sensory awareness and disengagement of executive control during sleep (Larson-Prior et al., 2009;Wu et al., 2012;Daneault et al., 2021). The decrease in FC between nodes of the DMN (particularly in the frontal regions) during NREM3 has been proposed to reflect the decrease in conscious awareness that characterizes this stage of sleep (Horovitz et al., 2009;Sämann et al., 2011;Wu et al., 2012). ...
... At the network level, the reduced communication between regions of sensorimotor and executive networks is thought to support the fading of sensory awareness and disengagement of executive control during sleep (Larson-Prior et al., 2009;Wu et al., 2012;Daneault et al., 2021). The decrease in FC between nodes of the DMN (particularly in the frontal regions) during NREM3 has been proposed to reflect the decrease in conscious awareness that characterizes this stage of sleep (Horovitz et al., 2009;Sämann et al., 2011;Wu et al., 2012). Note, however, that DMN connectivity has also been described to be maintained throughout all stages of sleep (Koike et al., 2011) or even to increase from NREM2 to NREM3 (Watanabe et al., 2014). ...
Functional connectivity (FC) during sleep has been shown to break down as non-rapid eye movement (NREM) sleep deepens before returning to a state closer to wakefulness during REM sleep. However, the specific spatial and temporal signatures of these fluctuations in connectivity patterns remain poorly understood. The goal of this study was to investigate how frequency-dependent network-level FC fluctuates during nocturnal sleep in healthy young adults using high-density electroencephalography (hdEEG). Specifically, we examined source-localized FC in resting-state networks during NREM2, NREM3, and REM sleep in the first three sleep cycles of 29 participants. Our results showed that FC within and between all resting-state networks decreased from NREM2 to NREM3 sleep in multiple frequency bands and in all sleep cycles. The data also highlighted a complex modulation of connectivity patterns during the transition to REM sleep whereby delta and sigma bands hosted a persistence of the connectivity breakdown in all networks, whereas a reconnection was observed in the default mode (DMN) and the attentional networks in frequency bands characterizing their organization during wake (i.e., alpha and beta bands, respectively). Finally, all network pairs (except the visual network) showed higher gamma-band FC during REM sleep in cycle three compared to earlier cycles during the night. Altogether, our results unravel the spatial and temporal characteristics of the well-known breakdown in connectivity observed as NREM sleep deepens. They also shed light on a complex pattern of connectivity during REM sleep that is consistent with both breakdown and reconnection processes that are network- and frequency-specific.
... Moreover, these findings are in line with the observations of a general breakdown of large-scale fronto-parietal connectivity and the shift towards more localized (modularized) networks during the deepest stage of sleep (Ferri et al., 2008;Spoormaker et al., 2012Spoormaker et al., , 2010Tononi and Massimini, 2008). In contrast, as the first sleep cycle and the deepest period of sleep dissipate, the subregions of the DMN appear to recouple during REM sleep (Chow et al., 2013;Wu et al., 2012). For instance, Chow and colleagues found that whereas the main nodes of the DMN (mPFC and posterior cingulate cortex) showed reduced coupling in SWS compared to wake, interregional connectivity was restored in REM, and was not different from wake. ...
... Moreover, correlated activity between the posterior cingulate cortex and the dorsal portions of the mPFC, and the dorsal IPLs were even stronger during REM than during wakefulness (Chow et al., 2013). Another study (Wu et al., 2012) also observed reduced connectivity in deep NREM sleep between sensorimotor regions and the nodes of the DMN, but re-established connectivity in both networks when participants entered into REM sleep. These findings indicate that the transition from restorative to predictive homeostasis involves the recoupling of the key nodes of the DMN. ...
... These may include spontaneous, self-referent thought processes that re-establish the coherent sense of the self and its orientation in space and time, as well as cuedriven immediate (e.g., drinking coffee) or future plans (e.g., finishing a manuscript). After awakening, the efficient recoupling of frontoparietal networks with the DMN (Wu et al., 2012) may facilitate executive functions that help to focus (constrain) the scope of morning prospection and select appropriate behaviors in accordance with specific goals (Fig. 2/B). Since self-referent simulations generated during sleep and goal-directed self-referent thoughts rely on overlapping neural networks, they compete for resources: goal-directed prospection inhibits the recall of dreams that fall into oblivion without retrieval. ...
Dreams are often viewed as fascinating but irrelevant mental epihenomena of the sleeping mind with questionable functional relevance. Despite long hours of oneiric activity, and high individual differences in dream recall, dreams are lost into oblivion. Here, we conceptualize dreaming and dream amnesia as inherent aspects of the reactive and predictive homeostatic functions of sleep. Mental activity during sleep conforms to the interplay of restorative processes and future anticipation, and particularly during the second half of the night, it unfolds as a special form of non-constrained, self-referent, and future-oriented cognitive process. Awakening facilitates constrained, goal-directed prospection that competes for shared neural resources with dream production and dream recall, and contributes to dream amnesia. We present the neurophysiological aspects of reactive and predictive homeostasis during sleep, highlighting the putative role of cortisol in predictive homeostasis and forgetting dreams. The theoretical and methodological aspects of our proposal are discussed in relation to the study of dreaming, dream recall, and sleep-related cognitive processes.
... These findings are in line with the observations of a general breakdown of large-scale fronto-parietal connectivity and the shift towards more localized (modularized) networks during the deepest stage of sleep [95][96][97][98]. In contrast, as the first sleep cycle and the deepest period of sleep dissipate, the subregions of the DMN appear to recouple and restore interregional connectivity during REM sleep [99,100]. Thus, the transition from restorative to predictive homeostasis involves the recoupling of the key nodes of the DMN. ...
... These may include spontaneous, self-referent thought processes that re-establish the coherent sense of the self and its orientation in space and time, as well as cue-driven immediate (e.g., drinking a coffee) or future plans (e.g., finishing a manuscript). After awakening, the efficient recoupling of fronto-parietal networks with the DMN [99] may facilitate executive functions that help to focus (constrain) the scope of morning prospection and select appropriate behaviors in accordance with specific goals (Figure 2/B). Since self-referent simulations generated during sleep and goal-directed self-referent thoughts rely on overlapping neural networks, they compete for resources: goal-directed prospection inhibits the recall of dreams that without retrieval fall into oblivion. ...
Dreams are often viewed as fascinating but relatively irrelevant mental epihenomena of the sleeping mind with questionable or no functional relevance. Despite long hours of oneiric activity, and high individual differences in dream recall, dream amnesia is one of the most robust and universal features of dreaming. In this review, we conceptualize dreaming and dream amnesia as inherent aspects of the homeostatic functions of sleep and wakefulness. In this framework the temporal progression of sleep throughout the night is shaped by reactive and predictive homeostatic functions. Mental activity during sleep conforms to the dynamic interplay of restorative processes and future anticipation, and particularly during the second half of the night, unfolds as a special form of non-constrained, self-referent, and future-oriented cognitive process. Awakening facilitates constrained, goal-directed prospection that competes for shared neural resources with dream production and dream recall, and hence, contributes to dream amnesia. We present the neurophysiological aspects of reactive and predictive homeostasis during sleep, highlighting the putative role of nocturnal cortisol as well as the cortisol awakening response in predictive homeostasis and dream amnesia, respectively. The theoretical and methodological aspect of our proposal is discussed in relation to the study of dream recall, sleep-related cognitive processes and altered dreaming in sleep disorders.
... The regions of the DMN retain their coupling during light sleep and are decoupled during deep sleep, signifying that DMN connections may support certain states of consciousness [16,17]. In addition, the alterations in FC in DMN subsystems from light sleep to deep sleep account well for the quantitative features of light sleep [18]. However, the FC among core regions of the DMN has been found to remain intact during sleep [11]. ...
The default-mode network (DMN) is believed to be associated with levels of consciousness, but how the functional connectivity (FC) of the DMN changes across different states of consciousness is still unclear. In the current work, we addressed this issue by exploring the coactive micropattern (CAMP) networks of the DMN according to the CAMPs of rat DMN activity during the sleep-wake cycle and tracking their topological alterations among different states of consciousness. Three CAMP networks were observed in DMN activity, and they displayed greater FC and higher efficiency than the original DMN structure in all states of consciousness, implying more efficient information processing in the CAMP networks. Furthermore, no significant differences in FC or network properties were found among the three CAMP networks in the waking state. However, the three networks were distinct in their characteristics in two sleep states, indicating that different CAMP networks played specific roles in distinct sleep states. In addition, we found that the changes in the FC and network properties of the CAMP networks were similar to those in the original DMN structure, suggesting intrinsic effects of various states of consciousness on DMN dynamics. Our findings revealed three underlying CAMP networks within the DMN dynamics and deepened the current knowledge concerning FC alterations in the DMN during conscious changes in the sleep-wake cycle.
