To wake or not to wake? The two-sided nature of the human K-complex

Jahnke, Kolja; Wegner, Frederic von; Morzelewski, Astrid; Borisov, S. V.; Maischein, Marcella; Steinmetz, Helmuth; Laufs, Helmut

doi:10.1016/j.neuroimage.2011.09.013

Cited by 108 publications

(86 citation statements)

References 67 publications

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“…We have also shown that H exponent and signal variance changes affect different networks during the deeper sleep stages. A possible source for the observed variance changes is the polymodal BOLD deactivation (44) induced by environmental stimuli during sleep (possibly having a role in sleep protection) (45), which could be repeatedly triggered by scanner noise or internal stimuli and thus, contribute to an increased signal variance in the sensory cortices.…”

Section: Discussionmentioning

confidence: 99%

Breakdown of long-range temporal dependence in default mode and attention networks during deep sleep

Tagliazucchi

Wegner

Morzelewski

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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223

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The integration of segregated brain functional modules is a prerequisite for conscious awareness during wakeful rest. Here, we test the hypothesis that temporal integration, measured as longterm memory in the history of neural activity, is another important quality underlying conscious awareness. For this aim, we study the temporal memory of blood oxygen level-dependent signals across the human nonrapid eye movement sleep cycle. Results reveal that this property gradually decreases from wakefulness to deep nonrapid eye movement sleep and that such decreases affect areas identified with default mode and attention networks. Although blood oxygen level-dependent spontaneous fluctuations exhibit nontrivial spatial organization, even during deep sleep, they also display a decreased temporal complexity in specific brain regions. Conversely, this result suggests that long-range temporal dependence might be an attribute of the spontaneous conscious mentation performed during wakeful rest.T he human brain displays complex spatiotemporal patterns of energy-consuming activity, even in the absence of an explicit task or stimulation (1). Large efforts have been devoted to the study of spontaneous neural activity encoded in the slow (∼0.1 Hz) fluctuations of the blood oxygen level-dependent (BOLD) signal, which are measured with functional MRI (fMRI). Nontrivial spatial organization of functional brain activity in resting state networks (RSNs) was consistently shown (2-4), comprising brain regions with high BOLD signal coherence and anatomical consistency with systems activated during task performance or stimulation (5).Remarkably, although human nonrapid eye movement (NREM) sleep is characterized by impaired awareness and reduced conscious mentation, organization into RSNs is preserved in light sleep (6) and to a large extent, deeper sleep stages (7, 8) (SI Appendix, Fig. S8.1). In particular, the default mode network (DMN; a set of task-deactivated regions implied with internal conscious cognitive processes) (9, 10) was repeatedly observed during deep sleep, albeit with reduced frontal connectivity (11,12). Although brain modules are preserved, even in the absence of conscious awareness, their functional integration is greatly impaired (8,13,14), which was predicted by an information integration account of consciousness (15). These results suggest that ongoing conscious mentation is not the only origin of RSN activity, whereas the level of consciousness is reflected in the interaction of functional networks.However, brain activity is not completely characterized in the spatial domain only. fMRI BOLD signals display rich temporal organization, including scale-free 1/f power spectra and long-range temporal autocorrelations (16)(17)(18), with activity at any given time being influenced by the previous history of the system up to several minutes into the past. These landmarks of complex information processing and rapid adaptability are shared by many systems found in nature (19,20). Evidence for such properties is also manifest...

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Section: Discussionmentioning

confidence: 99%

Breakdown of long-range temporal dependence in default mode and attention networks during deep sleep

Tagliazucchi

Wegner

Morzelewski

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

223

197

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“…The results of PET studies combined with EEG recordings also pointed toward decreased cerebellar activity during the transition from presleep wakefulness to SWS [30][31][32][33][34]. During NREM2, cerebellar fMRI signals co-occurred with K-complexes [35] and sleep spindles [36] as measured with EEG from the neocortex, whereas during NREM3, cerebellar fMRI signals co-occurred with slow waves at the neocortex [29] ( Figure 1A and [37]). The level of slow wave density associated with fMRI bold signals in the cerebellum was positively correlated with its gray matter volume as measured with voxelbased morphometry [37,38], providing possible explanations for intersubject variability of sleep EEG features as well as for interlobular differences within the cerebellum of individuals.…”

Section: Nrem-dependent Cerebellar Activitymentioning

confidence: 99%

“…The genes that showed increased expression during sleep in both the cerebellar cortex and cerebellar nuclei were involved in synaptic transmission, plasticity, and membrane trafficking and maintenance. [33,35]. For display purposes, the locations of the circle labels do not correspond to the reported MNI or Talairach coordinates.…”

Section: Clock and Wake-sleep-related Genes In The Cerebellummentioning

confidence: 99%

The Sleeping Cerebellum

Canto

Onuki

Bruinsma

et al. 2017

Trends in Neurosciences

154

105

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We sleep almost one-third of our lives and sleep plays an important role in critical brain functions like memory formation and consolidation. The role of sleep in cerebellar processing, however, constitutes an enigma in the field of neuroscience; we know little about cerebellar sleep-physiology, cerebro-cerebellar interactions during sleep, or the contributions of sleep to cerebellum-dependent memory consolidation. Likewise, we do not understand why cerebellar malfunction can lead to changes in the sleep-wake cycle and sleep disorders. In this review, we evaluate how sleep and cerebellar processing may influence one another and highlight which scientific routes and technical approaches could be taken to uncover the mechanisms underlying these interactions.

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“…A further question is the degree to which sleep and transient sleep events such as k-complexes and vertex sharp waves (59,60) drive the reported fMRI arousal patterns. To examine this question, we performed sleep scoring of the LFP data (SI Methods) and excluded time points corresponding to potential sleep and sleep events in monkey A.…”

Section: Respectively) (B)mentioning

confidence: 99%

Tracking brain arousal fluctuations with fMRI

Chang

Leopold

Schölvinck

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

302

299

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Changes in brain activity accompanying shifts in vigilance and arousal can interfere with the study of other intrinsic and task-evoked characteristics of brain function. However, the difficulty of tracking and modeling the arousal state during functional MRI (fMRI) typically precludes the assessment of arousal-dependent influences on fMRI signals. Here we combine fMRI, electrophysiology, and the monitoring of eyelid behavior to demonstrate an approach for tracking continuous variations in arousal level from fMRI data. We first characterize the spatial distribution of fMRI signal fluctuations that track a measure of behavioral arousal; taking this pattern as a template, and using the local field potential as a simultaneous and independent measure of cortical activity, we observe that the timevarying expression level of this template in fMRI data provides a close approximation of electrophysiological arousal. We discuss the potential benefit of these findings for increasing the sensitivity of fMRI as a cognitive and clinical biomarker.resting-state fMRI | spontaneous fluctuations | arousal | electrophysiology D uring both active task engagement and rest, the human brain exhibits fluctuations in neural activity that can be readily measured using functional MRI (fMRI). In recent years, examining the spatiotemporal organization of these fluctuations has generated novel insight into the functional architecture of the human brain and its changes with development and disease (1). A prominent approach for mapping this architecture is to study interregional correlations in the fMRI signal fluctuations, which, even during rest, appear to be indicative of networks supporting specific functions. However, despite the promise and rapidly increasing application of this technique in the endeavor of brain connectomics (2-4), its sensitivity and specificity are compromised by unexplained variability arising from multiple neural and nonneural sources (e.g., refs. 5-11). As a result, the interpretation of resting-state fMRI data and the efficacy of these data as a biomarker rely critically on understanding and accounting for such sources of variability (11-13).Changes in arousal, mediated by an interaction between the ascending arousal system and the neocortex, may strongly modulate neuronal activity in much of the brain (14-18). Indeed, changes in vigilance and arousal (hereafter described jointly as "arousal"), which can be especially prevalent during the passive and uncontrolled resting state, result in fMRI signal variability that may confound the extraction of functional networks (5, 19). For example, the amplitude and extent of correlations in restingstate fMRI data vary with EEG-and behaviorally defined indicators of drowsiness and light sleep (20)(21)(22)(23)(24)(25) and are altered by sleep deprivation (26-28) and caffeine-induced changes in arousal state (29). Distinct patterns of functional connectivity across multiple networks have been associated with distinct EEG-defined sleep stages (30, 31) with sufficient reliabilit...

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To wake or not to wake? The two-sided nature of the human K-complex

Cited by 108 publications

References 67 publications

Breakdown of long-range temporal dependence in default mode and attention networks during deep sleep

Breakdown of long-range temporal dependence in default mode and attention networks during deep sleep

The Sleeping Cerebellum

Tracking brain arousal fluctuations with fMRI

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