Development of a Large-Scale Functional Brain Network during Human Non-Rapid Eye Movement Sleep

Spoormaker, Victor I.; Schröter, Manuel; Gleiser, Pablo M.; Andrade, Kátia C.; Dresler, Martin; Wehrle, R.; Sämann, Philipp G.; Czisch, Michael

doi:10.1523/jneurosci.2015-10.2010

Cited by 255 publications

(285 citation statements)

References 58 publications

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“…These FC changes involve decreased within-DMN and within-dorsal attention network (DAN) connectivity, as well as reduced anticorrelation between DMN and DAN (27)(28)(29)(30). In the present work, we sought to demonstrate that spontaneous FC fluctuations in sleep-deprived persons correspond to fluctuations in arousal that coincide with pronounced SEC.…”

mentioning

confidence: 95%

Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states

Wang

Ong

Patanaik

et al. 2016

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

193

185

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Fluctuations in resting-state functional connectivity occur but their behavioral significance remains unclear, largely because correlating behavioral state with dynamic functional connectivity states (DCS) engages probes that disrupt the very behavioral state we seek to observe. Observing spontaneous eyelid closures following sleep deprivation permits nonintrusive arousal monitoring. During periods of low arousal dominated by eyelid closures, sliding-window correlation analysis uncovered a DCS associated with reduced withinnetwork functional connectivity of default mode and dorsal/ventral attention networks, as well as reduced anticorrelation between these networks. Conversely, during periods when participants' eyelids were wide open, a second DCS was associated with less decoupling between the visual network and higher-order cognitive networks that included dorsal/ventral attention and default mode networks. In subcortical structures, eyelid closures were associated with increased connectivity between the striatum and thalamus with the ventral attention network, and greater anticorrelation with the dorsal attention network. When applied to task-based fMRI data, these two DCS predicted interindividual differences in frequency of behavioral lapsing and intraindividual temporal fluctuations in response speed. These findings with participants who underwent a night of total sleep deprivation were replicated in an independent dataset involving partially sleep-deprived participants. Fluctuations in functional connectivity thus appear to be clearly associated with changes in arousal. T he existence of large-scale functional brain networks is evidenced by well-defined spatial patterns of correlated bloodoxygenation level-dependent (BOLD) signal fluctuation in fMRI data (1). Recent work has shown that functional connectivity (FC) within and between brain networks is dynamic, corresponding to the observation that even while we are performing a task, our mental focus fluctuates (2). Fluctuation of fMRI-based FC occurs over tens of seconds (3, 4) and exhibits different patterns across conscious and unconscious states (5, 6). Furthermore, just as interindividual differences in stationary FC relate to variation in human behavior and cognition (7-10), it seems likely that recurring patterns (11) of fluctuating FC have behavioral significance.Temporal fluctuations in FC can arise from conscious mental activity (12), episodes of random synchrony (3), or simply timevarying levels of physiological noise (13,14). The association between BOLD signal fluctuation in the default mode network (DMN) and mind-wandering episodes (15-17) has prompted investigations into the behavioral correlates of spontaneous resting-state FC fluctuations (11,18). Although these fluctuations in FC have been shown to correlate with several physiological markers, such as electroencephalogram (EEG) power, magnetoencephalography (MEG) power, and heart rate variability (19-21), their behavioral significance remains unclear.A key obstacle to elucidating clear FC...

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mentioning

confidence: 95%

Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states

Wang

Ong

Patanaik

et al. 2016

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

193

185

View full text Add to dashboard Cite

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“…Previous studies have found that the thalamocortical network is involved in the transitional state from wakefulness to sleep (Timofeev, 2011), and that the left and right thalamus show quite different changing courses over different stages of sleep (Spoormaker et al, 2010). For example, when shifting from an early sleep stage to slow-wave sleep, the left thalamus has a differential connection with 50 regions, whereas the right thalamus has only 22 connections changed (Spoormaker et al, 2010). Second, the left thalamus might be more vulnerable to the effects of long-term sleep loss.…”

Section: Discussionmentioning

confidence: 97%

Measuring individual morphological relationship of cortical regions

Kong

Wang

Huang

et al. 2014

Journal of Neuroscience Methods

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h i g h l i g h t s• A new metric was proposed to quantify individual morphological relations of regions.• The new metric is indexed as the similarity of different morphological distributions.• We estimate the morphological distribution from individual MRI image.• The metric seemed to have potential application to individual differences studies. a r t i c l e i n f o b s t r a c tBackground: Although local features of brain morphology have been widely investigated in neuroscience, the inter-regional relations in brain morphology have rarely been investigated, especially not for individual participants. New method: In this paper, we proposed a novel framework for investigating this relation based on an individual's magnetic resonance imaging (MRI) data. The key idea was to estimate the probability density function (PDF) of local morphological features within a brain region to provide a global description of this region. Then, the inter-regional relations were quantified by calculating the similarity of the PDFs for pairs of regions based on the Kullback-Leibler (KL) divergence. Results: For illustration, we applied this approach to a pre-post intervention study to investigate the longitudinal changes in morphological relations after long-term sleep deprivation. The results suggest the potential application of this new method for studies on individual differences in brain structure.Comparison with existing methods: The current method can be employed to estimate individual morphological relations between regions, which have been largely ignored by previous studies. Conclusions: Our morphological relation metric, as a novel quantitative biomarker, can be used to investigate normal individual variability and even within-individual alterations/abnormalities in brain structure.

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“…Impairments in performance after sleep loss could be attributed to the changes in functional connectivity in various neural networks [61,62] and it could also be due to neurons going offline at local level [63].…”

Section: Discussionmentioning

confidence: 99%

Reduced Theta Coherence and P Wave Ratio Linked to Memory Deficits After Sleep Deprivation in Rat Model

2017

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Purpose It is well established that loss of sleep leads to lapses in learning and memory. The brain regions including hippocampus, amygdala and the prefrontal cortex are associated with memory processes. However, there are not many studies on the changes in neuronal communications between these brain areas during sleep deprivation. Methods In this study, changes in the P waves, and theta coherence among the hippocampus, amygdala and the prefrontal cortex, were studied before and after exposing the animals to total sleep deprivation of 24 h. Changes in reference and working memory were evaluated after sleep deprivation.Results Concomitant with a rebound increase in non-REM sleep after total sleep deprivation, there was a decrease in the coherence of theta waves between all the studied areas, especially between hippocampus and amygdala. The ratio of P wave cluster to singlet was reduced. There was a decrease in 'correct memory performance' after sleep deprivation. Conclusions This study showed that after sleep deprivation, there was a correlation in the cognitive deficits and decrease in the coherence of theta waves, and the ratio of P wave cluster to singlet. Impairments in brain function, especially cognition, after sleep loss could be attributed to changes in functional connectivity between these neural networks. This study highlighted important functions of sleep in maintaining an appropriate synchrony between various neuronal networks during REM sleep.

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Development of a Large-Scale Functional Brain Network during Human Non-Rapid Eye Movement Sleep

Cited by 255 publications

References 58 publications

Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states

Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states

Measuring individual morphological relationship of cortical regions

Reduced Theta Coherence and P Wave Ratio Linked to Memory Deficits After Sleep Deprivation in Rat Model

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