Slow brain oscillations of sleep, resting state, and vigilance

Someren, Eus J.W. Van; Werf, Ysbrand D. van der; Roelfsema, Pieter R.; Mansvelder, Huibert D.; Silva, F.H. Lopes da

doi:10.1016/b978-0-444-53839-0.00001-6

Cited by 47 publications

(30 citation statements)

References 86 publications

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“…Slower brain oscillations in the delta and sub-delta range appear to have a physiological role in sensory processing and cognition, even in the absence of environmental stimulation (for example, the default mode network in resting EEG and fMRI studies) [75]. In autistic patients, Coben and colleagues found the most significant coherence changes in autistic patients in the delta and theta bands compared to controls [46].…”

Section: Discussionmentioning

confidence: 99%

Brain functional networks in syndromic and non-syndromic autism: a graph theoretical study of EEG connectivity

Peters

Taquet

Vega

et al. 2013

BMC Med

163

140

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BackgroundGraph theory has been recently introduced to characterize complex brain networks, making it highly suitable to investigate altered connectivity in neurologic disorders. A current model proposes autism spectrum disorder (ASD) as a developmental disconnection syndrome, supported by converging evidence in both non-syndromic and syndromic ASD. However, the effects of abnormal connectivity on network properties have not been well studied, particularly in syndromic ASD. To close this gap, brain functional networks of electroencephalographic (EEG) connectivity were studied through graph measures in patients with Tuberous Sclerosis Complex (TSC), a disorder with a high prevalence of ASD, as well as in patients with non-syndromic ASD.MethodsEEG data were collected from TSC patients with ASD (n = 14) and without ASD (n = 29), from patients with non-syndromic ASD (n = 16), and from controls (n = 46). First, EEG connectivity was characterized by the mean coherence, the ratio of inter- over intra-hemispheric coherence and the ratio of long- over short-range coherence. Next, graph measures of the functional networks were computed and a resilience analysis was conducted. To distinguish effects related to ASD from those related to TSC, a two-way analysis of covariance (ANCOVA) was applied, using age as a covariate.ResultsAnalysis of network properties revealed differences specific to TSC and ASD, and these differences were very consistent across subgroups. In TSC, both with and without a concurrent diagnosis of ASD, mean coherence, global efficiency, and clustering coefficient were decreased and the average path length was increased. These findings indicate an altered network topology. In ASD, both with and without a concurrent diagnosis of TSC, decreased long- over short-range coherence and markedly increased network resilience were found.ConclusionsThe altered network topology in TSC represents a functional correlate of structural abnormalities and may play a role in the pathogenesis of neurological deficits. The increased resilience in ASD may reflect an excessively degenerate network with local overconnection and decreased functional specialization. This joint study of TSC and ASD networks provides a unique window to common neurobiological mechanisms in autism.

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Section: Discussionmentioning

confidence: 99%

Brain functional networks in syndromic and non-syndromic autism: a graph theoretical study of EEG connectivity

Peters

Taquet

Vega

et al. 2013

BMC Med

163

140

View full text Add to dashboard Cite

show abstract

“…In contrast to fMRI, these methods measure neuronal activity directly and have excellent temporal resolution with bandwidths typically of 1–100 Hz (compared to the 0.001–0.5 Hz range accessible to fMRI). This is especially valuable as both very low and very high frequency oscillations have been shown to have neurophysiological significance (Bragin, Engel, & Staba, 2010; Van Someren, Van Der Werf, Roelfsema, Mansvelder, & da Silva, 2011). The trade-off, however, is that EEG and MEG have worse spatial resolution (on the order of millimetres or centimetres) and the signal is not recorded at its anatomical source but rather at surface sensors, which complicates both the interpretation of results and the separation of true signal from so-called volume conduction effects spreading each signal source across several sensors.…”

Section: Normative Development Of Brain Networkmentioning

confidence: 99%

Annual Research Review: Growth connectomics – the organization and reorganization of brain networks during normal and abnormal development

Vértes

Bullmore

2014

Child Psychology Psychiatry

183

152

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BackgroundWe first give a brief introduction to graph theoretical analysis and its application to the study of brain network topology or connectomics. Within this framework, we review the existing empirical data on developmental changes in brain network organization across a range of experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans).SynthesisWe discuss preliminary evidence and current hypotheses for how the emergence of network properties correlates with concomitant cognitive and behavioural changes associated with development. We highlight some of the technical and conceptual challenges to be addressed by future developments in this rapidly moving field. Given the parallels previously discovered between neural systems across species and over a range of spatial scales, we also review some recent advances in developmental network studies at the cellular scale. We highlight the opportunities presented by such studies and how they may complement neuroimaging in advancing our understanding of brain development. Finally, we note that many brain and mind disorders are thought to be neurodevelopmental in origin and that charting the trajectory of brain network changes associated with healthy development also sets the stage for understanding abnormal network development.ConclusionsWe therefore briefly review the clinical relevance of network metrics as potential diagnostic markers and some recent efforts in computational modelling of brain networks which might contribute to a more mechanistic understanding of neurodevelopmental disorders in future.

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“…Intracellularly, this oscillation was seen as a relatively rapid switch in the values of the membrane potential between silent (hyperpolarizing) and active (depoarizing by 7–10 mV) state. The two states are referred to as UP and DOWN states and the >1 Hz alternation between them is generally referred to as the slow oscillation (Van Someren et al, 2011). Usually, membrane potential fluctuations around the Up state are of higher amplitude, whereas the Down state is relatively free of membrane fluctuations (Steriade et al, 1993b).…”

Section: Slow Wave Activitymentioning

confidence: 99%

Large Scale Cortical Functional Networks Associated with Slow-Wave and Spindle-Burst-Related Spontaneous Activity

McVea

Murphy

Mohajerani

2016

Front. Neural Circuits

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Cortical sensory systems are active with rich patterns of activity during sleep and under light anesthesia. Remarkably, this activity shares many characteristics with those present when the awake brain responds to sensory stimuli. We review two specific forms of such activity: slow-wave activity (SWA) in the adult brain and spindle bursts in developing brain. SWA is composed of 0.5–4 Hz resting potential fluctuations. Although these fluctuations synchronize wide regions of cortex, recent large-scale imaging has shown spatial details of their distribution that reflect underlying cortical structural projections and networks. These networks are regulated, as prior awake experiences alter both the spatial and temporal features of SWA in subsequent sleep. Activity patterns of the immature brain, however, are very different from those of the adult. SWA is absent, and the dominant pattern is spindle bursts, intermittent high frequency oscillations superimposed on slower depolarizations within sensory cortices. These bursts are driven by intrinsic brain activity, which act to generate peripheral inputs, for example via limb twitches. They are present within developing sensory cortex before they are mature enough to exhibit directed movements and respond to external stimuli. Like in the adult, these patterns resemble those evoked by sensory stimulation when awake. It is suggested that spindle-burst activity is generated purposefully by the developing nervous system as a proxy for true external stimuli. While the sleep-related functions of both slow-wave and spindle-burst activity may not be entirely clear, they reflect robust regulated phenomena which can engage select wide-spread cortical circuits. These circuits are similar to those activated during sensory processing and volitional events. We highlight these two patterns of brain activity because both are prominent and well-studied forms of spontaneous activity that will yield valuable insights into brain function in the coming years.

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Slow brain oscillations of sleep, resting state, and vigilance

Cited by 47 publications

References 86 publications

Brain functional networks in syndromic and non-syndromic autism: a graph theoretical study of EEG connectivity

Brain functional networks in syndromic and non-syndromic autism: a graph theoretical study of EEG connectivity

Annual Research Review: Growth connectomics – the organization and reorganization of brain networks during normal and abnormal development

Large Scale Cortical Functional Networks Associated with Slow-Wave and Spindle-Burst-Related Spontaneous Activity

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