Modular slowing of resting-state dynamic functional connectivity as a marker of cognitive dysfunction induced by sleep deprivation

Lombardo, Diego; Cassé-Perrot, Catherine; Ranjeva, Jean‐Philippe; Troter, Arnaud Le; Guye, Maxime; Wirsich, Jonathan; Payoux, Pierre; Bartrés‐Faz, David; Bordet, Régis; Richardson, Jill C.; Félician, Olivier; Jirsa, Viktor K.; Blin, Olivier; Didic, Mira; Battaglia, Demian

doi:10.1016/j.neuroimage.2020.117155

Cited by 34 publications

(62 citation statements)

References 70 publications

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“…Wake-state instability is proposed to account for the more variable behavioral performance after SD, which is driven by the competition between staying alert and falling asleep (Doran et al, 2001). This hypothesis is supported by recent dynamic functional connectivity (dFC) studies that can capture momentto-moment brain activity variability in SD (Lombardo et al, 2020). Previous studies have reported connectivity states of specific brain networks related to vigilance (the high arousal state and the low arousal state), indicating that the dynamics of these states in SD were related to the temporal fluctuations of vigilance during rest and auditory vigilance tasks (Wang et al, 2016;Patanaik et al, 2018).…”

Section: Introductionmentioning

confidence: 57%

“…This further indicated that the distribution of saliency in internal mental events was upregulated and that the transition between SN and attention network was also increased to meet the demands of a longer awake state. It should be noted that previous restingstate fMRI studies have reported decreases of transitions between dFC states following SD (Teng et al, 2019;Lombardo et al, 2020). One possible reason for this inconsistent finding is that the moving average dFC in fMRI analysis may have smoothened the rapid state transitions associated with wake-state instability (Teng et al, 2019).…”

Section: Discussionmentioning

confidence: 94%

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Alteration in Resting-State EEG Microstates Following 24 Hours of Total Sleep Deprivation in Healthy Young Male Subjects

Wang

2021

Front. Hum. Neurosci.

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Purpose: The cognitive effects of total sleep deprivation (TSD) on the brain remain poorly understood. Electroencephalography (EEG) is a very useful tool for detecting spontaneous brain activity in the resting state. Quasi-stable electrical distributions, known as microstates, carry useful information about the dynamics of large-scale brain networks. In this study, microstate analysis was used to study changes in brain activity after 24 h of total sleep deprivation.Participants and Methods: Twenty-seven healthy volunteers were recruited and underwent EEG scans before and after 24 h of TSD. Microstate analysis was applied, and six microstate classes (A–F) were identified. Topographies and temporal parameters of the microstates were compared between the rested wakefulness (RW) and TSD conditions.Results: Microstate class A (a right-anterior to left-posterior orientation of the mapped field) showed lower global explained variance (GEV), frequency of occurrence, and time coverage in TSD than RW, whereas microstate class D (a fronto-central extreme location of the mapped field) displayed higher GEV, frequency of occurrence, and time coverage in TSD compared to RW. Moreover, subjective sleepiness was significantly negatively correlated with the microstate parameters of class A and positively correlated with the microstate parameters of class D. Transition analysis revealed that class B exhibited a higher probability of transition than did classes D and F in TSD compared to RW.Conclusion: The observation suggests alterations of the dynamic brain-state properties of TSD in healthy young male subjects, which may serve as system-level neural underpinnings for cognitive declines in sleep-deprived subjects.

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Section: Introductionmentioning

confidence: 57%

Section: Discussionmentioning

confidence: 94%

Alteration in Resting-State EEG Microstates Following 24 Hours of Total Sleep Deprivation in Healthy Young Male Subjects

Wang

2021

Front. Hum. Neurosci.

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“…The generation of a variety of dynamic regimes from changes in the global 'environmental' parameters is biologically realistic. For example, sleep deprivation is associated with changes in the speed of the F C evolution [72]. Accordingly, distinct features of the dF C should correspond to a di↵erent underlying neuroelectric organization (and perhaps to distinct mechanisms of communication across the brain regions [73]).…”

Section: Neuronal Cascades and Emerging Functional Networkmentioning

confidence: 99%

Neuronal cascades shape whole-brain functional dynamics at rest

Rabuffo

Fousek

Bernard

et al. 2020

Preprint

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At rest, mammalian brains display a rich complex spatiotemporal behavior, which is reminiscent of healthy brain function and has provided nuanced understandings of several major neurological conditions. Despite the increasingly detailed phenomenological documentation of the brain’s resting state, its principle underlying causes remain unknown. To establish causality, we link structurally defined features of a brain network model to neural activation patterns and their variability. For the mouse, we use a detailed connectome-based model and simulate the resting state dynamics for neural sources and whole brain imaging signals (Blood-Oxygen-Level-Dependent (BOLD), Electroencephalography (EEG)). Under conditions of near-criticality, characteristic neuronal cascades form spontaneously and propagate through the network. The largest neuronal cascades produce short-lived but robust co-fluctuations at pairs of regions across the brain. During these co-activation episodes, long-lasting functional networks emerge giving rise to epochs of stable resting state networks correlated in time. Sets of neural cascades are typical for a resting state network, but different across. We experimentally confirm the existence and stability of functional connectivity epochs comprising BOLD co-activation bursts in mice (N=19). We further demonstrate the leading role of the neuronal cascades in a simultaneous EEG/fMRI data set in humans (N=15), explaining a large part of the variability of functional connectivity dynamics. We conclude that short-lived neuronal cascades are a major robust dynamic component contributing to the organization of the slowly evolving spontaneous fluctuations in brain dynamics at rest.

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“…Third, the simulated collective dynamics give rise to meta-stability of FC along time, i.e., to a non-trivially structured dFC, which alternates between “knots” of transiently slowed-down FC network reconfiguration and “leaps” of accelerated reconfigurations. Such non-triviality of dFC can be detected by the inspection of the so-called dFC matrix ( Hansen et al, 2015 ; Arbabyazd et al, 2020 ; Battaglia et al, 2020 ; Lombardo et al, 2020 ), representing the similarity between FC matrices computed at different time-windows (see Materials and Methods). In this dFC matrix analysis, dFC knots are visualized as blocks with high inter-FC correlations, while dFC leaps give rise to stripes of low inter-FC correlation.…”

Section: Resultsmentioning

confidence: 99%

Virtual Connectomic Datasets in Alzheimer’s Disease and Aging Using Whole-Brain Network Dynamics Modelling

Arbabyazd¹,

Shen

Wang

et al. 2021

eNeuro

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Modular slowing of resting-state dynamic functional connectivity as a marker of cognitive dysfunction induced by sleep deprivation

Cited by 34 publications

References 70 publications

Alteration in Resting-State EEG Microstates Following 24 Hours of Total Sleep Deprivation in Healthy Young Male Subjects

Alteration in Resting-State EEG Microstates Following 24 Hours of Total Sleep Deprivation in Healthy Young Male Subjects

Neuronal cascades shape whole-brain functional dynamics at rest

Virtual Connectomic Datasets in Alzheimer’s Disease and Aging Using Whole-Brain Network Dynamics Modelling

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