Cerebral functional networks during sleep in young and older individuals

Daneault, Véronique; Orban, Pierre; Martin, Nicolas; Dansereau, Christian; Godbout, Jonathan P.; Pouliot, Philippe; Dickinson, Philip; Gosselin, Nadia; Vandewalle, Gilles; Maquet, Pierre; Lina, Jean‐Marc; Doyon, Julien; Bellec, Pierre; Carrier, Julie

doi:10.1038/s41598-021-84417-0

Cited by 15 publications

(11 citation statements)

References 73 publications

(45 reference statements)

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“…The findings reported are not expected to be driven by arousal differences between young and old subjects. Indeed, a recent study (Daneault et al, 2021) has found no statistically significant differences in FC between young and old people and no interaction with age when comparing wakefulness and N1 during 100 minutes of eye‐closed acquisition. It is of note that the subjects employed in our study were asked to keep their eyes‐open for the entire duration of the acquisition and this should prevent N1 stage (Gu, Han, & Liu, 2019).…”

Section: Discussionmentioning

confidence: 94%

“…It is of note that the subjects employed in our study were asked to keep their eyes‐open for the entire duration of the acquisition and this should prevent N1 stage (Gu, Han, & Liu, 2019). However, even if we assume that some of them fell into a N1 sleep stage, it does not foresee significant differences in FC between young and old, based on (Daneault et al, 2021).…”

Section: Discussionmentioning

confidence: 97%

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Unveiling whole‐brain dynamics in normal aging through Hidden Markov Models

Moretto

Silvestri

Zangrossi

et al. 2021

Human Brain Mapping

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During normal aging, the brain undergoes structural and functional changes. Many studies applied static functional connectivity (FC) analysis on resting state functional magnetic resonance imaging (rs‐fMRI) data showing a link between aging and the increase of between‐networks connectivity. However, it has been demonstrated that FC is not static but varies over time. By employing the dynamic data‐driven approach of Hidden Markov Models, this study aims to investigate how aging is related to specific characteristics of dynamic brain states. Rs‐fMRI data of 88 subjects, equally distributed in young and old were analyzed. The best model resulted to be with six states, which we characterized not only in terms of FC and mean BOLD activation, but also uncertainty of the estimates. We found two states were mostly occupied by young subjects, whereas three other states by old subjects. A graph‐based analysis revealed a decrease in strength with the increase of age, and an overall more integrated topology of states occupied by old subjects. Indeed, while young subjects tend to cycle in a loop of states characterized by a high segregation of the networks, old subjects' loops feature high integration, with a crucial intermediary role played by the dorsal attention network. These results suggest that the employed mathematical approach captures the complex and rich brain's dynamics underpinning the aging process.

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 97%

Unveiling whole‐brain dynamics in normal aging through Hidden Markov Models

Moretto

Silvestri

Zangrossi

et al. 2021

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…Furthermore, direct stimulation of individual thalamic nuclei has been shown to drive arousal state transitions 24,[30][31][32][33][34][35][36][37][38][39][40][41][42] . Human imaging studies have further confirmed that spontaneous thalamic activity shifts across arousal state: functional magnetic resonance imaging (fMRI) studies have shown altered thalamocortical and intrathalamic connectivity during sleep [43][44][45][46] , and distinct spatiotemporal dynamics linked to arousal state fluctuations [47][48][49] . Intriguingly, the thalamus has also been shown to activate during arousal state transitions in humans 50,51 .…”

mentioning

confidence: 94%

A temporal sequence of thalamic activity unfolds at transitions in behavioral arousal state

et al. 2022

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Awakening from sleep reflects a profound transformation in neural activity and behavior. The thalamus is a key controller of arousal state, but whether its diverse nuclei exhibit coordinated or distinct activity at transitions in behavioral arousal state is unknown. Using fast fMRI at ultra-high field (7 Tesla), we measured sub-second activity across thalamocortical networks and within nine thalamic nuclei to delineate these dynamics during spontaneous transitions in behavioral arousal state. We discovered a stereotyped sequence of activity across thalamic nuclei and cingulate cortex that preceded behavioral arousal after a period of inactivity, followed by widespread deactivation. These thalamic dynamics were linked to whether participants subsequently fell back into unresponsiveness, with unified thalamic activation reflecting maintenance of behavior. These results provide an outline of the complex interactions across thalamocortical circuits that orchestrate behavioral arousal state transitions, and additionally, demonstrate that fast fMRI can resolve sub-second subcortical dynamics in the human brain.

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“…Finding distinguishing features of the human brain mechanisms considering different states of mind seems to be a reliable approach toward unraveling the neural correlate of consciousness. In fact, several studies using neuroimaging data have shown consistent correlations between mental states and functional brain networks in humans [1][2][3][4][5][6][7]. Emergent behaviors on networked systems are determined by their topological structure.…”

Section: Introductionmentioning

confidence: 99%

Information parity increases on functional brain networks under influence of a psychedelic substance

Viol

Viswanathan²,

Soldatkina³

et al. 2023

J. Phys. Complex.

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The physical basis of consciousness is one of the most intriguing open questions that contemporary science aims to solve. By approaching the brain as an interactive information system, complex network theory has greatly contributed to understand brain process in different states of mind. We study an non-ordinary state of mind by comparing resting-state functional brain networks of individuals in two different conditions: before and after the ingestion of the psychedelic brew Ayahuasca. In order to quantify the functional, statistical symmetries between brain region connectivity, we calculate the pairwise information parity of the functional brain networks. Unlike the usual approach to quantitative network analysis that considers only local or global scales, information parity instead quantifies pairwise statistical similarities over the entire network structure. We find an increase in the average information parity on brain networks of individuals under psychedelic influences. Notably, the information parity between regions from the limbic system and frontal cortex is consistently higher for all the individuals while under the psychedelic influence. These finding suggest that the resemblance of statistical influences between pair of brain regions activities tends to increase under Ayahuasca effects. This can be interpreted as a mechanisms to maintain the network functional resilience.

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Cerebral functional networks during sleep in young and older individuals

Cited by 15 publications

References 73 publications

Unveiling whole‐brain dynamics in normal aging through Hidden Markov Models

Unveiling whole‐brain dynamics in normal aging through Hidden Markov Models

A temporal sequence of thalamic activity unfolds at transitions in behavioral arousal state

Information parity increases on functional brain networks under influence of a psychedelic substance

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