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

AbstractDynamic Functional Connectivity (dFC) in the resting state (rs) is considered as a correlate of cognitive processing. Describing dFC as a flow across morphing connectivity configurations, our notion of dFC speed quantifies the rate at which FC networks evolve in time. Here we probe the hypothesis that variations of rs dFC speed and cognitive performance are selectively interrelated within specific functional subnetworks.In particular, we focus on Sleep Deprivation (SD) as a reversible model of cognitive dysfunction. We found that whole-brain level (global) dFC speed significantly slows down after 24h of SD. However, the reduction in global dFC speed does not correlate with variations of cognitive performance in individual tasks, which are subtle and highly heterogeneous. On the contrary, we found strong correlations between performance variations in individual tasks –including Rapid Visual Processing (RVP, assessing sustained visual attention)– and dFC speed quantified at the level of functional subnetworks of interest. Providing a compromise between classic static FC (no time) and global dFC (no space), modular dFC speed analyses allow quantifying a different speed of dFC reconfiguration independently for sub-networks overseeing different tasks. Importantly, we found that RVP performance robustly correlates with the modular dFC speed of a characteristic frontoparietal module.HighlightsSleep Deprivation (SD) slows down the random walk in FC space implemented by Dynamic Functional Connectivity (dFC) at rest.Whole-brain level slowing of dFC speed does not selectively correlate with fine and taskspecific changes in performanceWe quantify dFC speed separately for different link-based modules coordinated by distinct regional “meta-hubs”Modular dFC speed variations capture subtle and task-specific variations of cognitive performance induced by SD.Author summaryWe interpreted dynamic Functional Connectivity (dFC) as a random walk in the space of possible FC networks performed with a quantifiable “speed”.Here, we analyze a fMRI dataset in which subjects are scanned and cognitively tested both before and after Sleep Deprivation (SD), used as a reversible model of cognitive dysfunction. While global dFC speed slows down after a sleepless night, it is not a sufficiently sensitive metric to correlate with fine and specific cognitive performance changes. To boost the capacity of dFC speed analyses to account for fine and specific cognitive decline, we introduce the notion of modular dFC speed. Capitalizing on an edge-centric measure of functional connectivity, which we call Meta-Connectivity, we isolate subgraphs of FC describing relatively independent random walks (dFC modules) and controlled by distinct “puppet masters” (meta-hubs). We then find that variations of the random walk speed of distinct dFC modules now selectively correlate with SD-induced variations of performance in the different tasks. This is in agreement with the fact that different subsystems – distributed but functionally distinct– oversee different tasks.The high sensitivity of modular dFC analyses bear promise of future applications to the early detection and longitudinal characterization of pathologies such as Alzheimer’s disease.

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Psilocybin modulation of dynamic functional connectivity is associated with plasma psilocin and subjective effects

10.1101/2021.12.17.21267992 ◽

2021 ◽

Author(s):

Anders S Olsen ◽

Anders Lykkebo-Valloee ◽

Brice Ozenne ◽

Martin K Madsen ◽

Dea Siggaard Stenbaek ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Dwell Time ◽

Brain State ◽

Healthy Individuals ◽

Acute Effects ◽

Dynamic Functional Connectivity ◽

Negatively Associated ◽

State Dynamic ◽

Brain States

Background: Psilocin, the neuroactive metabolite of psilocybin, is a serotonergic psychedelic that induces an acute altered state of consciousness, evokes lasting changes in mood and personality in healthy individuals, and has potential as an antidepressant treatment. Examining the acute effects of psilocin on resting-state dynamic functional connectivity implicates network-level connectivity motifs that may underlie acute and lasting behavioral and clinical effects. Aim: Evaluate the association between resting-state dynamic functional connectivity (dFC) characteristics and plasma psilocin level (PPL) and subjective drug intensity (SDI) before and right after intake of a psychedelic dose of psilocybin in healthy humans. Methods: Fifteen healthy individuals completed the study. Before and at multiple time points after psilocybin intake, we acquired 10-minute resting-state blood-oxygen-level-dependent functional magnetic resonance imaging scans. Leading Eigenvector Dynamics Analysis (LEiDA) and diametrical clustering were applied to estimate discrete, sequentially active brain states. We evaluated associations between the fractional occurrence of brain states during a scan session and PPL and SDI using linear mixed-effects models. We examined associations between brain state dwell time and PPL and SDI using frailty Cox proportional hazards survival analysis. Results: Fractional occurrences for two brain states characterized by lateral frontoparietal and medial fronto-parietal-cingulate coherence were statistically significantly negatively associated with PPL and SDI. Dwell time for these brain states was negatively associated with SDI and, to a lesser extent, PPL. Conversely, fractional occurrence and dwell time of a fully connected brain state was positively associated with PPL and SDI. Conclusion: Our findings suggest that the acute perceptual psychedelic effects induced by psilocybin may stem from drug-level associated decreases in the occurrence and duration of lateral and medial frontoparietal connectivity motifs in exchange for increases in a uniform connectivity structure. We apply and argue for a modified approach to modeling eigenvectors produced by LEiDA that more fully acknowledges their underlying structure. Together these findings contribute to a more comprehensive neurobiological framework underlying acute effects of serotonergic psychedelics.

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Resting State Dynamic Functional Connectivity in Neurodegenerative Conditions: A Review of Magnetic Resonance Imaging Findings

Frontiers in Neuroscience ◽

10.3389/fnins.2019.00657 ◽

2019 ◽

Vol 13 ◽

Cited By ~ 11

Author(s):

Massimo Filippi ◽

Edoardo G. Spinelli ◽

Camilla Cividini ◽

Federica Agosta

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Functional Connectivity ◽

Resting State ◽

Imaging Findings ◽

Resonance Imaging ◽

Dynamic Functional Connectivity ◽

State Dynamic

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Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach

Frontiers in Neuroscience ◽

10.3389/fnins.2020.00191 ◽

2020 ◽

Vol 14 ◽

Cited By ~ 1

Author(s):

Baoyu Yan ◽

Xiaopan Xu ◽

Mengwan Liu ◽

Kaizhong Zheng ◽

Jian Liu ◽

...

Keyword(s):

Machine Learning ◽

Major Depression ◽

Functional Connectivity ◽

Resting State ◽

Learning Approach ◽

Dynamic Functional Connectivity ◽

State Dynamic ◽

Machine Learning Approach ◽

Quantitative Identification

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0143 Vigilance Declines Following Sleep Deprivation are Associated with Two Previously Identified Dynamic Connectivity States

SLEEP ◽

10.1093/sleep/zsaa056.141 ◽

2020 ◽

Vol 43 (Supplement_1) ◽

pp. A56-A57

Author(s):

J Teng ◽

J Ong ◽

A Patanaik ◽

J Zhou ◽

M Chee ◽

...

Keyword(s):

Functional Connectivity ◽

Sleep Deprivation ◽

Sustained Attention ◽

Resting State ◽

Resting State Fmri ◽

Fmri Data ◽

Dynamic Functional Connectivity ◽

Arousal State ◽

Total Sleep Deprivation ◽

Dynamic Connectivity

Abstract Introduction Dynamic functional connectivity (DFC) analysis of resting-state fMRI data has been successfully used to track fluctuations in arousal in the human brain. Changes in DFC have also been reported with acute sleep deprivation. Here, we demonstrate that dynamic connectivity states (DCS) previously related to arousal are reproducible, and are associated with individual differences in sustained attention declines after one night of total sleep deprivation. Methods 32 participants underwent two counterbalanced resting-state fMRI scans: during rested wakefulness (RW) and following total sleep deprivation (SD). They also completed the Psychomotor Vigilance Test (PVT), a sustained attention task that is highly sensitive to the effects of sleep loss. SD vulnerability was computed as the decrease in response speed (∆RS) and increase in lapses (∆lapse) in SD compared with RW. Dynamic functional connectivity analysis was conducted on rs-fMRI data. Connectivity matrices were clustered to obtain 5 prototypical DCS. We calculated the proportion of time participants spent in each of these DCS, as well as how often participants transitioned between DCSs. Relationships between SD vulnerability and connectivity metrics were then correlated. Results We recovered two DCS that were highly similar (ρ = .89-.91) to arousal-related DCS observed in previous work (high arousal state (HAS); low arousal state (LAS)). After sleep deprivation, the proportion of time spent in the LAS increased significantly (t29=3.16, p=.0039), while there was no significant change in HAS (t29=-1.43, p=.16). We observed significantly more state transitions in RW compared with SD. Change in LAS and HAS across sleep conditions correlated significantly with SD vulnerability (ΔLASxΔRS: r=-0.64, p<.0001; ΔLASxΔlapse: r=0.43, p=.018; ΔHASxΔRS; r=0.43, p=.019; ΔHASxΔlapse; r=-0.39, p=.033). Finally, Δ%transitions was correlated with ΔRS but not Δlapse. Conclusion This study adds to the evidence that two specific reproducible DCS are robust markers of arousal and attention, and may be useful indicators of SD vulnerability. Support This work was supported by the National Medical Research Council, Singapore (STaR/0015/2013), and the National Research Foundation Science of Learning (NRF2016-SOL002-001).

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