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

SLEEP ◽  
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).

Impact of 36 h of total sleep deprivation on resting-state dynamic functional connectivity

2018 ◽  
Vol 1688 ◽  
pp. 22-32 ◽  
Author(s):  
Huaze Xu ◽  
Hui Shen ◽  
Lubin Wang ◽  
Qi Zhong ◽  
Yu Lei ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Sleep Deprivation ◽  
Resting State ◽  
Dynamic Functional Connectivity ◽  
Total Sleep Deprivation ◽  
State Dynamic

scholarly journals Decreased Thalamocortical Functional Connectivity after 36 Hours of Total Sleep Deprivation: Evidence from Resting State fMRI

PLoS ONE ◽  
2013 ◽  
Vol 8 (10) ◽  
pp. e78830 ◽  
Author(s):  
Yongcong Shao ◽  
Lubin Wang ◽  
Enmao Ye ◽  
Xiao Jin ◽  
Wei Ni ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Sleep Deprivation ◽  
Resting State ◽  
Resting State Fmri ◽  
Total Sleep Deprivation

scholarly journals Unsupervised clustering of track-weighted dynamic functional connectivity reveals white matter substrates of functional connectivity dynamics

2021 ◽  
Author(s):  
Gianpaolo Antonio Basile ◽  
Salvatore Bertino ◽  
Victor Nozais ◽  
Alessia Bramanti ◽  
Rosella Ciurleo ◽  
...  
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Resting State ◽  
Time Course ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Dynamic Changes ◽  
Dynamic Functional Connectivity ◽  
Dynamic Connectivity

AbstractThe contribution of structural connectivity to functional connectivity dynamics is still far from being fully elucidated. Herein, we applied track-weighted dynamic functional connectivity (tw-dFC), a model integrating structural, functional, and dynamic connectivity, on high quality diffusion weighted imaging and resting-state fMRI data from two independent repositories. The tw-dFC maps were analyzed using independent component analysis, aiming at identifying spatially independent white matter components which support dynamic changes in functional connectivity. Each component consisted of a spatial map of white matter bundles that show consistent fluctuations in functional connectivity at their endpoints, and a time course representative of such functional activity. These components show high intra-subject, inter-subject, and inter-cohort reproducibility. We provided also converging evidence that functional information about white matter activity derived by this method can capture biologically meaningful features of brain connectivity organization, as well as predict higher-order cognitive performance.

Distinct dynamic functional connectivity patterns of pain and touch thresholds: A resting-state fMRI study

2019 ◽  
Vol 375 ◽  
pp. 112142
Author(s):  
Yueming Yuan ◽  
Li Zhang ◽  
Linling Li ◽  
Gan Huang ◽  
Ahmed Anter ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri ◽  
Dynamic Functional Connectivity ◽  
Fmri Study ◽  
Connectivity Patterns

scholarly journals Model testing for distinctive functional connectivity gradients with resting-state fMRI data

2018 ◽  
Author(s):  
Jonathan F. O’Rawe ◽  
Jaime S. Ide ◽  
Hoi-Chung Leung
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Functional Organization ◽  
Region Of Interest ◽  
Resting State Fmri ◽  
Model Testing ◽  
Fmri Data ◽  
Extensive Literature ◽  
Topographic Organization ◽  
Lateral Organization

AbstractIn accordance with the concept of topographic organization of neuroanatomical structures, there is an increased interest in estimating and delineating continuous changes in the functional connectivity patterns across neighboring voxels within a region of interest using resting-state fMRI data. Fundamental to this functional connectivity gradient analysis is the assumption that the functional organization is stable and uniform across the region of interest. To evaluate this assumption, we developed a model testing procedure to arbitrate between overlapping, shifted, or different topographic connectivity gradients across subdivisions of a structure. We tested the procedure using the striatum, a subcortical structure consisting of the caudate nucleus and putamen, in which an extensive literature, primarily from rodents and non-human primates, suggest to have a shared topographic organization of a single diagonal gradient. We found, across multiple resting state fMRI data samples of different spatial resolutions in humans, and one macaque resting state fMRI data sample, that the models with different functional connectivity gradients across the caudate and putamen was the preferred model. The model selection procedure was validated in control conditions of checkerboard subdivisions, demonstrating the expected overlapping gradient. More specifically, while we replicated the diagonal organization of the functional connectivity gradients in both the caudate and putamen, our analysis also revealed a medial-lateral organization within the caudate. Not surprisingly, performing the same analysis assuming a unitary gradient obfuscates the medial-lateral organization of the caudate, producing only a diagonal gradient. These findings demonstrate the importance of testing basic assumptions and evaluating interpretations across species. The significance of differential topographic gradients across the putamen and caudate and the medial-lateral gradient of the caudate in humans should be tested in future studies.

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