Name
Title | ||
|---|---|---|
Vigilance declines following sleep deprivation are associated with two previously identified dynamic connectivity states. |
Abstract | ||
|---|---|---|
Robustly linking dynamic functional connectivity (DFC) states to behaviour is important for establishing the utility of the method as a functional measurement. We previously used a sliding window approach to identify two dynamic connectivity states (DCS) related to vigilance. A new sample of 32 healthy participants underwent two sets of task-free functional magnetic resonance imaging (fMRI) scans, once in a well-rested state and once after a single night of total sleep deprivation. Using a temporal difference method, DFC and clustering analysis on the task-free fMRI data revealed five centroids that were highly correlated with those found in previous work. In particular, two of these states were associated with high and low arousal respectively. Individual differences in vulnerability to sleep deprivation were measured by assessing state-related changes in Psychomotor Vigilance Test (PVT) performance. Changes in the duration spent in each of the arousal states from the well-rested to the sleep-deprived condition correlated with declines in PVT performance. The reproducibility of DFC measures and their association with vigilance highlight their utility in serving as a neuroimaging method with behavioural relevance. (178 words). |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.neuroimage.2019.07.004 | NeuroImage |
Keywords | Field | DocType |
Arousal,Dynamic functional connectivity,Individual differences,Reproducibility,Sleep deprivation | Arousal,Low arousal theory,Temporal difference learning,Functional magnetic resonance imaging,Cognitive psychology,Psychology,Vigilance (psychology),Sleep deprivation,Neuroimaging,Audiology,Dynamic functional connectivity | Journal |
Volume | ISSN | Citations |
200 | 1053-8119 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| James Teng | 1 | 0 | 0.34 |
| Ju Lynn Ong | 2 | 31 | 4.45 |
| Amiya Patanaik | 3 | 6 | 1.45 |
| Jesisca Tandi | 4 | 44 | 2.91 |
| Juan Helen Zhou | 5 | 0 | 0.34 |
| Michael W. L. Chee | 6 | 279 | 26.13 |
| Julian Lim | 7 | 40 | 4.86 |