Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep.

By Enzo Tagliazucchi, Helmut Laufs. Neuron, 2014

637 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/43a93ce3c61594b0cb4eb55eecb9b52b7f5f2a41

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In Cognitive Neuroscience

Citations in the corpus

Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, positive): “…a wide range of cognitive and behavioral states ranging from explicitly cued task execution (e.g., Gonzalez-Castillo & Bandettini, 2018) to wakeful rest (e.g., Allen et al., 2014), sleep (e.g., Tagliazucchi & Laufs, 2014), and anesthesia (e.g., Hutchison, Womelsdorf, Gati, Everling, & Menon, 2013).”
Inscapes: a movie paradigm to improve compliance in functional magnetic resonance imaging (2015, background): “A second drawback to the resting state approach is the tendency of many participants to fall asleep (Tagliazucchi and Laufs, 2014).”
Sources and implications of whole-brain fMRI signals in humans (2016, neutral): “5 of (Tagliazucchi and Laufs, 2014)).”
Precision functional mapping of individual human brains (2017, positive): “Thus low reliability likely is attributable to high levels of drowsiness (Laumann et al., 2016; Tagliazucchi and Laufs, 2014).”
Can brain state be manipulated to emphasize individual differences in functional connectivity? (2017, background): “6 they are (Chang et al., 2016; Tagliazucchi and Laufs, 2014), the content of their spontaneous”
Building a Science of Individual Differences from fMRI. (2016, background): “Of course, careful investigations have pointed out ‘details’ that influence the data collected during the resting-state, such as whether the subject's eyes are open or closed [161], whether they are completely awake [124], what they are actually thinking about during the run [162–164], or what task ”
Influences on the Test–Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility (2017, background): “Parsing the contribution of real, brain-derived dynamics underlying variability at shorter timescales remains a challenge (Hutchison et al. 2013; Tagliazucchi and Laufs 2014; Hindriks et al. 2016; Laumann et al. 2016).”
Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation (2018, background): “, 2016; Menon, 2011; Sheffield and Barch, 2016; Stam, 2014) aremost likely underpinned by differences in a stable network architecture, although comparisons are best made if studies also avoid contamination from statebased differences such as might occur with different levels of sleep between groups”
Ten simple rules for predictive modeling of individual differences in neuroimaging (2019, background): “Additionally, state-of-the-art preprocessing strategies can remove many confound and nuisance variables that cannot be accounted for in data collection, such as physiological and head motion artifacts (Murphy et al., 2013; Power et al., 2012; Siegel et al., 2017; Tagliazucchi and Laufs, 2014).”
Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders (2014, methodology): “The networks underlying anhedonia, the lack of pleasure, are compromised in the diseased brain (Treadway and Zald, 2011) and, specifically, disruptions have been demonstrated to the predictive coding underlying reinforcement learning (Stephan et al., 2006).”
Functional system and areal organization of a highly sampled individual human brain (2015, background): “This residual variability most likely reﬂects variable arousal across sessions, as Tagliazucchi and Laufs (2014) have recently reported increased BOLD variance in somato-motor and visual regions during light sleep relative to waking.”