Fluctuations in resting-state functional connectivity occur but their behavioral significance remains unclear, largely because correlating behavioral state with dynamic functional connectivity states (DCS) engages probes that disrupt the very behavioral state we seek to observe. Observing spontaneous eyelid closures following sleep deprivation permits nonintrusive arousal monitoring. During periods of low arousal dominated by eyelid closures, sliding-window correlation analysis uncovered a DCS associated with reduced withinnetwork functional connectivity of default mode and dorsal/ventral attention networks, as well as reduced anticorrelation between these networks. Conversely, during periods when participants' eyelids were wide open, a second DCS was associated with less decoupling between the visual network and higher-order cognitive networks that included dorsal/ventral attention and default mode networks. In subcortical structures, eyelid closures were associated with increased connectivity between the striatum and thalamus with the ventral attention network, and greater anticorrelation with the dorsal attention network. When applied to task-based fMRI data, these two DCS predicted interindividual differences in frequency of behavioral lapsing and intraindividual temporal fluctuations in response speed. These findings with participants who underwent a night of total sleep deprivation were replicated in an independent dataset involving partially sleep-deprived participants. Fluctuations in functional connectivity thus appear to be clearly associated with changes in arousal. T he existence of large-scale functional brain networks is evidenced by well-defined spatial patterns of correlated bloodoxygenation level-dependent (BOLD) signal fluctuation in fMRI data (1). Recent work has shown that functional connectivity (FC) within and between brain networks is dynamic, corresponding to the observation that even while we are performing a task, our mental focus fluctuates (2). Fluctuation of fMRI-based FC occurs over tens of seconds (3, 4) and exhibits different patterns across conscious and unconscious states (5, 6). Furthermore, just as interindividual differences in stationary FC relate to variation in human behavior and cognition (7-10), it seems likely that recurring patterns (11) of fluctuating FC have behavioral significance.Temporal fluctuations in FC can arise from conscious mental activity (12), episodes of random synchrony (3), or simply timevarying levels of physiological noise (13,14). The association between BOLD signal fluctuation in the default mode network (DMN) and mind-wandering episodes (15-17) has prompted investigations into the behavioral correlates of spontaneous resting-state FC fluctuations (11,18). Although these fluctuations in FC have been shown to correlate with several physiological markers, such as electroencephalogram (EEG) power, magnetoencephalography (MEG) power, and heart rate variability (19-21), their behavioral significance remains unclear.A key obstacle to elucidating clear FC...

Sleep staging is a fundamental but time consuming process in any sleep laboratory. To greatly speed up sleep staging without compromising accuracy, we developed a novel framework for performing real-time automatic sleep stage classification. The client–server architecture adopted here provides an end-to-end solution for anonymizing and efficiently transporting polysomnography data from the client to the server and for receiving sleep stages in an interoperable fashion. The framework intelligently partitions the sleep staging task between the client and server in a way that multiple low-end clients can work with one server, and can be deployed both locally as well as over the cloud. The framework was tested on four datasets comprising ≈1700 polysomnography records (≈12000 hr of recordings) collected from adolescents, young, and old adults, involving healthy persons as well as those with medical conditions. We used two independent validation datasets: one comprising patients from a sleep disorders clinic and the other incorporating patients with Parkinson’s disease. Using this system, an entire night’s sleep was staged with an accuracy on par with expert human scorers but much faster (≈5 s compared with 30–60 min). To illustrate the utility of such real-time sleep staging, we used it to facilitate the automatic delivery of acoustic stimuli at targeted phase of slow-sleep oscillations to enhance slow-wave sleep.

Study ObjectivesSlow oscillations (SO) during sleep contribute to the consolidation of learned material. How the encoding of declarative memories during subsequent wakefulness might benefit from their enhancement during sleep is less clear. In this study, we investigated the impact of acoustically enhanced SO during a nap on subsequent encoding of declarative material.MethodsThirty-seven healthy young adults were studied under two conditions: stimulation (STIM) and no stimulation (SHAM), in counter-balanced order following a night of sleep restriction (4 hr time-in-bed [TIB]). In the STIM condition, auditory tones were phase-locked to the SO up-state during a 90 min nap opportunity. In the SHAM condition, corresponding time points were marked but tones were not presented. Thirty minutes after awakening, participants encoded pictures while undergoing fMRI. Picture recognition was tested 60 min later.ResultsAcoustic stimulation augmented SO across the group, but there was no group level benefit on memory. However, the magnitude of SO enhancement correlated with greater recollection. SO enhancement was also positively correlated with hippocampal activation at encoding. Although spindle activity increased, this did not correlate with memory benefit or shift in hippocampal signal.ConclusionsAcoustic stimulation during a nap can benefit encoding of declarative memories. Hippocampal activation positively correlated with SO augmentation.

While mindfulness is commonly viewed as a skill to be cultivated through practice, untrained individuals can also vary widely in dispositional mindfulness. Prior research has identified static neural connectivity correlates of this trait. Here, we use dynamic functional connectivity (DFC) analysis of resting-state fMRI to study time-varying connectivity patterns associated with naturally varying and objectively measured trait mindfulness. Participants were selected from the top and bottom tertiles of performers on a breath-counting task to form high trait mindfulness (HTM; N = 21) and low trait mindfulness (LTM; N = 18) groups. DFC analysis of resting state fMRI data revealed that the HTM group spent significantly more time in a brain state associated with task-readiness - a state characterized by high within-network connectivity and greater anti-correlations between task-positive networks and the default-mode network (DMN). The HTM group transitioned between brain states more frequently, but the dwell time in each episode of the task-ready state was equivalent between groups. These results persisted even after controlling for vigilance. Across individuals, certain connectivity metrics were weakly correlated with self-reported mindfulness as measured by the Five Facet Mindfulness Questionnaire, though these did not survive multiple comparisons correction. In the static connectivity maps, HTM individuals had greater within-network connectivity in the DMN and the salience network, and greater anti-correlations between the DMN and task-positive networks. In sum, DFC features robustly distinguish HTM and LTM individuals, and may be useful biological markers for the measurement of dispositional mindfulness.