Correction: Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity.

[This corrects the article DOI: 10.1371/journal.pbio.3000602.].

Understanding the Need for Sleep to Improve Cognition.

The restorative function of sleep is shaped by its duration, timing, continuity, subjective quality, and efficiency. Current sleep recommendations specify only nocturnal duration and have been largely derived from sleep self-reports that can be imprecise and miss relevant details. Sleep duration, preferred timing, and ability to withstand sleep deprivation are heritable traits whose expression may change with age and affect the optimal sleep prescription for an individual. Prevailing societal norms and circumstances related to work and relationships interact to influence sleep opportunity and quality. The value of allocating time for sleep is revealed by the impact of its restriction on behavior, functional brain imaging, sleep macrostructure, and late-life cognition. Augmentation of sleep slow oscillations and spindles have been proposed for enhancing sleep quality, but they inconsistently achieve their goal. Crafting bespoke sleep recommendations could benefit from large-scale, longitudinal collection of objective sleep data integrated with behavioral and self-reported data.

Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity.

The brain exhibits substantial diurnal variation in physiology and function, but neuroscience studies rarely report or consider the effects of time of day. Here, we examined variation in resting-state functional MRI (fMRI) in around 900 individuals scanned between 8 AM and 10 PM on two different days. Multiple studies across animals and humans have demonstrated that the brain's global signal (GS) amplitude (henceforth referred to as "fluctuation") increases with decreased arousal. Thus, in accord with known circadian variation in arousal, we hypothesised that GS fluctuation would be lowest in the morning, increase in the midafternoon, and dip in the early evening. Instead, we observed a cumulative decrease in GS fluctuation as the day progressed. Although respiratory variation also decreased with time of day, control analyses suggested that this did not account for the reduction in GS fluctuation. Finally, time of day was associated with marked decreases in resting-state functional connectivity across the whole brain. The magnitude of decrease was significantly stronger than associations between functional connectivity and behaviour (e.g., fluid intelligence). These findings reveal time of day effects on global brain activity that are not easily explained by expected arousal state or physiological artefacts. We conclude by discussing potential mechanisms for the observed diurnal variation in resting brain activity and the importance of accounting for time of day in future studies.

Nocturnal Blood Pressure Estimation from Sleep Plethysmography Using Machine Learning.

BACKGROUND: Elevated nocturnal blood pressure (BP) is a risk factor for cardiovascular disease (CVD) and mortality. Cuffless BP assessment aided by machine learning could be a desirable alternative to traditional cuff-based methods for monitoring BP during sleep. We describe a machine-learning-based algorithm for predicting nocturnal BP using single-channel fingertip plethysmography (PPG) in healthy adults. METHODS: Sixty-eight healthy adults with no apparent sleep or CVD (53% male), with a median (IQR) age of 29 (23-46 years), underwent overnight polysomnography (PSG) with fingertip PPG and ambulatory blood pressure monitoring (ABPM). Features based on pulse morphology were extracted from the PPG waveforms. Random forest models were used to predict night-time systolic blood pressure (SBP) and diastolic blood pressure (DBP). RESULTS: Our model achieved the highest out-of-sample performance with a window length of 7 s across window lengths explored (60 s, 30 s, 15 s, 7 s, and 3 s). The mean absolute error (MAE ± STD) was 5.72 ± 4.51 mmHg for SBP and 4.52 ± 3.60 mmHg for DBP. Similarly, the root mean square error (RMSE ± STD) was 6.47 ± 1.88 mmHg for SBP and 4.62 ± 1.17 mmHg for DBP. The mean correlation coefficient between measured and predicted values was 0.87 for SBP and 0.86 for DBP. Based on Shapley additive explanation (SHAP) values, the most important PPG waveform feature was the stiffness index, a marker that reflects the change in arterial stiffness. CONCLUSION: Our results highlight the potential of machine learning-based nocturnal BP prediction using single-channel fingertip PPG in healthy adults. The accuracy of the predictions demonstrated that our cuffless method was able to capture the dynamic and complex relationship between PPG waveform characteristics and BP during sleep, which may provide a scalable, convenient, economical, and non-invasive means to continuously monitor blood pressure.

Respiratory, cardiac, EEG, BOLD signals and functional connectivity over multiple microsleep episodes.

Falling asleep is common in fMRI studies. By using long eyelid closures to detect microsleep onset, we showed that the onset and termination of short sleep episodes invokes a systematic sequence of BOLD signal changes that are large, widespread, and consistent across different microsleep durations. The signal changes are intimately intertwined with shifts in respiration and heart rate, indicating that autonomic contributions are integral to the brain physiology evaluated using fMRI and cannot be simply treated as nuisance signals. Additionally, resting state functional connectivity (RSFC) was altered in accord with the frequency of falling asleep and in a manner that global signal regression does not eliminate. Our findings point to the need to develop a consensus among neuroscientists using fMRI on how to deal with microsleep intrusions. SIGNIFICANCE STATEMENT: Sleep, breathing and cardiac action are influenced by common brainstem nuclei. We show that falling asleep and awakening are associated with a sequence of BOLD signal changes that are large, widespread and consistent across varied durations of sleep onset and awakening. These signal changes follow closely those associated with deceleration and acceleration of respiration and heart rate, calling into question the separation of the latter signals as 'noise' when the frequency of falling asleep, which is commonplace in RSFC studies, correlates with the extent of RSFC perturbation. Autonomic and central nervous system contributions to BOLD signal have to be jointly considered when interpreting fMRI and RSFC studies.

ENIGMA-Sleep: Challenges, opportunities, and the road map.

Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful collaborative framework for combining datasets across individual sites. Recently, we have launched the ENIGMA-Sleep working group with the collaboration of several institutes from 15 countries to perform large-scale worldwide neuroimaging and genetics studies for better understanding the neurobiology of impaired sleep quality in population-based healthy individuals, the neural consequences of sleep deprivation, pathophysiology of sleep disorders, as well as neural correlates of sleep disturbances across various neuropsychiatric disorders. In this introductory review, we describe the details of our currently available datasets and our ongoing projects in the ENIGMA-Sleep group, and discuss both the potential challenges and opportunities of a collaborative initiative in sleep medicine.

Longitudinal Changes in the Cerebral Cortex Functional Organization of Healthy Elderly.

Healthy aging is accompanied by disruptions in the functional modular organization of the human brain. Cross-sectional studies have shown age-related reductions in the functional segregation and distinctiveness of brain networks. However, less is known about the longitudinal changes in brain functional modular organization and their associations with aging-related cognitive decline. We examined age- and aging-related changes in functional architecture of the cerebral cortex using a dataset comprising a cross-sectional healthy young cohort of 57 individuals (mean ± SD age, 23.71 ± 3.61 years, 22 males) and a longitudinal healthy elderly cohort of 72 individuals (mean ± baseline age, 68.22 ± 5.80 years, 39 males) with 2-3 time points (18-24 months apart) of task-free fMRI data. We found both cross-sectional (elderly vs young) and longitudinal (in elderly) global decreases in network segregation (decreased local efficiency), integration (decreased global efficiency), and module distinctiveness (increased participation coefficient and decreased system segregation). At the modular level, whereas cross-sectional analyses revealed higher participation coefficient across all modules in the elderly compared with young participants, longitudinal analyses revealed focal longitudinal participation coefficient increases in three higher-order cognitive modules: control network, default mode network, and salience/ventral attention network. Cross-sectionally, elderly participants also showed worse attention performance with lower local efficiency and higher mean participation coefficient, and worse global cognitive performance with higher participation coefficient in the dorsal attention/control network. These findings suggest that healthy aging is associated with whole-brain connectome-wide changes in the functional modular organization of the brain, accompanied by loss of functional segregation, particularly in higher-order cognitive networks.SIGNIFICANCE STATEMENT Cross-sectional studies have demonstrated age-related reductions in the functional segregation and distinctiveness of brain networks. However, longitudinal aging-related changes in brain functional modular architecture and their links to cognitive decline remain relatively understudied. Using graph theoretical and community detection approaches to study task-free functional network changes in a cross-sectional young and longitudinal healthy elderly cohort, we showed that aging was associated with global declines in network segregation, integration, and module distinctiveness, and specific declines in distinctiveness of higher-order cognitive networks. Further, such functional network deterioration was associated with poorer cognitive performance cross-sectionally. Our findings suggest that healthy aging is associated with system-level changes in brain functional modular organization, accompanied by functional segregation loss particularly in higher-order networks specialized for cognition.

Vigilance declines following sleep deprivation are associated with two previously identified dynamic connectivity states.

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).

Reward motivation normalises temporal attention after sleep deprivation.

Preparation of attention facilitates speeded responding at time points with a high probability of target occurrence. Conversely, time points with low target probability are disadvantaged due to lower readiness. When targets are uniformly distributed in time, this effect results in higher readiness after longer preparation times (foreperiods). During sleep deprivation, this temporal bias is amplified, resulting in greater performance decrement when stimuli occur at unfavourable times. In this study, we examined whether reward motivation could modulate this increased temporal bias in response speed. Participants (n = 24) performed the psychomotor vigilance task under four reward conditions (0, 1, 5 or 15c per fast response), both after normal sleep (rested wakefulness) and sleep deprivation. To assess temporal preparation (foreperiod-effect), trials were binned based on the lead time prior to target presentation (short foreperiod: 1-6 s; long foreperiod: 6-10 s). As previously observed, the foreperiod-effect (slower reaction time for short foreperiod trials) increased after sleep deprivation. However, this state effect was attenuated with reward, reaching a response speed comparable to that observed in the unrewarded, well-rested condition. The current findings, therefore, suggest that reward improves overall response performance and normalises temporal attention in sleep-deprived individuals.

Trait-like characteristics of sleep EEG power spectra in adolescents across sleep opportunity manipulations.

The electroencephalographic power spectra of non-rapid eye movement sleep in adults demonstrate trait-like consistency within participants across multiple nights, even when prior sleep deprivation is present. Here, we examined the extent to which this finding applies to adolescents who are habitually sleep restricted on school-days and sleep longer on weekends. We evaluated 78 adolescents across three sleep restriction groups who underwent different permutations of adequate sleep (9 hr time-in-bed), sleep restriction (5 hr time-in-bed), afternoon naps (1 hr afternoon) and recovery sleep (9 hr time-in-bed) that simulate behaviour on school-days and weekends. The control group comprised a further 22 adolescents who had 9 hr of sleep opportunity each night. Intra-class correlation coefficients showed moderate to almost perfect within-subject stability in electroencephalographic power spectra across multiple nights in both sleep restriction and control groups, even when changes to sleep macrostructure were observed. While nocturnal intra-class correlation metrics were lower in the low-frequency and spindle frequency bins in the sleep restriction compared with the control group, hierarchical clustering measures could still identify multi-night electroencephalographic spectra as originating from the same individual. The trait-like characteristics of electroencephalographic spectra from an adolescent remain identifiable despite the disruptive effects of multi-night sleep restriction to sleep architecture.

Large-Scale Network Topology Reveals Heterogeneity in Individuals With at Risk Mental State for Psychosis: Findings From the Longitudinal Youth-at-Risk Study.

Emerging evidence demonstrates heterogeneity in clinical outcomes of prodromal psychosis that only a small percentage of at-risk individuals eventually progress to full-blown psychosis. To examine the neurobiological underpinnings of this heterogeneity from a network perspective, we tested whether the early patterns of large-scale brain network topology were associated with risk of developing clinical psychosis. Task-free functional MRI data were acquired from subjects with At Risk Mental State (ARMS) for psychosis and healthy controls (HC). All individuals had no history of drug abuse and were not on antipsychotics. We performed functional connectomics analysis to identify patterns of system-level functional brain dysconnectivity associated with ARMS individuals with different outcomes. In comparison to HC and ARMS who did not transition to psychosis at follow-up (ARMS-NT), ARMS individuals who did (ARMS-T) showed marked brain functional dysconnectivity, characterized by loss of network segregation and disruption of network communities, especially the salience, default, dorsal attention, sensorimotor and limbic networks (P < 0.05 FWE-corrected, Cohen's d > 1.00), and was associated with baseline symptom severity. In contrast, we did not observe connectivity differences between ARMS-NT and HC individuals. Taken together, these results suggest a possible large-scale functional brain network topology phenotype related to risk of psychosis transition in ARMS individuals.

Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states.

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 within-network 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.

Dynamic functional connectivity and its behavioral correlates beyond vigilance.

Fluctuations in resting-state functional connectivity and global signal have been found to correspond with vigilance fluctuations, but their associations with other behavioral measures are unclear. We evaluated 52 healthy adolescents after a week of adequate sleep followed by five nights of sleep restriction to unmask inter-individual differences in cognition and mood. Resting state scans obtained at baseline only, analyzed using sliding window analysis, consistently yielded two polar dynamic functional connectivity states (DCSs) corresponding to previously reported 'low arousal' and 'high arousal' states. We found that the relative temporal preponderance of two dynamic connectivity states (DCS) in well-rested participants, indexed by a median split of participants, based on the relative time spent in these DCS, revealed highly significant group differences in vigilance at baseline and its decline following multiple nights of sleep restriction. Group differences in processing speed and working memory following manipulation but not at baseline suggest utility of DCS in predicting cognitive vulnerabilities unmasked by a stressor like sleep restriction. DCS temporal predominance was uninformative about mood and sleepiness speaking to specificity in its behavioral predictions. Global signal fluctuation provided information confined to vigilance. This appears to be related to head motion, which increases during periods of low arousal.

Memory encoding is impaired after multiple nights of partial sleep restriction.

Sleep is important for normative cognitive functioning. A single night of total sleep deprivation can reduce the capacity to encode new memories. However, it is unclear how sleep restriction during several consecutive nights affects memory encoding. To explore this, we employed a parallel-group design with 59 adolescents randomized into sleep-restricted (SR) and control groups. Both groups were afforded 9 h time in bed (TIB) for 2 baseline nights, followed by 5 consecutive nights of 5 h TIB for the SR group (n = 29) and 9 h TIB for the control group (n = 30). Participants then performed a picture-encoding task. Encoding ability was measured with a recognition test after 3 nights of 9 h TIB recovery sleep for both groups, allowing the assessment of encoding ability without the confounding effects of fatigue at retrieval. Memory was significantly worse in the sleep-restricted group (P = 0.001), and this impairment was not correlated with decline in vigilance. We conclude that memory-encoding deteriorates after several nights of partial sleep restriction, and this typical pattern of sleep negatively affects adolescents' ability to learn declarative information.

Degradation of cortical representations during encoding following sleep deprivation.

A night of total sleep deprivation (TSD) reduces task-related activation of fronto-parietal and higher visual cortical areas. As this reduction in activation corresponds to impaired attention and perceptual processing, it might also be associated with poorer memory encoding. Related animal work has established that cortical columns stochastically enter a 'down' state in sleep deprivation, leading to predictions that neural representations are less stable and distinctive following TSD. To test these predictions participants incidentally encoded scene images while undergoing fMRI, either during rested wakefulness (RW) or after TSD. In scene-selective PPA, TSD reduced stability of neural representations across repetition. This was accompanied by poorer subsequent memory. Greater representational stability benefitted subsequent memory in RW but not TSD. Even for items subsequently recognized, representational distinctiveness was lower in TSD, suggesting that quality of encoding is degraded. Reduced representational stability and distinctiveness are two novel mechanisms by which TSD can contribute to poorer memory formation.

Longitudinal brain structure and cognitive changes over 8 years in an East Asian cohort.

Although East Asia harbors the largest number of aging adults in the world, there is currently little data clarifying the longitudinal brain-cognition relationships in this group. Here, we report structural MRI and neuropsychological findings from relatively healthy Chinese older adults of the Singapore-Longitudinal Aging Brain Study cohort over 8 years of follow up (n=111, mean age=67.1 years, range=56.1-83.1 years at baseline). Aging-related change in structural volume was observed, with total cerebral atrophy at -0.56%/year, hippocampal atrophy at -0.94%/year and ventricular expansion at 3.56%/year. Only speed of processing showed an aging-related decline, while other cognitive domains were relatively maintained. Faster decline in global cognition was associated with total cerebral, hippocampal and gray matter volume losses over time. Faster total cerebral atrophy and white matter atrophy (frontal and parietal regions) was associated with faster decline in verbal memory. Hippocampal atrophy and ventricular expansion were both associated with greater decline in verbal memory and executive function. Our findings provide a benchmark for research on brain structural and cognitive changes with aging in East Asians.

Assessing the benefits of napping and short rest breaks on processing speed in sleep-restricted adolescents.

Achievement-oriented adolescents often study long hours under conditions of chronic sleep restriction, adversely affecting cognitive function. Here, we studied how napping and rest breaks (interleaved off-task periods) might ameliorate the negative effects of sleep restriction on processing speed. Fifty-seven healthy adolescents (26 female, age = 15-19 years) participated in a 15-day live-in protocol. All participants underwent sleep restriction (5 h time-in-bed), but were then randomized into two groups: one of these groups received a daily 1-h nap opportunity. Data from seven of the study days (sleep restriction days 1-5, and recovery days 1-2) are reported here. The Blocked Symbol Decoding Test, administered once a day, was used to assess time-on-task effects and the effects of rest breaks on processing speed. Controlling for baseline differences, participants who took a nap demonstrated faster speed of processing and greater benefit across testing sessions from practice. These participants were also affected significantly less by time-on-task effects. In contrast, participants who did not receive a nap benefited more from the rest breaks that were permitted between blocks of the test. Our results indicate that napping partially reverses the detrimental effects of sleep restriction on processing speed. However, rest breaks have a greater effect as a countermeasure against poor performance when sleep pressure is higher. These data add to the growing body of evidence showing the importance of sleep for good cognitive functioning in adolescents, and suggest that more frequent rest breaks might be important in situations where sleep loss is unavoidable.

Disrupted Sleep: From Molecules to Cognition.

Although the functions of sleep remain to be fully elucidated, it is clear that there are far-reaching effects of its disruption, whether by curtailment for a single night, by a few hours each night over a long period, or by disruption in sleep continuity. Epidemiological and experimental studies of these different forms of sleep disruption show deranged physiology from subcellular levels to complex affective behavior. In keeping with the multifaceted influence of sleep on health and well-being, we illustrate how the duration of sleep, its timing, and continuity can affect cellular ultrastructure, gene expression, metabolic and hormone regulation, mood, and vigilance. Recent brain imaging studies provide some clues on mechanisms underlying the most common cause of disrupted sleep (insomnia). These insights should ultimately result in adequate interventions to prevent and treat sleep disruption because of their high relevance to our most prevalent health problems. SIGNIFICANCE STATEMENT: Disruption of the duration, timing, and continuity of sleep affects cellular ultrastructure, gene expression, appetite regulation, hormone production, vigilance, and reward functions.

Modulating rest-break length induces differential recruitment of automatic and controlled attentional processes upon task reengagement.

Rest breaks are commonly administered as a countermeasure to reduce on-the-job fatigue, both physical and mental. However, this practice makes the assumption that recovery from fatigue, as measured by the reversal of performance declines, is the sole effect of taking a break on behavior. Here, through administering rest breaks of differing lengths in between blocks of a mentally demanding symbol decoding task, we show that this assumption may not be strictly true. First, we replicate previous work by showing that taking a longer break leads to two correlated effects: greater immediate rebound in performance, and greater subsequent time-on-task decline. Using fMRI, we reveal that time-on-task in this paradigm is associated with increasing recruitment of fronto-parietal areas associated with top-down control, and decreasing deactivation in the default-mode network. Finally, by analyzing individual differences, we reveal a potential neural basis for our behavioral observation: greater recovery following long breaks is associated with greater activity in the putamen, an area associated with the automatic generation of motor responses, followed by greater activity in left middle frontal gyrus by the end of those task periods. Taken together, this suggests a shift in the implicit engagement of automatic and controlled attentional processing following longer breaks. This shift may be undesirable or detrimental in real-world situations where maintaining a stable level of attention over time is necessary.

Reduced functional segregation between the default mode network and the executive control network in healthy older adults: A longitudinal study.

The effects of age on functional connectivity (FC) of intrinsic connectivity networks (ICNs) have largely been derived from cross-sectional studies. Far less is known about longitudinal changes in FC and how they relate to ageing-related cognitive decline. We evaluated intra- and inter-network FC in 78 healthy older adults two or three times over a period of 4years. Using linear mixed modeling we found progressive loss of functional specialization with ageing, evidenced by a decline in intra-network FC within the executive control (ECN) and default mode networks (DMN). In contrast, longitudinal inter-network FC between ECN and DMN showed a u-shaped trajectory whereby functional segregation between these two networks initially increased over time and later decreased as participants aged. The rate of loss in functional segregation between ECN and DMN was associated with ageing-related decline in processing speed. The observed longitudinal FC changes and their associations with processing speed remained after correcting for longitudinal reduction in gray matter volume. These findings help connect ageing-related changes in FC with ageing-related decline in cognitive performance and underscore the value of collecting concurrent longitudinal imaging and behavioral data.