Increased sleep pressure reduces resting state functional connectivity

Altered resting-state brain activity in obstructive sleep apnea

STUDY OBJECTIVES

Structural and functional brain changes may contribute to neural dysfunction in patients with obstructive sleep apnea (OSA). However, the effect of OSA on resting-state brain activity has not been established. The objective of this study was to investigate alterations in resting-state functional connectivity (rsFC) of the common brain networks in patients with OSA and their relationships with changes in gray matter volume (GMV) in the corresponding brain regions.

DESIGNS

Resting-state functional and structural MRI data were acquired from patients with OSA and healthy controls. Seven brain networks were identified by independent component analysis. The rsFC in each network was compared between groups and the GMV of brain regions with significant differences in rsFC was also compared.

SETTING

University hospital.

PATIENTS AND PARTICIPANTS

Twenty-four male patients with untreated OSA and 21 matched healthy controls.

INTERVENTIONS

N/A.

MEASUREMENTS AND RESULTS

OSA specifically affected the cognitive and sensorimotor-related brain networks but not the visual and auditory networks. The medial prefrontal cortex and left dorsolateral prefrontal cortex (DLPFC) showed decreased rsFC and GMV in patients with OSA, suggesting structural and functional deficits. The right DLPFC and left precentral gyrus showed decreased rsFC and unchanged GMV, suggesting a functional deficit. The right posterior cingulate cortex demonstrated increased rsFC and unchanged GMV, suggesting functional compensation. In patients with OSA, the rsFC of the right DLPFC was negatively correlated with the apnea-hypopnea index.

CONCLUSIONS

OSA specifically affects resting-state functional connectivity in cognitive and sensorimotor-related brain networks, which may be related to the impaired cognitive and motor functions in these patients.

Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes

As researchers increase their efforts to characterize variations in the functional connectome across studies and individuals, concerns about the many sources of nuisance variation present and their impact on resting state fMRI (R-fMRI) measures continue to grow. Although substantial within-site variation can exist, efforts to aggregate data across multiple sites such as the 1000 Functional Connectomes Project (FCP) and International Neuroimaging Data-sharing Initiative (INDI) datasets amplify these concerns. The present work draws upon standardization approaches commonly used in the microarray gene expression literature, and to a lesser extent recent imaging studies, and compares them with respect to their impact on relationships between common R-fMRI measures and nuisance variables (e.g., imaging site, motion), as well as phenotypic variables of interest (age, sex). Standardization approaches differed with regard to whether they were applied post-hoc vs. during pre-processing, and at the individual vs. group level; additionally they varied in whether they addressed additive effects vs. additive+multiplicative effects, and were parametric vs. non-parametric. While all standardization approaches were effective at reducing undesirable relationships with nuisance variables, post-hoc approaches were generally more effective than global signal regression (GSR). Across approaches, correction for additive effects (global mean) appeared to be more important than for multiplicative effects (global SD) for all R-fMRI measures, with the exception of amplitude of low frequency fluctuations (ALFF). Group-level post-hoc standardizations for mean-centering and variance-standardization were found to be advantageous in their ability to avoid the introduction of artifactual relationships with standardization parameters; though results between individual and group-level post-hoc approaches were highly similar overall. While post-hoc standardization procedures drastically increased test-retest (TRT) reliability for ALFF, modest reductions were observed for other measures after post-hoc standardizations-a phenomena likely attributable to the separation of voxel-wise from global differences among subjects (global mean and SD demonstrated moderate TRT reliability for these measures). Finally, the present work calls into question previous observations of increased anatomical specificity for GSR over mean centering, and draws attention to the near equivalence of global and gray matter signal regression.

Overview of potential procedural and participant-related confounds for neuroimaging of the resting state

Studies of intrinsic brain activity in the resting state have become increasingly common. A productive discussion of what analysis methods are appropriate, of the importance of physiologic correction and of the potential interpretations of results has been ongoing. However, less attention has been paid to factors other than physiologic noise that may confound resting-state experiments. These range from straightforward factors, such as ensuring that participants are all instructed in the same manner, to more obscure participant-related factors, such as body weight. We provide an overview of such potentially confounding factors, along with some suggested approaches for minimizing their impact. A particular theme that emerges from the overview is the range of systematic differences between types of study groups (e.g., between patients and controls) that may influence resting-state study results.

Daytime sleepiness affects prefrontal regulation of food intake

The recent epidemic of obesity corresponds closely with the decline in the average number of hours of sleep obtained nightly. While growing research suggests that sleep loss may affect hormonal and other physiological systems related to food intake, no studies have yet explored the role that sleepiness may play in reducing prefrontal inhibitory control over food intake. Because evidence suggests that women may be more prone to obesity and eating disorders, as well as more likely to suffer from sleep problems, we examined the relation between general daytime sleepiness, brain responses to food stimuli, and self-reported overeating separately for men and women. Thirty-eight healthy adults (16 women; 22 men) aged 18 to 45 underwent functional magnetic resonance imaging (fMRI) while viewing pictures of high- and low-calorie foods. Subjects completed the Epworth Sleepiness Scale (ESS) and provided a rating to the query "how often do you eat more than you intend to." Contrast images comparing brain activation derived from the high- versus low-calorie conditions were correlated voxel-wise with scores from the ESS in a second-level regression model, the output of which was used to predict self-reported overeating. As hypothesized, daytime sleepiness correlated with reduced activation in the ventromedial prefrontal cortex during perception of high- versus low-calorie food images. Moreover, activation within this cluster predicted overeating, but only for women. Findings suggest that normal fluctuations in sleepiness may be sufficient to affect brain regions important for regulating food intake, but that these effects may differ between men and women.

Negative functional connectivity and its dependence on the shortest path length of positive network in the resting-state human brain

It is suggested that structurally segregated and functionally specialized brain regions are mediated by synchrony over large-scale networks in order to provide the formation of dynamic links and integration functions. The existence of negative synchrony, or negative functional connectivity (NFC), however, has been a subject of debate in terms of its origin, interpretation, relationship with structural connectivity, and possible neurophysiological function. The present study, which incorporated 20 cognitively healthy elderly human subjects, focused on testing the hypothesis that NFC significantly correlates with the shortest path length (SPL) in the human brain network. Our theoretical calculation, simulated data, and human study results support this hypothesis. In the human study, we find that (1) the percentage of NFC connections among all connections between brain regions significantly correlates with spatial Euclidian distance; (2) the strength of the NFC between the right amygdala and the left dorsolateral prefrontal cortex is significantly correlated with the SPL across the 20 human subjects; (3) such a significant relationship between the NFC and SPL exists in all the NFC connections in the whole brain; and (4) the correlations between the NFC and SPL also are frequency bandwidth dependent. These results suggest that an accumulated phased delay gives rise to the NFC, along the shortest path in the large-scale brain functional network. It is suggested that our study can be extended to examine a variety of neurological diseases and psychiatric disorders by measuring the changes of SPL and functional reorganization in the brain.

Self-reported nocturnal sleep duration is associated with next-day resting state functional connectivity

Sleep deprivation affects cerebral metabolism and reduces the functional connectivity among various regions of the brain, potentially explaining some of the associated mood and emotional changes often observed. Prior neuroimaging studies have only examined the effects of sleep deprivation or partial sleep restriction on functional connectivity, but none have studied how such connectivity is associated with normal variations in self-reported sleep duration the night before the scan. We examined the relationship between sleep duration and resting state functional connectivity among healthy volunteers who slept at home according to their own schedules. Thirty-nine healthy individuals aged 18-45 (21 females) completed a questionnaire asking about their recent sleep habits and entries in their sleep diary for the previous night, followed by resting state functional MRI at 3 T. Participants reported sleeping between 5.0 and 8.5 h the night before the scan (M=7.0, SD=0.9). Seed regions were placed in the medial prefrontal cortex and posterior cingulate cortex nodes of the default mode network, regions previously implicated in sleep deprivation. Longer self-reported sleep duration was associated with significantly enhanced functional connectivity between the medial prefrontal cortex and posterior cingulate, as well as greater anticorrelations with parietal, occipital, and lateral prefrontal regions. Findings suggest that even normal variations in sleep duration measured by self-report are related to the strength of functional connectivity within select nodes of the default mode network and its anticorrelated network.

Circadian rhythms, sleep deprivation, and human performance

Much of the current science on, and mathematical modeling of, dynamic changes in human performance within and between days is dominated by the two-process model of sleep-wake regulation, which posits a neurobiological drive for sleep that varies homeostatically (increasing as a saturating exponential during wakefulness and decreasing in a like manner during sleep), and a circadian process that neurobiologically modulates both the homeostatic drive for sleep and waking alertness and performance. Endogenous circadian rhythms in neurobehavioral functions, including physiological alertness and cognitive performance, have been demonstrated using special laboratory protocols that reveal the interaction of the biological clock with the sleep homeostatic drive. Individual differences in circadian rhythms and genetic and other components underlying such differences also influence waking neurobehavioral functions. Both acute total sleep deprivation and chronic sleep restriction increase homeostatic sleep drive and degrade waking neurobehavioral functions as reflected in sleepiness, attention, cognitive speed, and memory. Recent evidence indicating a high degree of stability in neurobehavioral responses to sleep loss suggests that these trait-like individual differences are phenotypic and likely involve genetic components, including circadian genes. Recent experiments have revealed both sleep homeostatic and circadian effects on brain metabolism and neural activation. Investigation of the neural and genetic mechanisms underlying the dynamically complex interaction between sleep homeostasis and circadian systems is beginning. A key goal of this work is to identify biomarkers that accurately predict human performance in situations in which the circadian and sleep homeostatic systems are perturbed.

Morning-evening variation in human brain metabolism and memory circuits

It has been posited that a critical function of sleep is synaptic renormalization following a net increase in synaptic strength during wake. We hypothesized that wake would alter the resting-state functional organization of the brain and increase its metabolic cost. To test these hypotheses, two experiments were performed. In one, we obtained morning and evening resting-state functional MRI scans to assess changes in functional brain organization. In the second experiment, we obtained quantitative positron emission tomography measures of glucose and oxygen consumption to assess the cost of wake. We found selective changes in brain organization. Most prominently, bilateral medial temporal regions were locally connected in the morning but in the evening exhibited strong correlations with frontal and parietal brain regions involved in memory retrieval. We speculate that these changes may reflect aspects of memory consolidation recurring on a daily basis. Surprisingly, these changes in brain organization occurred without increases in brain metabolism.

Control of sleep and wakefulness

This review summarizes the brain mechanisms controlling sleep and wakefulness. Wakefulness promoting systems cause low-voltage, fast activity in the electroencephalogram (EEG). Multiple interacting neurotransmitter systems in the brain stem, hypothalamus, and basal forebrain converge onto common effector systems in the thalamus and cortex. Sleep results from the inhibition of wake-promoting systems by homeostatic sleep factors such as adenosine and nitric oxide and GABAergic neurons in the preoptic area of the hypothalamus, resulting in large-amplitude, slow EEG oscillations. Local, activity-dependent factors modulate the amplitude and frequency of cortical slow oscillations. Non-rapid-eye-movement (NREM) sleep results in conservation of brain energy and facilitates memory consolidation through the modulation of synaptic weights. Rapid-eye-movement (REM) sleep results from the interaction of brain stem cholinergic, aminergic, and GABAergic neurons which control the activity of glutamatergic reticular formation neurons leading to REM sleep phenomena such as muscle atonia, REMs, dreaming, and cortical activation. Strong activation of limbic regions during REM sleep suggests a role in regulation of emotion. Genetic studies suggest that brain mechanisms controlling waking and NREM sleep are strongly conserved throughout evolution, underscoring their enormous importance for brain function. Sleep disruption interferes with the normal restorative functions of NREM and REM sleep, resulting in disruptions of breathing and cardiovascular function, changes in emotional reactivity, and cognitive impairments in attention, memory, and decision making.

On the Need of Objective Vigilance Monitoring: Effects of Sleep Loss on Target Detection and Task-Negative Activity Using Combined EEG/fMRI

Sleep loss affects attention by reducing levels of arousal and alertness. The neural mechanisms underlying the compensatory efforts of the brain to maintain attention and performance after sleep deprivation (SD) are not fully understood. Previous neuroimaging studies of SD have not been able to separate the effects of reduced arousal from the effects of SD on cerebral responses to cognitive challenges. Here, we used a simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) approach to study the effects of 36 h of total sleep deprivation (TSD). Specifically, we focused on changes in selective attention processes as induced by an active acoustic oddball task, with the ability to isolate runs with objective EEG signs of high (SD(alert)) or reduced (SD(sleepy)) vigilance. In the SD(alert) condition, oddball task-related activity appears to be sustained by compensatory co-activation of insular regions, but task-negative activity in the right posterior node of the default mode network is altered following TSD. In the SD(sleepy) condition, oddball task-positive activity was massively impaired, but task-negative activation was showing levels comparable with the control condition after a well-rested night. Our results suggest that loss of strict negative correlation between oddball task-positive and task-negative activation reflects the effects of TSD, while the actual state of vigilance during task performance can affects either task-related or task-negative activity, depending on the exact vigilance level.

Functional connectivity hubs in the human brain

Brain networks appear to have few and well localized regions with high functional connectivity density (hubs) for fast integration of neural processing, and their dysfunction could contribute to neuropsychiatric diseases. However the variability in the distribution of these brain hubs is unknown due in part to the overwhelming computational demands associated to their localization. Recently we developed a fast algorithm to map the local functional connectivity density (lFCD). Here we extend our method to map the global density (gFDC) taking advantage of parallel computing. We mapped the gFCD in the brain of 1031 subjects from the 1000 Functional Connectomes project and show that the strongest hubs are located in regions of the default mode network (DMN) and in sensory cortices, whereas subcortical regions exhibited the weakest hubs. The strongest hubs were consistently located in ventral precuneus/cingulate gyrus (previously identified by other analytical methods including lFCD) and in primary visual cortex (BA 17/18), which highlights their centrality to resting connectivity networks. In contrast and after rescaling, hubs in prefrontal regions had lower gFCD than lFCD, which suggests that their local functional connectivity (as opposed to long-range connectivity) prevails in the resting state. The power scaling of the probability distribution of gFCD hubs (as for lFCD) was consistent across research centers further corroborating the "scale-free" topology of brain networks. Within and between-subject variability for gFCD were twice than that for lFCD (20% vs. 12% and 84% vs. 34%, respectively) suggesting that gFCD is more sensitive to individual differences in functional connectivity.

Functional connectivity hubs in the human brain

Brain networks appear to have few and well localized regions with high functional connectivity density (hubs) for fast integration of neural processing, and their dysfunction could contribute to neuropsychiatric diseases. However the variability in the distribution of these brain hubs is unknown due in part to the overwhelming computational demands associated to their localization. Recently we developed a fast algorithm to map the local functional connectivity density (lFCD). Here we extend our method to map the global density (gFDC) taking advantage of parallel computing. We mapped the gFCD in the brain of 1031 subjects from the 1000 Functional Connectomes project and show that the strongest hubs are located in regions of the default mode network (DMN) and in sensory cortices, whereas subcortical regions exhibited the weakest hubs. The strongest hubs were consistently located in ventral precuneus/cingulate gyrus (previously identified by other analytical methods including lFCD) and in primary visual cortex (BA 17/18), which highlights their centrality to resting connectivity networks. In contrast and after rescaling, hubs in prefrontal regions had lower gFCD than lFCD, which suggests that their local functional connectivity (as opposed to long-range connectivity) prevails in the resting state. The power scaling of the probability distribution of gFCD hubs (as for lFCD) was consistent across research centers further corroborating the "scale-free" topology of brain networks. Within and between-subject variability for gFCD were twice than that for lFCD (20% vs. 12% and 84% vs. 34%, respectively) suggesting that gFCD is more sensitive to individual differences in functional connectivity.

Development of the brain's default mode network from wakefulness to slow wave sleep

Falling asleep is paralleled by a loss of conscious awareness and reduced capacity to process external stimuli. Little is known on sleep-associated changes of spontaneously synchronized anatomical networks as detected by resting-state functional magnetic resonance imaging (rs-fMRI). We employed functional connectivity analysis of rs-fMRI series obtained from 25 healthy participants, covering all non-rapid eye movement (NREM) sleep stages. We focused on the default mode network (DMN) and its anticorrelated network (ACN) that are involved in internal and external awareness during wakefulness. Using independent component analysis, cross-correlation analysis (CCA), and intraindividual dynamic network tracking, we found significant changes in DMN/ACN integrity throughout the NREM sleep. With increasing sleep depth, contributions of the posterior cingulate cortex (PCC)/retrosplenial cortex (RspC), parahippocampal gyrus, and medial prefrontal cortex to the DMN decreased. CCA revealed a breakdown of corticocortical functional connectivity, particularly between the posterior and anterior midline node of the DMN and the DMN and the ACN. Dynamic tracking of the DMN from wakefulness into slow wave sleep in a single subject added insights into intraindividual network fluctuations. Results resonate with a role of the PCC/RspC for the regulation of consciousness. We further submit that preserved corticocortical synchronization could represent a prerequisite for maintaining internal and external awareness.