Cerebrovascular activity is a major factor in the cerebrospinal fluid flow dynamics.

Cerebrospinal fluid (CSF) provides physical protection to the central nervous system as well as an essential homeostatic environment for the normal functioning of neurons. Additionally, it has been proposed that the pulsatile movement of CSF may assist in glymphatic clearance of brain metabolic waste products implicated in neurodegeneration. In awake humans, CSF flow dynamics are thought to be driven primarily by cerebral blood volume fluctuations resulting from a number of mechanisms, including a passive vascular response to blood pressure variations associated with cardiac and respiratory cycles. Recent research has shown that mechanisms that rely on the action of vascular smooth muscle cells ("cerebrovascular activity") such as neuronal activity, changes in intravascular CO2, and autonomic activation from the brainstem, may lead to CSF pulsations as well. Nevertheless, the relative contribution of these mechanisms to CSF flow remains unclear. To investigate this further, we developed an MRI approach capable of disentangling and quantifying CSF flow components of different time scales associated with these mechanisms. This approach was evaluated on human control subjects (n = 12) performing intermittent voluntary deep inspirations, by determining peak flow velocities and displaced volumes between these mechanisms in the fourth ventricle. We found that peak flow velocities were similar between the different mechanisms, while displaced volumes per cycle were about a magnitude larger for deep inspirations. CSF flow velocity peaked at around 10.4 s (range 7.1-14.8 s, n = 12) following deep inspiration, consistent with known cerebrovascular activation delays for this autonomic challenge. These findings point to an important role of cerebrovascular activity in the genesis of CSF pulsations. Other regulatory triggers for cerebral blood flow such as autonomic arousal and orthostatic challenges may create major CSF pulsatile movement as well. Future quantitative comparison of these and possibly additional types of CSF pulsations with the proposed approach may help clarify the conditions that affect CSF flow dynamics.

Autonomic arousals contribute to brain fluid pulsations during sleep.

During sleep, slow waves of neuro-electrical activity engulf the human brain and aid in the consolidation of memories. Recent research suggests that these slow waves may also promote brain health by facilitating the removal of metabolic waste, possibly by orchestrating the pulsatile flow of cerebrospinal fluid (CSF) through local neural control over vascular tone. To investigate the role of slow waves in the generation of CSF pulsations, we analyzed functional MRI data obtained across the full sleep-wake cycle and during a waking respiratory task. This revealed a novel generating mechanism that relies on the autonomic regulation of cerebral vascular tone without requiring slow electrocortical activity or even sleep. Therefore, the role of CSF pulsations in brain waste clearance may, in part, depend on proper autoregulatory control of cerebral blood flow.

Tracking brain arousal fluctuations with fMRI.

Changes in brain activity accompanying shifts in vigilance and arousal can interfere with the study of other intrinsic and task-evoked characteristics of brain function. However, the difficulty of tracking and modeling the arousal state during functional MRI (fMRI) typically precludes the assessment of arousal-dependent influences on fMRI signals. Here we combine fMRI, electrophysiology, and the monitoring of eyelid behavior to demonstrate an approach for tracking continuous variations in arousal level from fMRI data. We first characterize the spatial distribution of fMRI signal fluctuations that track a measure of behavioral arousal; taking this pattern as a template, and using the local field potential as a simultaneous and independent measure of cortical activity, we observe that the time-varying expression level of this template in fMRI data provides a close approximation of electrophysiological arousal. We discuss the potential benefit of these findings for increasing the sensitivity of fMRI as a cognitive and clinical biomarker.

Contribution of systemic vascular effects to fMRI activity in white matter.

To investigate a potential contribution of systemic physiology to recently reported BOLD fMRI signals in white matter, we compared photo-plethysmography (PPG) and whole-brain fMRI signals recorded simultaneously during long resting-state scans from an overnight sleep study. We found that intermittent drops in the amplitude of the PPG signal exhibited strong and widespread correlations with the fMRI signal, both in white matter (WM) and in gray matter (GM). The WM signal pattern resembled that seen in previous resting-state fMRI studies and closely tracked the location of medullary veins. Its temporal cross-correlation with the PPG amplitude was bipolar, with an early negative value. In GM, the correlation was consistently positive. Consistent with previous studies comparing physiological signals with fMRI, these findings point to a systemic vascular contribution to WM fMRI signals. The PPG drops are interpreted as systemic vasoconstrictive events, possibly related to intermittent increases in sympathetic tone related to fluctuations in arousal state. The counter-intuitive polarity of the WM signal is explained by long blood transit times in the medullary vasculature of WM, which cause blood oxygenation loss and a substantial timing mismatch between blood volume and blood oxygenation effects. A similar mechanism may explain previous findings of negative WM signals around large draining veins during both task- and resting-state fMRI.

Rhythmic alternating patterns of brain activity distinguish rapid eye movement sleep from other states of consciousness.

Rapid eye movement (REM) sleep constitutes a distinct "third state" of consciousness, during which levels of brain activity are commensurate with wakefulness, but conscious awareness is radically transformed. To characterize the temporal and spatial features of this paradoxical state, we examined functional interactions between brain regions using fMRI resting-state connectivity methods. Supporting the view that the functional integrity of the default mode network (DMN) reflects "level of consciousness," we observed functional uncoupling of the DMN during deep sleep and recoupling during REM sleep (similar to wakefulness). However, unlike either deep sleep or wakefulness, REM was characterized by a more widespread, temporally dynamic interaction between two major brain systems: unimodal sensorimotor areas and the higher-order association cortices (including the DMN), which normally regulate their activity. During REM, these two systems become anticorrelated and fluctuate rhythmically, in reciprocally alternating multisecond epochs with a frequency ranging from 0.1 to 0.01 Hz. This unique spatiotemporal pattern suggests a model for REM sleep that may be consistent with its role in dream formation and memory consolidation.

A PILOT STUDY ON THE ENCODING OF A PERCEPTUAL LEARNING TASK FOLLOWING SLEEP DEPRIVATION.

Memory encoding sometimes must occur during a period of sleep deprivation. The question was whether one night of sleep deprivation inhibits encoding on a perceptual learning task (the texture discrimination task). The sample was 18 human participants (M age=22.1 yr., SEM=0.5; 8 men). The participants were randomized to a sleep deprivation or sleep control condition and, after the manipulation, were given two administrations of the texture discrimination task. All participants were given an opportunity for a 90 min. nap between the two administrations. Performance was measured by the interpolated stimulus-to-mask-onset asynchrony (i.e., the inter-stimulus interval), at which the percentage of correct responses for the stimuli in the participant's peripheral vision fell below 80%. Offline consolidation was defined as a decrease in this index between the two administrations. Participants who were sleep deprived prior to encoding exhibited similar offline consolidation (M=-5.3 msec., SEM=2.3) compared to participants who were not sleep deprived prior to encoding (M=-6.2 msec., SEM=3.9); the two-way interaction between time and condition was not significant. In light of reports in the literature, these results indicate encoding following sleep deprivation may be influenced by both the type of task encoded and the brain regions involved in memory processing.

Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI.

Understanding the function of sleep requires studying the dynamics of brain activity across whole-night sleep and their transitions. However, current gold standard polysomnography (PSG) has limited spatial resolution to track brain activity. Additionally, previous fMRI studies were too short to capture full sleep stages and their cycling. To study whole-brain dynamics and transitions across whole-night sleep, we used an unsupervised learning approach, the Hidden Markov model (HMM), on two-night, 16-hour fMRI recordings of 12 non-sleep-deprived participants who reached all PSG-based sleep stages. This method identified 21 recurring brain states and their transition probabilities, beyond PSG-defined sleep stages. The HMM trained on one night accurately predicted the other, demonstrating unprecedented reproducibility. We also found functionally relevant subdivisions within rapid eye movement (REM) and within non-REM 2 stages. This study provides new insights into brain dynamics and transitions during sleep, aiding our understanding of sleep disorders that impact sleep transitions.

Sleep and the functional connectome.

Sleep and the functional connectome are research areas with considerable overlap. Neuroimaging studies of sleep based on EEG-PET and EEG-fMRI are revealing the brain networks that support sleep, as well as networks that may support the roles and processes attributed to sleep. For example, phenomena such as arousal and consciousness are substantially modulated during sleep, and one would expect this modulation to be reflected in altered network activity. In addition, recent work suggests that sleep also has a number of adaptive functions that support waking activity. Thus the study of sleep may elucidate the circuits and processes that support waking function and complement information obtained from fMRI during waking conditions. In this review, we will discuss examples of this for memory, arousal, and consciousness after providing a brief background on sleep and on studying it with fMRI.

Caffeine gum minimizes sleep inertia.

Naps are an effective strategy for maintaining alertness and cognitive performance; however, upon abrupt wakening from naps, sleep inertia (temporary performance degradation) may ensue. In the present study, attenuation of post-nap sleep inertia was attempted by administration of caffeine gum. Using a double-blind, placebo-controlled crossover design, 15 healthy, non-smoking adults were awakened at 1 hr. and again at 6 hr. after lights out (0100 and 0600, respectively) and were immediately administered a gum pellet containing 100 mg of caffeine or placebo. A 5-min. psychomotor vigilance task was administered at 0 min., 6 min., 12 min., and 18 min. post-awakening. At 0100, response speed with caffeine was significantly better at 12 min. and 18 min. post-awakening compared to placebo; at 0600, caffeine's effects were evident at 18 min. post-awakening. Caffeinated gum is a viable means of rapidly attenuating sleep inertia, suggesting that the adenosine receptor system is involved in sleep maintenance.

The effect of sleep-corrected social jetlag on crystalized intelligence, school performance, and functional connectome in early adolescence.

Approximately half of adolescents encounter a mismatch between their sleep patterns on school days and free days, also referred to as "social jetlag". This condition has been linked to various adverse outcomes, such as poor sleep, cognitive deficits, and mental disorders. However, prior research was unsuccessful in accounting for other variables that are correlated with social jetlag, including sleep duration and quality. To address this limitation, we applied a propensity score matching method on a sample of 8853 11-12-year-olds from the two-year follow-up (FL2) data of the Adolescent Brain Cognitive Development (ABCD) study. We identified 3366 pairs of participants with high sleep-corrected social jetlag (SJLsc, over 1 hour) and low SJLsc (<= 1 hour) at FL2, as well as 1277 pairs at three-year follow-up (FL3), after matching based on 11 covariates including socioeconomic status, demographics, and sleep duration and quality. Our results showed that high SJLsc, as measured by the Munich Chronotype Questionnaire, was linked to reduced crystallized intelligence, lower school performance - grades, and decreased functional connectivity between cortical networks and subcortical regions, specifically between cingulo-opercular network and right hippocampus (cerc-hprh). Further mediation and longitudinal mediation analyses revealed that cerc-hprh connection mediated the associations between SJLsc and crystallized intelligence at FL2, and between SJLsc and grades at both FL2 and FL3. We validated these findings by replicating these results using objective SJLsc measurements obtained via Fitbit watches. Overall, our study highlights the negative association between social jetlag and crystallized intelligence during early adolescence.

Effects of sleep-corrected social jetlag on measures of mental health, cognitive ability, and brain functional connectivity in early adolescence.

Approximately half of adolescents encounter a mismatch between their sleep patterns on school days and free days, also referred to as "social jetlag." This condition has been linked to various adverse outcomes, such as poor sleep, cognitive deficits, and mental disorders. However, prior research was unsuccessful in accounting for other variables that are correlated with social jetlag, including sleep duration and quality. To address this limitation, we applied a propensity score matching method on a sample of 6335 11-12-year-olds from the 2-year follow-up (FL2) data of the Adolescent Brain Cognitive Development study. We identified 2424 pairs of participants with high sleep-corrected social jetlag (SJLsc, over 1 hour) and low SJLsc (<= 1 hour) at FL2 (1728 pairs have neuroimaging data), as well as 1626 pairs at 3-year follow-up (FL3), after matching based on 11 covariates including socioeconomic status, demographics, and sleep duration and quality. Our results showed that high SJLsc, as measured by the Munich Chronotype Questionnaire, was linked to reduced crystallized intelligence (CI), lower school performance-grades, and decreased functional connectivity between cortical networks and subcortical regions, specifically between cingulo-opercular network and right hippocampus. Further mediation and longitudinal mediation analyses revealed that this connection mediated the associations between SJLsc and CI at FL2, and between SJLsc and grades at both FL2 and FL3. We validated these findings by replicating these results using objective SJLsc measurements obtained via Fitbit watches. Overall, our study highlights the negative association between social jetlag and CI during early adolescence.

Increased rates of brain protein synthesis during [N1,N2] sleep: L-[1-11C]leucine PET studies in human subjects.

During sleep, reduced brain energy demands provide an opportunity for biosynthetic processes like protein synthesis. Sleep is required for some forms of memory consolidation which requires de novo protein synthesis. We measured regional cerebral protein synthesis rates (rCPS) in human subjects to ascertain how rCPS is affected during sleep. Subjects underwent three consecutive L-[1-11C]leucine PET scans with simultaneous polysomnography: 1. rested awake, 2. sleep-deprived awake, 3. sleep. Measured rCPS were similar across the three conditions. Variations in sleep stage times during sleep scans were used to estimate rCPS in sleep stages under the assumption that measured rCPS is the weighted sum of rCPS in each stage, with weights reflecting time and availability of [11C]leucine in that stage. During sleep scans, subjects spent most of the time in N2, N3, and awake and very little time in N1 and REM; rCPS in N1 and REM could not be reliably estimated. When stages N1 and N2 were combined [N1,N2], estimates of rCPS were more robust. In selective regions, estimated rCPS were statistically significantly higher (30-39%) in [N1,N2] compared with N3; estimated rCPS in N3 were similar to values measured in sleep-deprived awake scans. Results indicate increased rates of protein synthesis linked to [N1,N2] sleep.

Decoupling of the brain's default mode network during deep sleep.

The recent discovery of a circuit of brain regions that is highly active in the absence of overt behavior has led to a quest for revealing the possible function of this so-called default-mode network (DMN). A very recent study, finding similarities in awake humans and anesthetized primates, has suggested that DMN activity might not simply reflect ongoing conscious mentation but rather a more general form of network dynamics typical of complex systems. Here, by performing functional MRI in humans, it is shown that a natural, sleep-induced reduction of consciousness is reflected in altered correlation between DMN network components, most notably a reduced involvement of frontal cortex. This suggests that DMN may play an important role in the sustenance of conscious awareness.

Insomnia as predictor versus outcome of PTSD and depression among Iraq combat veterans.

OBJECTIVES: The study conducted a longitudinal assessment of insomnia as an antecedent versus consequence of posttraumatic stress disorder (PTSD) and depression symptoms among combat veterans. DESIGN: Two postdeployment time points were used in combination with structural equation modeling to examine the relative strength of two possible directions of prediction: insomnia as a predictor of psychological symptoms, and psychological symptoms as a predictor of insomnia. Participants were active duty soldiers (N = 659) in a brigade combat team who were assessed 4 months after their return from a 12-month deployment to Iraq, and then again eight months later. RESULTS: Although both insomnia and psychological symptoms were associated at both time periods and across time periods, insomnia at 4 months postdeployment was a significant predictor of change in depression and PTSD symptoms at 12 months postdeployment, whereas depression and PTSD symptoms at 4 months postdeployment were not significant predictors of change in insomnia at 12 months postdeployment. CONCLUSIONS: Results support the role of insomnia in the development of additional psychological problems and highlight the clinical implications for combat veterans, to include the importance of longitudinal assessment and monitoring of sleep disturbances, and the need for early intervention.

fMRI-based detection of alertness predicts behavioral response variability.

Levels of alertness are closely linked with human behavior and cognition. However, while functional magnetic resonance imaging (fMRI) allows for investigating whole-brain dynamics during behavior and task engagement, concurrent measures of alertness (such as EEG or pupillometry) are often unavailable. Here, we extract a continuous, time-resolved marker of alertness from fMRI data alone. We demonstrate that this fMRI alertness marker, calculated in a short pre-stimulus interval, captures trial-to-trial behavioral responses to incoming sensory stimuli. In addition, we find that the prediction of both EEG and behavioral responses during the task may be accomplished using only a small fraction of fMRI voxels. Furthermore, we observe that accounting for alertness appears to increase the statistical detection of task-activated brain areas. These findings have broad implications for augmenting a large body of existing datasets with information about ongoing arousal states, enriching fMRI studies of neural variability in health and disease.

Single-trial fMRI shows contralesional activity linked to overt naming errors in chronic aphasic patients.

We used fMRI to investigate the roles played by perilesional and contralesional cortical regions during language production in stroke patients with chronic aphasia. We applied comprehensive psycholinguistic analyses based on well-established models of lexical access to overt picture-naming responses, which were evaluated using a single trial design that permitted distinction between correct and incorrect responses on a trial-by-trial basis. Although both correct and incorrect naming responses were associated with left-sided perilesional activation, incorrect responses were selectively associated with robust right-sided contralesional activity. Most notably, incorrect responses elicited overactivation in the right inferior frontal gyrus that was not observed in the contrasts for patients' correct responses or for responses of age-matched control subjects. Errors were produced at slightly later onsets than accurate responses and comprised predominantly semantic paraphasias and omissions. Both types of errors were induced by pictures with greater numbers of alternative names, and omissions were also induced by pictures with late acquired names. These two factors, number of alternative names per picture and age of acquisition, were positively correlated with activation in left and right inferior frontal gyri in patients as well as control subjects. These results support the hypothesis that some right frontal activation may normally be associated with increasing naming difficulty, but in patients with aphasia, right frontal overactivation may reflect ineffective effort when left hemisphere perilesional resources are insufficient. They also suggest that contralesional areas continue to play a role--dysfunctional rather than compensatory--in chronic aphasic patients who have experienced a significant degree of recovery.

Physiological changes in sleep that affect fMRI inference.

fMRI relies on a localized cerebral blood flow (CBF) response to changes in cortical neuronal activity. An underappreciated aspect however is its sensitivity to contributions from autonomic physiology that may affect CBF through changes in vascular resistance and blood pressure. As is reviewed here, this is crucial to consider in fMRI studies of sleep, given the close linkage between the regulation of arousal state and autonomic physiology. Typical methods for separating these effects are based on the use of reference signals that may include physiological parameters such as heart rate and respiration; however, the use of time-invariant models may not be adequate due to the possibly changing relationship between reference and fMRI signals with arousal state. In addition, recent research indicates that additional physiological reference signals may be needed to accurately describe changes in systemic physiology, including sympathetic indicators such as finger skin vascular tone and blood pressure.

Positron emission tomography correlates of visually-scored electroencephalographic waveforms during non-Rapid Eye Movement sleep.

Visually-scored, non-Rapid Eye Movement (REM) sleep electroencephalographic (EEG) waveform activity for each 30-s sleep scored epoch-including the number of sleep spindles, the number of K-complexes, and the percentage of delta waves occupying the epoch-was correlated with H(2)(15)O positron emission tomography. Sleep spindle correlations included positive correlations in the thalamus and right hippocampus. K-complex correlations included positive correlations in the frontomedian prefrontal cortex and cerebellum. Delta wave correlations included negative correlations in the thalamus, frontomedian prefrontal cortex, dorsal pons, and primary visual cortex. Each pattern of correlations may suggest a functional significance for these waveforms that relates to a waking outcome.

Sympathetic activity contributes to the fMRI signal.

The interpretation of functional magnetic resonance imaging (fMRI) studies of brain activity is often hampered by the presence of brain-wide signal variations that may arise from a variety of neuronal and non-neuronal sources. Recent work suggests a contribution from the sympathetic vascular innervation, which may affect the fMRI signal through its putative and poorly understood role in cerebral blood flow (CBF) regulation. By analyzing fMRI and (electro-) physiological signals concurrently acquired during sleep, we found that widespread fMRI signal changes often co-occur with electroencephalography (EEG) K-complexes, signatures of sub-cortical arousal, and episodic drops in finger skin vascular tone; phenomena that have been associated with intermittent sympathetic activity. These findings support the notion that the extrinsic sympathetic innervation of the cerebral vasculature contributes to CBF regulation and the fMRI signal. Accounting for this mechanism could help separate systemic from local signal contributions and improve interpretation of fMRI studies.

All-night functional magnetic resonance imaging sleep studies.

BACKGROUND: Previous functional magnetic resonance imaging (fMRI) sleep studies have been hampered by the difficulty of obtaining extended amounts of sleep in the sleep-adverse environment of the scanner and often have resorted to manipulations such as sleep depriving subjects before scanning. These manipulations limit the generalizability of the results. NEW METHOD: The current study is a methodological validation of procedures aimed at obtaining all-night fMRI data in sleeping subjects with minimal exposure to experimentally induced sleep deprivation. Specifically, subjects slept in the scanner on two consecutive nights, allowing the first night to serve as an adaptation night. RESULTS/COMPARISON WITH EXISTING METHOD(S): Sleep scoring results from simultaneously acquired electroencephalography data on Night 2 indicate that subjects (n = 12) reached the full spectrum of sleep stages including slow-wave (M = 52.1 min, SD = 26.5 min) and rapid eye movement (REM, M = 45.2 min, SD = 27.9 min) sleep and exhibited a mean of 2.1 (SD = 1.1) nonREM-REM sleep cycles. CONCLUSIONS: It was found that by diligently applying fundamental principles and methodologies of sleep and neuroimaging science, performing all-night fMRI sleep studies is feasible. However, because the two nights of the study were performed consecutively, some sleep deprivation from Night 1 as a cause of the Night 2 results is likely, so consideration should be given to replicating the current study with a washout period. It is envisioned that other laboratories can adopt the core features of this protocol to obtain similar results.