Sleep and the functional connectome

Sleep defined by arousal threshold reveals decreases in corticocortical functional correlations independently from the conventional sleep stages

Sleep research and sleep medicine have benefited from the use of polysomnography but have also suffered from an overreliance on the conventional, polysomnography-defined sleep stages. For example, reports of sleep-specific brain activity patterns have, with few exceptions, been constrained by assessing brain function as it relates to the conventional sleep stages. This limits the variety of sleep states and underlying activity patterns that one can discover. If undiscovered brain activity patterns exist during sleep, then removing the constraint of a stage-specific analysis may uncover them. The current study used all-night functional magnetic resonance imaging sleep data and defined sleep behaviorally with auditory arousal threshold (AAT) to begin to search for new brain states. It was hypothesized that, during sleep compared to wakefulness, corticocortical functional correlations would decrease. Functional correlation values calculated in a window immediately before the determination of AAT were entered into a linear mixed effects model, allowing multiple arousals across the night per subject into the analysis. The hypothesis was supported using both correlation matrices of brain networks and single seed-region analyses showing whole-brain maps. This represents a novel approach to studying the neuroanatomical correlates of sleep with high spatial resolution by defining sleep in a way that was independent from the conventional sleep stages. This work provides initial evidence to justify searching for sleep stages that are more neuroanatomically localized and unrelated to the conventional sleep stages.

Functional connectivity changes in meditators and novices during yoga nidra practice

Yoga nidra (YN) practice aims to induce a deeply relaxed state akin to sleep while maintaining heightened awareness. Despite the growing interest in its clinical applications, a comprehensive understanding of the underlying neural correlates of the practice of YN remains largely unexplored. In this fMRI investigation, we aim to discover the differences between wakeful resting states and states attained during YN practice. The study included individuals experienced in meditation and/or yogic practices, referred to as 'meditators' (n = 30), and novice controls (n = 31). The GLM analysis, based on audio instructions, demonstrated activation related to auditory cues without concurrent default mode network (DMN) deactivation. DMN seed based functional connectivity (FC) analysis revealed significant reductions in connectivity among meditators during YN as compared to controls. We did not find differences between the two groups during the pre and post resting state scans. Moreover, when DMN-FC was compared between the YN state and resting state, meditators showed distinct decoupling, whereas controls showed increased DMN-FC. Finally, participants exhibit a remarkable correlation between reduced DMN connectivity during YN and self-reported hours of cumulative meditation and yoga practice. Together, these results suggest a unique neural modulation of the DMN in meditators during YN which results in being restful yet aware, aligned with their subjective experience of the practice. The study deepens our understanding of the neural mechanisms of YN, revealing distinct DMN connectivity decoupling in meditators and its relationship with meditation and yoga experience. These findings have interdisciplinary implications for neuroscience, psychology, and yogic disciplines.

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.

From physiological awakening to pathological sleep inertia: Neurophysiological and behavioural characteristics of the sleep-to-wake transition

Sleep inertia refers to the transient physiological state of hypoarousal upon awakening, associated with various degrees of impaired neurobehavioral performance, confusion, a desire to return to sleep and often a negative emotional state. Scalp and intracranial electro-encephalography as well as functional imaging studies have provided evidence that the sleep inertia phenomenon is underpinned by an heterogenous cerebral state mixing local sleep and local wake patterns of activity, at the neuronal and network levels. Sleep inertia is modulated by homeostasis and circadian processes, sleep stage upon awakening, and individual factors; this translates into a huge variability in its intensity even under physiological conditions. In sleep disorders, especially in hypersomnolence disorders such as idiopathic hypersomnia, sleep inertia may be a daily, serious and long-lasting symptom leading to severe impairment. To date, few tools have been developed to assess sleep inertia in clinical practice. They include mainly questionnaires and behavioral tests such as the psychomotor vigilance task. Only one neurophysiological protocol has been evaluated in hypersomnia, the forced awakening test which is based on an event-related potentials paradigm upon awakening. This contrasts with the major functional consequences of sleep inertia and its potentially dangerous consequences in subjects required to perform safety-critical tasks soon after awakening. There is a great need to identify reproducible biomarkers correlated with sleep inertia-associated cognitive and behavioral impairment. These biomarkers will aim at better understanding and measuring sleep inertia in physiological and pathological conditions, as well as objectively evaluating wake-promoting treatments or non-pharmacological countermeasures to reduce this phenomenon.

Circuits and Biomarkers of the Central Nervous System Relating to Astronaut Performance: Summary Report for a NASA-Sponsored Technical Interchange Meeting

Biomarkers, ranging from molecules to behavior, can be used to identify thresholds beyond which performance of mission tasks may be compromised and could potentially trigger the activation of countermeasures. Identification of homologous brain regions and/or neural circuits related to operational performance may allow for translational studies between species. Three discussion groups were directed to use operationally relevant performance tasks as a driver when identifying biomarkers and brain regions or circuits for selected constructs. Here we summarize small-group discussions in tables of circuits and biomarkers categorized by (a) sensorimotor, (b) behavioral medicine and (c) integrated approaches (e.g., physiological responses). In total, hundreds of biomarkers have been identified and are summarized herein by the respective group leads. We hope the meeting proceedings become a rich resource for NASA's Human Research Program (HRP) and the community of researchers.

Scratching the structure of moral agency: insights from philosophy applied to neuroscience

This paper explores the intersection between neuroscience and philosophy, particularly in the areas of moral philosophy and philosophy of mind. While traditional philosophical questions, such as those relating to free will and moral motivation, have been subject to much debate, the rise of neuroscience has led to a reinterpretation of these questions considering empirical evidence. This has led to tensions between those who believe neuroscience can provide definitive answers to very complex philosophical questions and those who are skeptical about the scope of these studies. However, the paper argues that neuroscientists and philosophers can work together to generate major scientific and social advances. To contribute to bridge the gap, in this paper we expose the complexity of moral experience from a philosophical point of view and point to two great challenges and gaps to cover from neurosciences.

Sleep, learning, and memory in human research using noninvasive neuroimaging techniques

An accumulating body of evidence indicates that sleep is beneficial for learning and memory. Task performance improves significantly after a period that includes sleep, whereas a lack of sleep nullifies or impairs such improvements. Our current knowledge about sleep's role in learning and memory has been obtained based on studies that were conducted in both animal models and human subjects. Nevertheless, how sleep promotes learning and memory in humans is not fully understood. In this review, we overview our current understating of how sleep may contribute to learning and memory, covering different roles of non-rapid eye movement and rapid eye movement sleep. We then discuss cutting-edge advanced techniques that are currently available, including simultaneous functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) and simultaneous functional magnetic resonance spectroscopy (fMRS) and EEG measurements, and evaluate how these may contribute to advance the understanding of the role of sleep in human cognition. We also highlight the current limitations and challenges using these methods and discuss ways that may allow us to overcome these limitations.

Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness

Wakefulness levels modulate estimates of functional connectivity (FC), and, if unaccounted for, can become a substantial confound in resting-state fMRI. Unfortunately, wakefulness is rarely monitored due to the need for additional concurrent recordings (e.g., eye tracking, EEG). Recent work has shown that strong fluctuations around 0.05Hz, hypothesized to be CSF inflow, appear in the fourth ventricle (FV) when subjects fall asleep, and that they correlate significantly with the global signal. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data and improve our understanding of FC during sleep. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project (HCP). Our results replicate the observation that fourth ventricle ultra-slow fluctuations (∼0.05Hz) with inflow-like characteristics (decreasing in intensity for successive slices) are present in scans during which subjects did not comply with instructions to keep their eyes open (i.e., drowsy scans). This is true despite the HCP data not being optimized for the detection of inflow-like effects. In addition, time-locked BOLD fluctuations of the same frequency could be detected in large portions of grey matter with a wide range of temporal delays and contribute in significant ways to our understanding of how FC changes during sleep. First, these ultra-slow fluctuations explain half of the increase in global signal that occurs during descent into sleep. Similarly, global shifts in FC between awake and sleep states are driven by changes in this slow frequency band. Second, they can influence estimates of inter-regional FC. For example, disconnection between frontal and posterior components of the Defulat Mode Network (DMN) typically reported during sleep were only detectable after regression of these ultra-slow fluctuations. Finally, we report that the temporal evolution of the power spectrum of these ultra-slow FV fluctuations can help us reproduce sample-level sleep patterns (e.g., a substantial number of subjects descending into sleep 3 minutes following scanning onset), partially rank scans according to overall drowsiness levels, and predict individual segments of elevated drowsiness (at 60 seconds resolution) with 71% accuracy.

Evaluation of functional MRI-based human brain parcellation: a review

Brain parcellations play a crucial role in the analysis of brain imaging data sets, as they can significantly affect the outcome of the analysis. In recent years, several novel approaches for constructing MRI-based brain parcellations have been developed with promising results. In the absence of ground truth, several evaluation approaches have been used to evaluate currently available brain parcellations. In this article, we review and critique methods used for evaluating functional brain parcellations constructed using fMRI data sets. We also describe how some of these evaluation methods have been used to estimate the optimal parcellation granularity. We provide a critical discussion of the current approach to the problem of identifying the optimal brain parcellation that is suited for a given neuroimaging study. We argue that the criteria for an optimal brain parcellation must depend on the application the parcellation is intended for. We describe a teleological approach to the evaluation of brain parcellations, where brain parcellations are evaluated in different contexts and optimal brain parcellations for each context are identified separately. We conclude by discussing several directions for further research that would result in improved evaluation strategies.

Topologically Disrupted Gray Matter Networks in Drug-Naïve Essential Tremor Patients With Poor Sleep Quality

Background

Sleep disturbances are widespread among patients with essential tremor (ET) and may have adverse effects on patients' quality of life. However, the pathophysiology underlying poor quality of sleep (QoS) in patients with ET remains unclear. Our study aimed to identify gray matter (GM) network alterations in the topological properties of structural MRI related to QoS in patients with ET.

Method

We enrolled 45 ET patients with poor QoS (SleET), 59 ET patients with normal QoS (NorET), and 66 healthy controls (HC), and they all underwent a three-dimensional T1-weighted MRI scan. We used a graph-theoretical approach to investigate the topological organization of GM morphological networks, and individual morphological brain networks were constructed according to the interregional similarity of GM volume distributions. Furthermore, we performed network-based statistics, and partial correlation analyses between topographic features and clinical characteristics were conducted.

Results

Global network organization was disrupted in patients with ET. Compared with the NorET group, the SleET group exhibited disrupted topological GM network organization with a shift toward randomization. Moreover, they showed altered nodal centralities in mainly the frontal, temporal, parietal, and cerebellar lobes. Morphological connection alterations within the default mode network (DMN), salience, and basal ganglia networks were observed in the SleET group and were generally more extensive than those in the NorET and HC groups. Alterations within the cerebello-thalamo-(cortical) network were only detected in the SleET group. The nodal degree of the left thalamus was negatively correlated with the Fahn-Tolosa-Marin Tremor Rating Scale score (r = −0.354, p =0.027).

Conclusion

Our findings suggest that potential complex interactions underlie tremor and sleep disruptions in patients with ET. Disruptions within the DMN and the cerebello-thalamo-(cortical) network may have a broader impact on sleep quality in patients with ET. Our results offer valuable insight into the neural mechanisms underlying poor QoS in patients with ET.

High Dream Recall Frequency is Associated with Increased Creativity and Default Mode Network Connectivity

INTRODUCTION

Several results suggest that the frequency of dream recall is positively correlated with personality traits such as creativity and openness to experience. In addition, neuroimaging results have evidenced different neurophysiological profiles in high dream recallers (HR) and low dream recallers (LR) during both sleep and wakefulness, specifically within regions of the default mode network (DMN). These findings are consistent with the emerging view that dreaming and mind wandering pertain to the same family of spontaneous mental processes, subserved by the DMN.

METHODS

To further test this hypothesis, we measured the DMN functional connectivity during resting wakefulness, together with personality and cognitive abilities (including creativity) in 28 HR and 27 LR.

RESULTS

As expected, HR demonstrated a greater DMN connectivity than LR, higher scores of creativity, and no significant difference in memory abilities. However, there was no significant correlation between creativity scores and DMN connectivity.

DISCUSSION

These results further demonstrate that there are trait neurophysiological and psychological differences between individuals who frequently recall their dreams and those who do not. They support the forebrain and the DMN hypotheses of dreaming and leave open the possibility that increased activity in the DMN promotes creative-thinking during both wakefulness and sleep. Further work is needed to test whether activity in the DMN is causally associated with creative-thinking.

Resting state network connectivity is attenuated by fMRI acoustic noise

INTRODUCTION

During the past decades there has been an increasing interest in tracking brain network fluctuations in health and disease by means of resting state functional magnetic resonance imaging (rs-fMRI). Rs-fMRI however does not provide the ideal environmental setting, as participants are continuously exposed to noise generated by MRI coils during acquisition of Echo Planar Imaging (EPI). We investigated the effect of EPI noise on resting state activity and connectivity using magnetoencephalography (MEG), by reproducing the acoustic characteristics of rs-fMRI environment during the recordings. As compared to fMRI, MEG has little sensitivity to brain activity generated in deep brain structures, but has the advantage to capture both the dynamic of cortical magnetic oscillations with high temporal resolution and the slow magnetic fluctuations highly correlated with BOLD signal.

METHODS

Thirty healthy subjects were enrolled in a counterbalanced design study including three conditions: a) silent resting state (Silence), b) resting state upon EPI noise (fMRI), and c) resting state upon white noise (White). White noise was employed to test the specificity of fMRI noise effect. The amplitude envelope correlation (AEC) in alpha band measured the connectivity of seven Resting State Networks (RSN) of interest (default mode network, dorsal attention network, language, left and right auditory and left and right sensory-motor). Vigilance dynamic was estimated from power spectral activity.

RESULTS

fMRI and White acoustic noise consistently reduced connectivity of cortical networks. The effects were widespread, but noise and network specificities were also present. For fMRI noise, decreased connectivity was found in the right auditory and sensory-motor networks. Progressive increase of slow theta-delta activity related to drowsiness was found in all conditions, but was significantly higher for fMRI . Theta-delta significantly and positively correlated with variations of cortical connectivity.

DISCUSSION

rs-fMRI connectivity is biased by unavoidable environmental factors during scanning, which warrant more careful control and improved experimental designs. MEG is free from acoustic noise and allows a sensitive estimation of resting state connectivity in cortical areas. Although underutilized, MEG could overcome issues related to noise during fMRI, in particular when investigation of motor and auditory networks is needed.

Fluctuations in Arousal Correlate with Neural Activity in the Human Thalamus

The neural basis of consciousness has been explored in humans and animals; however, the exact nature of consciousness remains elusive. In this study, we aimed to elucidate which brain regions are relevant to arousal in humans. Simultaneous recordings of brain activity and eye-tracking were conducted in 20 healthy human participants. Brain activity was measured by resting-state functional magnetic resonance imaging with a multiband acquisition protocol. The subjective levels of arousal were investigated based on the degree of eyelid closure that was recorded using a near-infrared eye camera within the scanner. The results showed that the participants were in an aroused state for 79% of the scan time, and the bilateral thalami were significantly associated with the arousal condition. Among the major thalamic subnuclei, the mediodorsal nucleus (MD) showed greater involvement in arousal when compared with other subnuclei. A receiver operating characteristic analysis with leave-one-out crossvalidation conducted using template-based brain activity and arousal-level data from eye-tracking showed that, in most participants, thalamic activity significantly predicted the subjective levels of arousal. These results indicate a significant role of the thalamus, and in particular, the MD, which has rich connectivity with the prefrontal cortices and the limbic system in human consciousness.

Decoding brain states on the intrinsic manifold of human brain dynamics across wakefulness and sleep

Current state-of-the-art functional magnetic resonance imaging (fMRI) offers remarkable imaging quality and resolution, yet, the intrinsic dimensionality of brain dynamics in different states (wakefulness, light and deep sleep) remains unknown. Here we present a method to reveal the low dimensional intrinsic manifold underlying human brain dynamics, which is invariant of the high dimensional spatio-temporal representation of the neuroimaging technology. By applying this intrinsic manifold framework to fMRI data acquired in wakefulness and sleep, we reveal the nonlinear differences between wakefulness and three different sleep stages, and successfully decode these different brain states with a mean accuracy across participants of 96%. Remarkably, a further group analysis shows that the intrinsic manifolds of all participants share a common topology. Overall, our results reveal the intrinsic manifold underlying the spatiotemporal dynamics of brain activity and demonstrate how this manifold enables the decoding of different brain states such as wakefulness and various sleep stages.

Regression dynamic causal modeling for resting-state fMRI

“Resting‐state” functional magnetic resonance imaging (rs‐fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks. Here, we show that a method recently developed for task‐fMRI—regression dynamic causal modeling (rDCM)—extends to rs‐fMRI and offers both directional estimates and scalability to whole‐brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal‐to‐noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs‐fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole‐brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.

Imaging the rapidly developing brain: Current challenges for MRI studies in the first five years of life

Rapid and widespread changes in brain anatomy and physiology in the first five years of life present substantial challenges for developmental structural, functional, and diffusion MRI studies. One persistent challenge is that methods best suited to earlier developmental stages are suboptimal for later stages, which engenders a trade-off between using different, but age-appropriate, methods for different developmental stages or identical methods across stages. Both options have potential benefits, but also biases, as pipelines for each developmental stage can be matched on methods or the age-appropriateness of methods, but not both. This review describes the data acquisition, processing, and analysis challenges that introduce these potential biases and attempts to elucidate decisions and make recommendations that would optimize developmental comparisons.

Brain functional connectivity upon awakening from sleep predicts interindividual differences in dream recall frequency

Why do some individuals recall dreams every day while others hardly ever recall one? We hypothesized that sleep inertia-the transient period following awakening associated with brain and cognitive alterations-could be a key mechanism to explain interindividual differences in dream recall at awakening. To test this hypothesis, we measured the brain functional connectivity (combined electroencephalography-functional magnetic resonance imaging) and cognition (memory and mental calculation) of high dream recallers (HR, n = 20) and low dream recallers (LR, n = 18) in the minutes following awakening from an early-afternoon nap. Resting-state scans were acquired just after or before a 2 min mental calculation task, before the nap, 5 min after awakening from the nap, and 25 min after awakening. A comic was presented to the participants before the nap with no explicit instructions to memorize it. Dream(s) and comic recall were collected after the first post-awakening scan. As expected, between-group contrasts of the functional connectivity at 5 min post-awakening revealed a pattern of enhanced connectivity in HR within the default mode network (DMN) and between regions of the DMN and regions involved in memory processes. At the behavioral level, a between-group difference was observed in dream recall, but not comic recall. Our results provide the first evidence that brain functional connectivity right after awakening is associated with interindividual trait differences in dream recall and suggest that the brain connectivity of HR at awakening facilitates the maintenance of the short-term memory of the dream during the sleep-wake transition.

Lucid Dreaming Brain Network Based on Tholey's 7 Klartraum Criteria

Lucid dreaming refers to a dream state characterized by the dreamers’ awareness of being in a dream and being able to volitionally control its content. The aim of this study was to describe and model neurophysiological evidence for the seven awareness criteria of lucid dreaming based on those proposed by Paul Tholey. Each of the awareness criteria was analyzed separately with regard to its underlying neurocircuits. We hypothesized that not one, but several regions are involved in the state of lucid dreaming. Our results have shown a satisfactory overlap of the awareness criteria and the brain regions activated. During lucid dreaming, a brain network seems to emerge, that is something other than the sum of its parts. Further research is needed to understand the psychoneurological underpinnings of lucid dreams.

The subcortical belly of sleep: New possibilities in neuromodulation of basal ganglia?

Early studies posited a relationship between sleep and the basal ganglia, but this relationship has received little attention recently. It is timely to revisit this relationship, given new insights into the functional anatomy of the basal ganglia and the physiology of sleep, which has been made possible by modern techniques such as chemogenetic and optogenetic mapping of neural circuits in rodents and intracranial recording, functional imaging, and a better understanding of human sleep disorders. We discuss the functional anatomy of the basal ganglia, and review evidence implicating their role in sleep. Whilst these studies are in their infancy, we suggest that the basal ganglia may play an integral role in the sleep-wake cycle, specifically by contributing to a thalamo-cortical-basal ganglia oscillatory network in slow-wave sleep which facilitates neural plasticity, and an active state during REM sleep which enables the enactment of cognitive and emotional networks. A better understanding of sleep mechanisms may pave the way for more effective neuromodulation strategies for sleep and basal ganglia disorders.

Electrically coupled inhibitory interneurons constrain long-range connectivity of cortical networks

Spontaneous infra-slow brain activity (ISA) exhibits a high degree of temporal synchrony, or correlation, between distant brain regions. The spatial organization of ISA synchrony is not explained by anatomical connections alone, suggesting that active neural processes coordinate spontaneous activity. Inhibitory interneurons (IINs) form electrically coupled connections via the gap junction protein connexin 36 (Cx36) and networks of interconnected IINs are known to influence neural synchrony over short distances. However, the role of electrically coupled IIN networks in regulating spontaneous correlation over the entire brain is unknown. In this study, we performed OIS imaging on Cx36-/- mice to examine the role of this gap junction in ISA correlation across the entire cortex. We show that Cx36 deletion increased long-distance intra-hemispheric anti-correlation and inter-hemispheric correlation in spontaneous ISA. This suggests that electrically coupled IIN networks modulate ISA synchrony over long cortical distances.

Dynamics of Cortical Local Connectivity during Sleep-Wake States and the Homeostatic Process

Sleep exerts modulatory effects on the cerebral cortex. Whether sleep modulates local connectivity in the cortex or only individual neural activity, however, is poorly understood. Here we investigated functional connectivity, that is, covarying activity between neurons, during spontaneous sleep-wake states and during and after sleep deprivation using calcium imaging of identified excitatory/inhibitory neurons in the motor cortex. Functional connectivity was estimated with a statistical learning approach glasso and quantified by "the probability of establishing connectivity (sparse/dense)" and "the strength of the established connectivity (weak/strong)." Local cortical connectivity was sparse in non-rapid eye movement (NREM) sleep and dense in REM sleep, which was similar in both excitatory and inhibitory neurons. The overall mean strength of the connectivity did not differ largely across spontaneous sleep-wake states. Sleep deprivation induced strong excitatory/inhibitory and dense inhibitory, but not excitatory, connectivity. Subsequent NREM sleep after sleep deprivation exhibited weak excitatory/inhibitory, sparse excitatory, and dense inhibitory connectivity. These findings indicate that sleep-wake states modulate local cortical connectivity, and the modulation is large and compensatory for stability of local circuits during the homeostatic control of sleep, which contributes to plastic changes in neural information flow.

Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches

The discovery of a stable, whole-brain functional connectivity organization that is largely independent of external events has drastically extended our view of human brain function. However, this discovery has been primarily based on functional magnetic resonance imaging (fMRI). The role of this whole-brain organization in fast oscillation-based connectivity as measured, for example, by electroencephalography (EEG) and magnetoencephalography (MEG) is only beginning to emerge. Here, we review studies of intrinsic connectivity and its whole-brain organization in EEG, MEG, and intracranial electrophysiology with a particular focus on direct comparisons to connectome studies in fMRI. Synthesizing this literature, we conclude that irrespective of temporal scale over four orders of magnitude, intrinsic neurophysiological connectivity shows spatial similarity to the connectivity organization commonly observed in fMRI. A shared structural connectivity basis and cross-frequency coupling are possible mechanisms contributing to this similarity. Acknowledging that a stable whole-brain organization governs long-range coupling across all timescales of neural processing motivates researchers to take "baseline" intrinsic connectivity into account when investigating brain-behavior associations, and further encourages more widespread exploration of functional connectomics approaches beyond fMRI by using EEG and MEG modalities.

SmartHypnos: Developing a Toolbox for Polysomnographic Data Visualization and Analysis

In this paper we present the first steps in developing SmartHypnos, an easy to use and user friendly graphical user interface, which aims to provide polysomngographic data visualization and the detection and classification of sleep related events. Currently SmartHypnos supports the visualization of EEG, ECG, EOG and EMG signals, and respiratory signals such as nasal pressure, thermistor, oxygen saturation, thoracic and abdominal belt recordings. All these are incorporated into an interface that provides quick and effortless access to the signals mentioned above. The interface displays automatic sleep staging capabilities as well as the detection of apnea events with accuracy rates surpassing 80%. It is expected that SmartHypnos will reduce the time required to analyze sleep data and also reduce possible human errors.

Relationship between the activity of the genioglossus, other peri-pharyngeal muscles and flow mechanics during wakefulness and sleep in patients with OSA and healthy subjects

INTRODUCTION

In patients with OSA, substantial increases in genioglossus (GG) activity during hypopneas/apneas usually fail to restore normal airflow. The present study was undertaken to evaluate if this phenomenon can be explained by reduced activation of other peri-pharyngeal muscles.

METHODS

We recorded EMGs of the GG and four other peri-pharyngeal muscles (accessory dilators, AD), in 8 patients with OSA and 12 healthy subjects, during wakefulness and sleep. Repetitive events of flow limitation were induced during sleep. The events with the highest increases in AD activity were evaluated, to assess if combined activation of both the GG and AD to levels higher than while awake ameliorate airflow reduction during sleep.

RESULTS

Flow limitation triggered large increases in GG-EMG, but only modest augmentation in AD activity. Nevertheless, phasic EMG activity was present in 40 % of the ADs during sleep. In multiple events, increases of both GG and AD activity to levels substantially higher than while awake were not associated with improvement in airflow.

CONCLUSIONS

We conclude that sleep-induced reduction in AD response to airway obstruction cannot completely explain the failure of upper airway dilators to maintain pharyngeal patency. We speculate that reduction in dilator muscle efficacy may be due to the alterations in motor units recruitment patterns during sleep.

[Sleep abnormality as a potential target of disease-modifying therapy for neurodegenerative diseases]

Sleep abnormality such as frequent nocturnal arousal and decreased deep non-REM (rapid-eye-movement) sleep is a prevalent but under-recognized symptom that affects patients with various neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD). In contrast to the conventional understanding that the sleep abnormality in these patients is caused by AD or PD pathology in the brain regions regulating sleep-wake or circadian rhythm, various epidemiological studies have demonstrated the association of sleep abnormality with an increased risk of these diseases. Through various recent studies using relevant animal models to test the causal relationship between sleep abnormality and neurodegenerative diseases, the recent concept of a bidirectional relationship between sleep abnormality and neurodegenerative diseases was established. However, whether therapeutic interventions against sleep abnormality would modify the disease course of neurodegenerative diseases remains unknown. In this review, we will first provide an overview of previous studies that link neurodegenerative diseases and sleep abnormality, mainly focusing on the sleep abnormality in patients with AD. We will then introduce the studies that examined the causal relationship between sleep abnormality and neurodegenerative diseases. Finally, we will discuss possible mechanisms underlying the bidirectional relationship between sleep abnormality and neurodegenerative diseases. A better understanding of these mechanisms would lead to the development of novel pharmacological and/or non-pharmacological treatments that would modify the disease course of neurodegenerative diseases through targeting the processes related to sleep abnormality in the patients of neurodegenerative diseases.

Unstable wakefulness during resting-state fMRI and its associations with network connectivity and affective psychopathology in young adults

BACKGROUND

Drifts between wakefulness and sleep are common during resting state functional MRI (rsfMRI). Among healthy adults, within-scanner sleep can impact functional connectivity of default mode (DMN), task-positive (TPN), and thalamo-cortical networks. Because dysfunctional arousal states (i.e., sleepiness, sleep disturbance) are common in affective disorders, individuals with affective psychopathology may be more prone to unstable wakefulness during rsfMRI, hampering the estimation of clinically meaningful functional connectivity biomarkers.

METHODS

A transdiagnostic sample of 150 young adults (68 psychologically distressed; 82 psychiatrically healthy) completed rsfMRI and reported whether they experienced within-scanner sleep. Symptom scales were reduced into depression/anxiety and mania proneness dimensions using principal component analysis. We evaluated associations between within-scanner sleep, clinical status, and functional connectivity of the DMN, TPN, and thalamus.

RESULTS

Within-scanner sleep during rsfMRI was reported by 44% of participants (n = 66) but was unrelated to psychiatric diagnoses or mood symptom severity (p-values > 0.05). Across all participants, self-reported within-scanner sleep was associated with connectivity signatures akin to objectively-assessed sleep, including lower within-DMN connectivity, lower DMN-TPN anti-correlation, and altered thalamo-cortical connectivity (p < 0.05, corrected). Among participants reporting sustained wakefulness (n = 84), depression/anxiety severity positively associated with averaged DMN-TPN connectivity and mania proneness negatively associated with averaged thalamus-DMN connectivity (p-values < 0.05). Both relationships were attenuated and became non-significant when participants reporting within-scanner sleep were included (p-values > 0.05).

LIMITATIONS

Subjective report of within-scanner sleep.

CONCLUSIONS

Findings implicate within-scanner sleep as a source of variance in network connectivity; careful monitoring and correction for within-scanner sleep may enhance our ability to characterize network signatures underlying affective psychopathology.

Sleep Spindle-dependent Functional Connectivity Correlates with Cognitive Abilities

EEG studies have shown that interindividual differences in the electrophysiological properties of sleep spindles (e.g., density, amplitude, duration) are highly correlated with trait-like "reasoning" abilities (i.e., "fluid intelligence"; problem-solving skills; the ability to employ logic or identify complex patterns), but not interindividual differences in STM or "verbal" intellectual abilities. Previous simultaneous EEG-fMRI studies revealed brain activations time-locked to spindles. Our group has recently demonstrated that the extent of activation in a subset of these regions was related to interindividual differences in reasoning intellectual abilities, specifically. However, spindles reflect communication between spatially distant and functionally distinct brain areas. The functional communication among brain regions related to spindles and their relationship to reasoning abilities have yet to be investigated. Using simultaneous EEG-fMRI sleep recordings and psychophysiological interaction analysis, we identified spindle-related functional communication among brain regions in the thalamo-cortical-BG system, the salience network, and the default mode network. Furthermore, the extent of the functional connectivity of the cortical-striatal circuitry and the thalamo-cortical circuitry was specifically related to reasoning abilities but was unrelated to STM or verbal abilities, thus suggesting that individuals with higher fluid intelligence have stronger functional coupling among these brain areas during spontaneous spindle events. This may serve as a first step in further understanding the function of sleep spindles and the brain network functional communication, which support the capacity for fluid intelligence.

Sleep inertia: current insights

Sleep inertia, or the grogginess felt upon awakening, is associated with significant cognitive performance decrements that dissipate as time awake increases. This impairment in cognitive performance has been observed in both tightly controlled in-laboratory studies and in real-world scenarios. Further, these decrements in performance are exaggerated by prior sleep loss and the time of day in which a person awakens. This review will examine current insights into the causes of sleep inertia, factors that may positively or negatively influence the degree of sleep inertia, the consequences of sleep inertia both in the laboratory and in real-world settings, and lastly discuss potential countermeasures to lessen the impact of sleep inertia.

Advanced network neuroscience approaches in sleep neurobiology on extreme environments

In this paper we propose a novel methodology for investigating pathological sleep patterns through network neuroscience approaches. It consists of initial identification of statistically significant alterations in cortical functional connectivity patterns. The resulting sub-network is then analyzed by employing graph theory for estimating both global performance metrics (integration and specialization) as well as the significance of specific network nodes and their hierarchical organization. So, nodes with important role in network structure are recognized and their functionality is correlated with adenosine biomarker which is important in sleep regulation and promotion. The aforementioned pipeline is applied in a dataset of sleep data gathered during a microgravity simulation experiment. The analysis was performed on cortical resting-state networks involved in sleep physiology. It demonstrated the detrimental effects of microgravity which were more prominent for the group which did not perform reactive sledge jumps as a countermeasure.

Separability of calcium slow waves and functional connectivity during wake, sleep, and anesthesia

Modulation of brain state, e.g., by anesthesia, alters the correlation structure of spontaneous activity, especially in the delta band. This effect has largely been attributed to the ∼ 1    Hz slow oscillation that is characteristic of anesthesia and nonrapid eye movement (NREM) sleep. However, the effect of the slow oscillation on correlation structures and the spectral content of spontaneous activity across brain states (including NREM) has not been comprehensively examined. Further, discrepancies between activity dynamics observed with hemoglobin versus calcium (GCaMP6) imaging have not been reconciled. Lastly, whether the slow oscillation replaces functional connectivity (FC) patterns typical of the alert state, or superimposes on them, remains unclear. Here, we use wide-field calcium imaging to study spontaneous cortical activity in awake, anesthetized, and naturally sleeping mice. We find modest brain state-dependent changes in infraslow correlations but larger changes in GCaMP6 delta correlations. Principal component analysis of GCaMP6 sleep/anesthesia data in the delta band revealed that the slow oscillation is largely confined to the first three components. Removal of these components revealed a correlation structure strikingly similar to that observed during wake. These results indicate that, during NREM sleep/anesthesia, the slow oscillation superimposes onto a canonical FC architecture.

Dynamic Network Activation of Hypothalamic MCH Neurons in REM Sleep and Exploratory Behavior

Most brain neurons are active in waking, but hypothalamic neurons that synthesize the neuropeptide melanin-concentrating hormone (MCH) are claimed to be active only during sleep, particularly rapid eye movement (REM) sleep. Here we use deep-brain imaging to identify changes in fluorescence of the genetically encoded calcium (Ca²⁺) indicator GCaMP6 in individual hypothalamic neurons that contain MCH. An in vitro electrophysiology study determined a strong relationship between depolarization and Ca²⁺ fluorescence in MCH neurons. In 10 freely behaving MCH-cre mice (male and female), the highest fluorescence occurred in all recorded neurons (n = 106) in REM sleep relative to quiet waking or non-REM sleep. Unexpectedly, 70% of the MCH neurons had strong fluorescence activity when the mice explored novel objects. Spatial and temporal mapping of the change in fluorescence between pairs of MCH neurons revealed dynamic activation of MCH neurons during REM sleep and activation of a subset of the same neurons during exploratory behavior. Functional network activity maps will facilitate comparisons of not only single-neuron activity, but also network responses in different conditions and disease.SIGNIFICANCE STATEMENT Functional activity maps identify brain circuits responding to specific behaviors, including rapid eye movement sleep (REM sleep), a sleep phase when the brain is as active as in waking. To provide the first activity map of individual neurons during REM sleep, we use deep-brain calcium imaging in unrestrained mice to map the activity of hypothalamic melanin-concentrating hormone (MCH) neurons. MCH neurons were found to be synchronously active during REM sleep, and also during the exploration of novel objects. Spatial mapping revealed dynamic network activation during REM sleep and activation of a subset of the neurons during exploratory behavior. Functional activity maps at the cellular level in specific behaviors, including sleep, are needed to establish a brain connectome.

Objective and subjective measures of prior sleep-wake behavior predict functional connectivity in the default mode network during NREM sleep

INTRODUCTION

Prior sleep behavior has been shown to correlate with waking resting-state functional connectivity (FC) in the default mode network (DMN). However, the impact of sleep history on FC during sleep has not been investigated. The aim of this study was to establish whether there is an association between intersubject variability in habitual sleep behaviors and the strength of FC within the regions of the DMN during non-rapid eye movement (NREM) sleep.

METHODS

Wrist actigraphy and sleep questionnaires were used as objective and subjective measures of habitual sleep behavior, and EEG-functional MRI during NREM sleep was used to quantify sleep.

RESULTS

There was a significant, regionally specific association between the interindividual variability in objective (total sleep time on the night before scanning) and subjective (Insomnia Severity Index) measures of prior sleep-wake behavior and the strength of DMN FC during subsequent wakefulness and NREM sleep. In several cases, FC was related to sleep measures independently of sleep stage, suggesting that previous sleep history effects sleep FC globally across the stages.

CONCLUSIONS

This work highlights the need to consider a subject's prior sleep history in studies utilizing FC analysis during wakefulness and sleep, and indicates the complexity of the impact of sleep on the brain both in the short and long term.

Spectral analysis of peri-pharyngeal muscles' EMG in patients with OSA and healthy subjects

INTRODUCTION

In addition to dyscoordination of upper airway dilator muscles activity, sleep may also alter the pattern of intra-muscular activation of single motor units (SMUs). Such changes should be identifiable by a state dependent change in EMG power spectrum, i.e., a shift in centroid frequency (ƒc) during sleep.

METHODS

EMGs of the genioglossus and four other peri-pharyngeal muscles were recorded in OSA patients (n = 8), age-matched healthy subjects (n = 7), and 5 young healthy subjects, and ƒc was calculated for wakefulness and sleep periods.

RESULTS

ƒc decreased with the onset of sleep and returned to baseline levels after arousal. ƒc of all muscles decreased similarly and significantly during sleep in the OSA and the age-matched healthy subjects, but not in the young subjects.

CONCLUSIONS

The pattern of decrease in ƒc is compatible with altered synchronization of SMUs during sleep. We speculate that these changes may contribute to the failure of dilator muscles to improve flow limitation during sleep in older subjects.

Physical Activity and Cognition: A Mediating Role of Efficient Sleep

OBJECTIVE

Physical activity benefits executive control, but the mechanism through which this benefit occurs is unclear. Sleep is a candidate mechanism given that it improves with exercise and has restorative effects on the prefrontal cortex. The present cross-sectional study examined the mediating role of sleep in the relationship between physical activity and executive control in young and older adults.

PARTICIPANTS

Young (n = 59) and older (n = 53) community-dwelling adults ages 21-30 and 55-80.

METHODS

Participants wore an accelerometer for one week to assess sleep efficiency, total sleep time, and physical activity, operationalized as metabolic equivalent of task (METs) during time spent awake. Cognition was assessed in the laboratory across multiple measures of executive control, memory recall, and processing speed. Mediation analyses tested the role of sleep efficiency in the cross-sectional relationship between METs and cognitive performance accounting for age, sex, and education.

RESULTS

METs were significantly associated with performance before, but not after accounting for covariates. METs were associated with sleep efficiency but not total sleep time. Sleep efficiency, but not total sleep time, mediated the relationship between METs and working memory, switching, verbal ability and fluency, and recall. Age group did not moderate the mediating role of sleep efficiency in the relationship between METs and performance.

CONCLUSION

Sleep efficiency is one pathway by which physical activity may be associated with executive control across young and older adults.

Altered Regional Cortical Brain Activity in Healthy Subjects After Sleep Deprivation: A Functional Magnetic Resonance Imaging Study

Objective: To investigate acute sleep deprivation (SD)-related regional brain activity changes and their relationships with behavioral performances.

Methods: Twenty-two female subjects underwent an MRI scan and an attention network test at rested wakefulness (RW) status and after 24 h SD. The amplitude of low-frequency fluctuations (ALFF) was used to investigate SD-related regional brain activity changes. We used the receiver operating characteristic (ROC) curve to evaluate the ability of the ALFF differences in regional brain areas to distinguish the SD status from the RW status. We used Pearson correlations to evaluate the relationships between the ALFF differences in brain areas and the behavioral performances during the SD status.

Results: Subjects at the SD status exhibited a lower accuracy rate and a longer reaction time relative to the RW status. Compared with RW, SD showed significant lower ALFF values in the right cerebellum anterior lobe, and higher ALFF areas in the bilateral inferior occipital gyrus, left thalamus, left insula, and bilateral postcentral gyrus. The area under the curve values of the specific ALFF differences in brain areas were (mean ± std, 0.851 ± 0.045; 0.805–0.93). Further, the ROC curve analysis demonstrated that the ALFF differences in those regional brain areas alone discriminated the SD status from the RW status with high degrees of sensitivities (82.16 ± 7.61%; 75–93.8%) and specificities (81.23 ± 11.39%; 62.5–93.7%). The accuracy rate showed negative correlations with the left inferior occipital gyrus, left thalamus, and left postcentral gyrus, and showed a positive correlation with the right cerebellum.

Conclusions: The ALFF analysis is a potential indicator for detecting the excitation–inhibition imbalance of regional cortical activations disturbed by acute SD with high performances.

On the Stability of BOLD fMRI Correlations

Measurement of correlations between brain regions (functional connectivity) using blood oxygen level dependent (BOLD) fMRI has proven to be a powerful tool for studying the functional organization of the brain. Recently, dynamic functional connectivity has emerged as a major topic in the resting-state BOLD fMRI literature. Here, using simulations and multiple sets of empirical observations, we confirm that imposed task states can alter the correlation structure of BOLD activity. However, we find that observations of "dynamic" BOLD correlations during the resting state are largely explained by sampling variability. Beyond sampling variability, the largest part of observed "dynamics" during rest is attributable to head motion. An additional component of dynamic variability during rest is attributable to fluctuating sleep state. Thus, aside from the preceding explanatory factors, a single correlation structure-as opposed to a sequence of distinct correlation structures-may adequately describe the resting state as measured by BOLD fMRI. These results suggest that resting-state BOLD correlations do not primarily reflect moment-to-moment changes in cognitive content. Rather, resting-state BOLD correlations may predominantly reflect processes concerned with the maintenance of the long-term stability of the brain's functional organization.

Hemispheric asymmetry and homotopy of resting state functional connectivity correlate with visuospatial abilities in school-age children

Hemispheric specialization of cognitive functions is a developmental process that shapes the brain from the gestational stage to adulthood. Functional connectivity of the resting brain has been largely used to infer the hemispheric organization of the spontaneous brain activity. In particular, two main properties have been largely explored throughout development: hemispheric asymmetry of functional connectivity and homotopic functional connectivity. However, their relation with specific cognitive processes typically associated with hemispheric specialization, such as visuospatial abilities, remains largely unexplored. Such relationships could be particularly relevant for the quest of developmental cognitive biomarkers in childhood, a significant maturation period of visuospatial abilities. Moreover, the relation between asymmetry and homotopy of brain functional connectivity is not well understood. We have examined these two properties in a sample of 60 typically developing children between 6 and 10 years of age, and explored their relation with visuospatial abilities. First, we identified a strong negative relation between homotopy and asymmetry across the brain. In addition, these properties showed areas in the posterior portion of the brain, with significant correlation with performance in visual memory and visual attention tasks. These results highlight the relevance of the hemispheric organization of spontaneous brain activity for developmental cognition, particularly for visuospatial abilities.

Impact of global signal regression on characterizing dynamic functional connectivity and brain states

Recently, resting-state functional magnetic resonance imaging (fMRI) studies have been extended to explore fluctuations in correlations over shorter timescales, referred to as dynamic functional connectivity (dFC). However, the impact of global signal regression (GSR) on dFC is not well established, despite the intensive investigations of the influence of GSR on static functional connectivity (sFC). This study aimed to examine the effect of GSR on the performance of the sliding-window correlation, a commonly used method for capturing functional connectivity (FC) dynamics based on resting-state fMRI and simultaneous electroencephalograph (EEG)-fMRI data. The results revealed that the impact of GSR on dFC was spatially heterogeneous, with some susceptible regions including the occipital cortex, sensorimotor area, precuneus, posterior insula and superior temporal gyrus, and that the impact was temporally modulated by the mean global signal (GS) magnitude across windows. Furthermore, GSR substantially changed the connectivity structures of the FC states responding to a high GS magnitude, as well as their temporal features, and even led to the emergence of new FC states. Conversely, those FC states marked by obvious anti-correlation structures associated with the default model network (DMN) were largely unaffected by GSR. Finally, we reported an association between the fluctuations in the windowed magnitude of GS and the time-varying EEG power within subjects, which implied changes in mental states underlying GS dynamics. Overall, this study suggested a potential neuropsychological basis, in addition to nuisance sources, for GS dynamics and highlighted the need for caution in applying GSR to sliding-window correlation analyses. At a minimum, the mental fluctuations of an individual subject, possibly related to ongoing vigilance, should be evaluated during the entire scan when the dynamics of FC is estimated.

Resting-state fMRI in sleeping infants more closely resembles adult sleep than adult wakefulness

Resting state functional magnetic resonance imaging (rs-fMRI) in infants enables important studies of functional brain organization early in human development. However, rs-fMRI in infants has universally been obtained during sleep to reduce participant motion artifact, raising the question of whether differences in functional organization between awake adults and sleeping infants that are commonly attributed to development may instead derive, at least in part, from sleep. This question is especially important as rs-fMRI differences in adult wake vs. sleep are well documented. To investigate this question, we compared functional connectivity and BOLD signal propagation patterns in 6, 12, and 24 month old sleeping infants with patterns in adult wakefulness and non-REM sleep. We find that important functional connectivity features seen during infant sleep closely resemble those seen during adult sleep, including reduced default mode network functional connectivity. However, we also find differences between infant and adult sleep, especially in thalamic BOLD signal propagation patterns. These findings highlight the importance of considering sleep state when drawing developmental inferences in infant rs-fMRI.

Resting state functional connectivity of the anterior striatum and prefrontal cortex predicts reading performance in school-age children

The current study investigated the neural basis of reading performance in 60 school-age Spanish-speaking children, aged 6 to 9years. By using a data-driven approach and an automated matching procedure, we identified a left-lateralized resting state network that included typical language regions (Wernicke's and Broca's regions), prefrontal cortex, pre- and post-central gyri, superior and middle temporal gyri, cerebellum, and subcortical regions, and explored its relevance for reading performance (accuracy, comprehension and speed). Functional connectivity of the left frontal and temporal cortices and subcortical regions predicted reading speed. These results extend previous findings on the relationship between functional connectivity and reading competence in children, providing new evidence about such relationships in previously unexplored regions in the resting brain, including the left caudate, putamen and thalamus. This work highlights the relevance of a broad network, functionally synchronized in the resting state, for the acquisition and perfecting of reading abilities in young children.

Visual experience sculpts whole-cortex spontaneous infraslow activity patterns through an Arc-dependent mechanism

Decades of work in experimental animals has established the importance of visual experience during critical periods for the development of normal sensory-evoked responses in the visual cortex. However, much less is known concerning the impact of early visual experience on the systems-level organization of spontaneous activity. Human resting-state fMRI has revealed that infraslow fluctuations in spontaneous activity are organized into stereotyped spatiotemporal patterns across the entire brain. Furthermore, the organization of spontaneous infraslow activity (ISA) is plastic in that it can be modulated by learning and experience, suggesting heightened sensitivity to change during critical periods. Here we used wide-field optical intrinsic signal imaging in mice to examine whole-cortex spontaneous ISA patterns. Using monocular or binocular visual deprivation, we examined the effects of critical period visual experience on the development of ISA correlation and latency patterns within and across cortical resting-state networks. Visual modification with monocular lid suturing reduced correlation between left and right cortices (homotopic correlation) within the visual network, but had little effect on internetwork correlation. In contrast, visual deprivation with binocular lid suturing resulted in increased visual homotopic correlation and increased anti-correlation between the visual network and several extravisual networks, suggesting cross-modal plasticity. These network-level changes were markedly attenuated in mice with genetic deletion of Arc, a gene known to be critical for activity-dependent synaptic plasticity. Taken together, our results suggest that critical period visual experience induces global changes in spontaneous ISA relationships, both within the visual network and across networks, through an Arc-dependent mechanism.

Behavioral and neural concordance in parent-child dyadic sleep patterns

Sleep habits developed in adolescence shape long-term trajectories of psychological, educational, and physiological well-being. Adolescents’ sleep behaviors are shaped by their parents’ sleep at both the behavioral and biological levels. In the current study, we sought to examine how neural concordance in resting-state functional connectivity between parent-child dyads is associated with dyadic concordance in sleep duration and adolescents’ sleep quality. To this end, we scanned both parents and their child (N = 28 parent-child dyads; parent M_age = 42.8 years; adolescent M_age = 14.9 years; 14.3% father; 46.4% female adolescent) as they each underwent a resting-state scan. Using daily diaries, we also assessed dyadic concordance in sleep duration across two weeks. Our results show that greater daily concordance in sleep behavior is associated with greater neural concordance in default-mode network connectivity between parents and children. Moreover, greater neural and behavioral concordances in sleep is associated with more optimal sleep quality in adolescents. The current findings expand our understanding of dyadic concordance by providing a neurobiological mechanism by which parents and children share daily sleep behaviors.

The global signal in fMRI: Nuisance or Information?

The global signal is widely used as a regressor or normalization factor for removing the effects of global variations in the analysis of functional magnetic resonance imaging (fMRI) studies. However, there is considerable controversy over its use because of the potential bias that can be introduced when it is applied to the analysis of both task-related and resting-state fMRI studies. In this paper we take a closer look at the global signal, examining in detail the various sources that can contribute to the signal. For the most part, the global signal has been treated as a nuisance term, but there is growing evidence that it may also contain valuable information. We also examine the various ways that the global signal has been used in the analysis of fMRI data, including global signal regression, global signal subtraction, and global signal normalization. Furthermore, we describe new ways for understanding the effects of global signal regression and its relation to the other approaches.

The short- and long-term proteomic effects of sleep deprivation on the cortical and thalamic synapses

Acute total sleep deprivation (SD) impairs memory consolidation, attention, working memory and perception. Structural, electrophysiological and molecular experimental approaches provided evidences for the involvement of sleep in synaptic functions. Despite the wide scientific interest on the effects of sleep on the synapse, there is a lack of systematic investigation of sleep-related changes in the synaptic proteome. We isolated parietal cortical and thalamic synaptosomes of rats after 8h of total SD by gentle handling and 16h after the end of deprivation to investigate the short- and longer-term effects of SD on the synaptic proteome, respectively. The SD efficiency was verified by electrophysiology. Protein abundance alterations of the synaptosomes were analyzed by fluorescent two-dimensional differential gel electrophoresis and by tandem mass spectrometry. As several altered proteins were found to be involved in synaptic strength regulation, our data can support the synaptic homeostasis hypothesis function of sleep and highlight the long-term influence of SD after the recovery sleep period, mostly on cortical synapses. Furthermore, the large-scale and brain area-specific protein network change in the synapses may support both ideas of sleep-related synaptogenesis and molecular maintenance and reorganization in normal rat brain.

Global signal regression acts as a temporal downweighting process in resting-state fMRI

In resting-state functional MRI (rsfMRI), the correlation between blood oxygenation level dependent (BOLD) signals across different brain regions is used to estimate the functional connectivity of the brain. This approach has led to the identification of a number of resting-state networks, including the default mode network (DMN) and the task positive network (TPN). Global signal regression (GSR) is a widely used pre-processing step in rsfMRI that has been shown to improve the spatial specificity of the estimated resting-state networks. In GSR, a whole brain average time series, known as the global signal (GS), is regressed out of each voxel time series prior to the computation of the correlations. However, the use of GSR is controversial because it can introduce artifactual negative correlations. For example, it has been argued that anticorrelations observed between the DMN and TPN are primarily an artifact of GSR. Despite the concerns about GSR, there is currently no consensus regarding its use. In this paper, we introduce a new framework for understanding the effects of GSR. In particular, we show that the main effects of GSR can be well approximated as a temporal downweighting process in which the data from time points with relatively large GS magnitudes are greatly attenuated while data from time points with relatively small GS magnitudes are largely unaffected. Furthermore, we show that a limiting case of this downweighting process in which data from time points with large GS magnitudes are censored can also approximate the effects of GSR. In other words, the correlation maps obtained after GSR show a high degree of spatial similarity (including the presence of anticorrelations between the DMN and TPN) with maps obtained using only the uncensored (i.e. retained) time points. Since the data from these retained time points are unaffected by the censoring process, this finding suggests that the observed anticorrelations inherently exist in the data from time points with small GS magnitudes and are not simply an artifact of GSR.