The new science of sleep: From cells to large-scale societies.

In the past 20 years, more remarkable revelations about sleep and its varied functions have arguably been made than in the previous 200. Building on this swell of recent findings, this essay provides a broad sampling of selected research highlights across genetic, molecular, cellular, and physiological systems within the body, networks within the brain, and large-scale social dynamics. Based on this raft of exciting new discoveries, we have come to realize that sleep, in this moment of its evolution, is very much polyfunctional (rather than monofunctional), yet polyfunctional for reasons we had never previously considered. Moreover, these new polyfunctional insights powerfully reaffirm sleep as a critical biological, and thus health-sustaining, requisite. Indeed, perhaps the only thing more impressive than the unanticipated nature of these newly emerging sleep functions is their striking divergence, from operations of molecular mechanisms inside cells to entire group societal dynamics.

Sleep loss leads to the withdrawal of human helping across individuals, groups, and large-scale societies.

Humans help each other. This fundamental feature of homo sapiens has been one of the most powerful forces sculpting the advent of modern civilizations. But what determines whether humans choose to help one another? Across 3 replicating studies, here, we demonstrate that sleep loss represents one previously unrecognized factor dictating whether humans choose to help each other, observed at 3 different scales (within individuals, across individuals, and across societies). First, at an individual level, 1 night of sleep loss triggers the withdrawal of help from one individual to another. Moreover, fMRI findings revealed that the withdrawal of human helping is associated with deactivation of key nodes within the social cognition brain network that facilitates prosociality. Second, at a group level, ecological night-to-night reductions in sleep across several nights predict corresponding next-day reductions in the choice to help others during day-to-day interactions. Third, at a large-scale national level, we demonstrate that 1 h of lost sleep opportunity, inflicted by the transition to Daylight Saving Time, reduces real-world altruistic helping through the act of donation giving, established through the analysis of over 3 million charitable donations. Therefore, inadequate sleep represents a significant influential force determining whether humans choose to help one another, observable across micro- and macroscopic levels of civilized interaction. The implications of this effect may be non-trivial when considering the essentiality of human helping in the maintenance of cooperative, civil society, combined with the reported decline in sufficient sleep in many first-world nations.

Abnormal Visual Evoked Responses to Emotional Cues Correspond to Diagnosis and Disease Severity in Fibromyalgia.

Background: Chronic pain disorders are often associated with cognitive-emotional dysregulation. However, the relations between such dysregulation, underlying brain processes, and clinical symptom constellations, remain unclear. Here, we aimed to characterize the abnormalities in cognitive-emotional processing involved in fibromyalgia syndrome (FMS) and their relation to disease severity. Methods: Fifty-eight participants, 39 FMS patients (35F), and 19 healthy control subjects (16F) performed an EEG-based paradigm assessing attention allocation by extracting steady-state visually evoked potentials (ssVEP) in response to affective distractors presented during a cognitive task. Patients were also evaluated for pain severity, sleep quality, depression, and anxiety. Results: EEG ssVEP measurement indicated that, compared to healthy controls, FMS patients displayed impaired affective discrimination, and sustained attention to negative distractors. Moreover, patients displayed decreased task-related fronto-occipital EEG connectivity. Lack of adaptive attentional discrimination, measured via EEG, was predictive of pain severity, while impairments in fronto-occipital connectivity were predictive of impaired sleep. Conclusions: FMS patients display maladaptive affective attention modulation, which predicts disease symptoms. These findings support the centrality of cognitive-emotional dysregulation in the pathophysiology of chronic pain.

Sleep Loss and the Socio-Emotional Brain.

Are you feeling emotionally fragile, moody, unpredictable, even ungenerous to those around you? Here, we review how and why these phenomena can occur as a result of insufficient sleep. Sleep loss disrupts a broad spectrum of affective processes, from basic emotional operations (e.g., recognition, responsivity, expression), through to high-order, complex socio-emotional functioning (e.g., loneliness, helping behavior, abusive behavior, and charisma). Translational insights further emerge regarding the pervasive link between sleep disturbance and psychiatric conditions, including anxiety, depression, and suicidality. More generally, such findings raise concerns regarding society's mental (ill)health and the prevalence of insufficient and disrupted sleep.

Overanxious and underslept.

Are you feeling anxious? Did you sleep poorly last night? Sleep disruption is a recognized feature of all anxiety disorders. Here, we investigate the basic brain mechanisms underlying the anxiogenic impact of sleep loss. Additionally, we explore whether subtle, societally common reductions in sleep trigger elevated next-day anxiety. Finally, we examine what it is about sleep, physiologically, that provides such an overnight anxiety-reduction benefit. We demonstrate that the anxiogenic impact of sleep loss is linked to impaired medial prefrontal cortex activity and associated connectivity with extended limbic regions. In contrast, non-rapid eye movement (NREM) slow-wave oscillations offer an ameliorating, anxiolytic benefit on these brain networks following sleep. Of societal relevance, we establish that even modest night-to-night reductions in sleep across the population predict consequential day-to-day increases in anxiety. These findings help contribute to an emerging framework explaining the intimate link between sleep and anxiety and further highlight the prospect of non-rapid eye movement sleep as a therapeutic target for meaningfully reducing anxiety.

Volitional limbic neuromodulation exerts a beneficial clinical effect on Fibromyalgia.

Volitional neural modulation using neurofeedback has been indicated as a potential treatment for chronic conditions that involve peripheral and central neural dysregulation. Here we utilized neurofeedback in patients suffering from Fibromyalgia - a chronic pain syndrome that involves sleep disturbance and emotion dysregulation. These ancillary symptoms, which have an amplificating effect on pain, are known to be mediated by heightened limbic activity. In order to reliably probe limbic activity in a scalable manner fit for EEG-neurofeedback training, we utilized an Electrical Finger Print (EFP) model of amygdala-BOLD signal (termed Amyg-EFP), that has been successfully validated in our lab in the context of volitional neuromodulation. We anticipated that Amyg-EFP-neurofeedback training aimed at limbic down modulation would improve chronic pain in patients suffering from Fibromyalgia, by reducing sleep disorder improving emotion regulation. We further expected that improved clinical status would correspond with successful training as indicated by improved down modulation of the Amygdala-EFP signal. Thirty-Four Fibromyalgia patients (31F; age 35.6 ±â€¯11.82) participated in a randomized placebo-controlled trial with biweekly Amyg-EFP-neurofeedback sessions or sham neurofeedback (n = 9) for a total duration of five consecutive weeks. Following training, participants in the real-neurofeedback group were divided into good (n = 13) or poor (n = 12) modulators according to their success in the neurofeedback training. Before and after treatment, self-reports on pain, depression, anxiety, fatigue and sleep quality were obtained, as well as objective sleep indices. Long-term clinical follow-up was made available, within up to three years of the neurofeedback training completion. REM latency and objective sleep quality index were robustly improved following the treatment course only in the real-neurofeedback group (time × group p < 0.05) and to a greater extent among good modulators (time × sub-group p < 0.05). In contrast, self-report measures did not reveal a treatment-specific response at the end of the neurofeedback training. However, the follow-up assessment revealed a delayed improvement in chronic pain and subjective sleep experience, evident only in the real-neurofeedback group (time × group p < 0.05). Moderation analysis showed that the enduring clinical effects on pain evident in the follow-up assessment were predicted by the immediate improvements following training in objective sleep and subjective affect measures. Our findings suggest that Amyg-EFP-neurofeedback that specifically targets limbic activity down modulation offers a successful principled approach for volitional EEG based neuromodulation treatment in Fibromyalgia patients. Importantly, it seems that via its immediate sleep improving effect, the neurofeedback training induced a delayed reduction in the target subjective symptom of chronic pain, far and beyond the immediate placebo effect. This indirect approach to chronic pain management reflects the substantial link between somatic and affective dysregulation that can be successfully targeted using neurofeedback.

Sleep loss causes social withdrawal and loneliness.

Loneliness and social isolation markedly increase mortality risk, and are linked to numerous mental and physical comorbidities, including sleep disruption. But does sleep loss causally trigger loneliness? Here, we demonstrate that a lack of sleep leads to a neural and behavioral phenotype of social withdrawal and loneliness; one that can be perceived by other members of society, and reciprocally, makes those societal members lonelier in return. We propose a model in which sleep loss instigates a propagating, self-reinforcing cycle of social separation and withdrawal.

Reduced mind wandering in patients with Parkinson's disease.

BACKGROUND: Mind Wandering (MW) refers to the process of disengaging from the immediate external environment and participating in internally driven mentation. This process has been suggested to be supported by a distributed set of brain regions, collectively referred to as the Default Mode Network (DMN). Recently, reduced recruitment and connectivity of the DMN has been described in Parkinson's disease (PD) patients compared to healthy controls. We thus aimed to explore whether PD patients with normal cognitive test scores show differential MW capabilities compared to healthy controls. METHODS: Thirty PD patients and thirty age-matched controls, all with a Montreal cognitive assessment (MoCA) score of 26 or above, performed a novel yet validated thought-sampling paradigm used to assess the frequency and extent of MW irrespective of cognitive load in which participants were asked to observe a series of geometric shapes and describe their thoughts after watching them. Shapes were presented one at a time for varying durations across nine trials. RESULTS: PD patients showed significantly less MW compared to the control. ANCOVA revealed a significant interaction indicating that the difference in MW scores was driven by trials with short stimulus presentation times. CONCLUSIONS: These findings provide evidence for decreased MW in PD patients. We propose that this is due to difficulties in performing MW within short time frames.

Tired and misconnected: A breakdown of brain modularity following sleep deprivation.

Sleep deprivation (SD) critically affects a range of cognitive and affective functions, typically assessed during task performance. Whether such impairments stem from changes to the brain's intrinsic functional connectivity remain largely unknown. To examine this hypothesis, we applied graph theoretical analysis on resting-state fMRI data derived from 18 healthy participants, acquired during both sleep-rested and sleep-deprived states. We hypothesized that parameters indicative of graph connectivity, such as modularity, will be impaired by sleep deprivation and that these changes will correlate with behavioral outcomes elicited by sleep loss. As expected, our findings point to a profound reduction in network modularity without sleep, evident in the limbic, default-mode, salience and executive modules. These changes were further associated with behavioral impairments elicited by SD: a decrease in salience module density was associated with worse task performance, an increase in limbic module density was predictive of stronger amygdala activation in a subsequent emotional-distraction task and a shift in frontal hub lateralization (from left to right) was associated with increased negative mood. Altogether, these results portray a loss of functional segregation within the brain and a shift towards a more random-like network without sleep, already detected in the spontaneous activity of the sleep-deprived brain. Hum Brain Mapp 38:3300-3314, 2017. © 2017 Wiley Periodicals, Inc.

Dependency Network Analysis (DEPNA) Reveals Context Related Influence of Brain Network Nodes.

Communication between and within brain regions is essential for information processing within functional networks. The current methods to determine the influence of one region on another are either based on temporal resolution, or require a predefined model for the connectivity direction. However these requirements are not always achieved, especially in fMRI studies, which have poor temporal resolution. We thus propose a new graph theory approach that focuses on the correlation influence between selected brain regions, entitled Dependency Network Analysis (DEPNA). Partial correlations are used to quantify the level of influence of each node during task performance. As a proof of concept, we conducted the DEPNA on simulated datasets and on two empirical motor and working memory fMRI tasks. The simulations revealed that the DEPNA correctly captures the network's hierarchy of influence. Applying DEPNA to the functional tasks reveals the dynamics between specific nodes as would be expected from prior knowledge. To conclude, we demonstrate that DEPNA can capture the most influencing nodes in the network, as they emerge during specific cognitive processes. This ability opens a new horizon for example in delineating critical nodes for specific clinical interventions.

Mindfulness Meditation Modulates Pain Through Endogenous Opioids.

BACKGROUND: Recent evidence supports the beneficial effects of mindfulness meditation on pain. However, the neural mechanisms underlying this effect remain poorly understood. We used an opioid blocker to examine whether mindfulness meditation-induced analgesia involves endogenous opioids. METHODS: Fifteen healthy experienced mindfulness meditation practitioners participated in a double-blind, randomized, placebo-controlled, crossover study. Participants rated the pain and unpleasantness of a cold stimulus prior to and after a mindfulness meditation session. Participants were then randomized to receive either intravenous naloxone or saline, after which they meditated again, and rated the same stimulus. RESULTS: A (3) × (2) repeated-measurements analysis of variance revealed a significant time effect for pain and unpleasantness scores (both P <.001) as well as a significant condition effect for pain and unpleasantness (both P <.2). Post hoc comparisons revealed that pain and unpleasantness scores were significantly reduced after natural mindfulness meditation and after placebo, but not after naloxone. Furthermore, there was a positive correlation between the pain scores following naloxone vs placebo and participants' mindfulness meditation experience. CONCLUSIONS: These findings show, for the first time, that meditation involves endogenous opioid pathways, mediating its analgesic effect and growing resilient with increasing practice to external suggestion. This finding could hold promising therapeutic implications and further elucidate the fine mechanisms involved in human pain modulation.

Human mesostriatal response tracks motivational tendencies under naturalistic goal conflict.

Goal conflict situations, involving the simultaneous presence of reward and punishment, occur commonly in real life, and reflect well-known individual differences in the behavioral tendency to approach or avoid. However, despite accumulating neural depiction of motivational processing, the investigation of naturalistic approach behavior and its interplay with individual tendencies is remarkably lacking. We developed a novel ecological interactive scenario which triggers motivational behavior under high or low goal conflict conditions. Fifty-five healthy subjects played the game during a functional magnetic resonance imaging scan. A machine-learning approach was applied to classify approach/avoidance behaviors during the game. To achieve an independent measure of individual tendencies, an integrative profile was composed from three established theoretical models. Results demonstrated that approach under high relative to low conflict involved increased activity in the ventral tegmental area (VTA), peri-aquaductal gray, ventral striatum (VS) and precuneus. Notably, only VS and VTA activations during high conflict discriminated between approach/avoidance personality profiles, suggesting that the relationship between individual personality and naturalistic motivational tendencies is uniquely associated with the mesostriatal pathway. VTA-VS further demonstrated stronger coupling during high vs low conflict. These findings are the first to unravel the multilevel relationship among personality profile, approach tendencies in naturalistic set-up and their underlying neural manifestation, thus enabling new avenues for investigating approach-related psychopathologies.

Comparison between human awake, meditation and drowsiness EEG activities based on directed transfer function and MVDR coherence methods.

This study examined the electroencephalogram functional connectivity (coherence) and effective connectivity (flow of information) of selected brain regions during three different attentive states: awake, meditation and drowsiness. For the estimation of functional connectivity (coherence), Welch and minimum variance distortionless response (MVDR) methods were compared. The MVDR coherence was found to be more suitable since it is both data and frequency dependent and enables higher spectral resolution, while Welch's periodogram-based approach is both data and frequency independent. The directed transfer function (DTF) method was applied in order to estimate the effective connectivity or brain's flow of information between different regions during each state. DTF enables to identify the main brain areas that initiate EEG activity and the spatial distribution of these activities with time. Analysis was conducted using the EEG data of 30 subjects (ten awake, ten drowsy and ten meditating) focusing on six main electrodes (F3, F4, C3, C4, P3, P4, O1 and O2). For each subject, EEG data were recorded during 5-min baseline and 15 min of a specific condition (awake, meditation or drowsiness). Statistical analysis included the Kruskal-Wallis (KW) nonparametric analysis of variance followed by post hoc tests with Bonferroni alpha correction. The results reveal that both states of drowsiness and meditation states lead to a marked difference in the brain's flow of information (effective connectivity) as shown by DTF analyses. In specific, a significant increase in the flow of information in the delta frequency band was found only in the meditation condition and was further found to originate from frontal (F3, F4), parietal (P3, P4) and occipital (O1, O2) regions. Altogether, these results suggest that a change in attentiveness leads to significant changes in the spectral profile of the brain's information flow as well as in its functional connectivity and that these changes can be captured using coherence and DTF analyses.

Information flow and coherence of EEG during awake, meditation and drowsiness.

A comparison of coupling (information flow) and coherence (connectedness) of the brain regions between human awake, meditation and drowsiness states was carried out in this study. The Directed Transfer Function (DTF) method was used to estimate the coupling or brain's flow of information between different regions during each condition. Welch and Minimum Variance Distortionless Response (MVDR) methods were utilised to estimate the coherence between brain areas. Analysis was conducted using the EEG data of 30 subjects (10 awake, 10 drowsiness and 10 meditating) with 6 EEG electrodes. The EEG data was recorded for each subject during 5 minutes baseline and 15 minutes of three specific conditions (awake, meditation or drowsiness). Statistical analysis was carried out which consisted of the Kruskal-Wallis (KW) non-parametric analysis of variance followed by post-hoc tests with Bonferroni alpha-correction. The results of this study revealed that a change in external awareness led to substantial differences in the spectral profile of the brain's information flow as well as it's connectedness.

Emotional processing of personally familiar faces in the vegetative state.

BACKGROUND: The Vegetative State (VS) is a severe disorder of consciousness in which patients are awake but display no signs of awareness. Yet, recent functional magnetic resonance imaging (fMRI) studies have demonstrated evidence for covert awareness in VS patients by recording specific brain activations during a cognitive task. However, the possible existence of incommunicable subjective emotional experiences in VS patients remains largely unexplored. This study aimed to probe the question of whether VS patients retain a brain ability to selectively process external stimuli according to their emotional value and look for evidence of covert emotional awareness in patients. METHODS AND FINDINGS: In order to explore these questions we employed the emotive impact of observing personally familiar faces, known to provoke specific perceptual as well as emotional brain activations. Four VS patients and thirteen healthy controls first underwent an fMRI scan while viewing pictures of non-familiar faces, personally familiar faces and pictures of themselves. In a subsequent imagery task participants were asked to actively imagine one of their parent's faces. Analyses focused on face and familiarity selective regional brain activations and inter-regional functional connectivity. Similar to controls, all patients displayed face selective brain responses with further limbic and cortical activations elicited by familiar faces. In patients as well as controls, Connectivity was observed between emotional, visual and face specific areas, suggesting aware emotional perception. This connectivity was strongest in the two patients who later recovered. Notably, these two patients also displayed selective amygdala activation during familiar face imagery, with one further exhibiting face selective activations, indistinguishable from healthy controls. CONCLUSIONS: Taken together, these results show that selective emotional processing can be elicited in VS patients both by external emotionally salient stimuli and by internal cognitive processes, suggesting the ability for covert emotional awareness of self and the environment in VS patients.

The dark side of the alpha rhythm: fMRI evidence for induced alpha modulation during complete darkness.

The unique role of the EEG alpha rhythm in different states of cortical activity is still debated. The main theories regarding alpha function posit either sensory processing or attention allocation as the main processes governing its modulation. Closing and opening eyes, a well-known manipulation of the alpha rhythm, could be regarded as attention allocation from inward to outward focus though during light is also accompanied by visual change. To disentangle the effects of attention allocation and sensory visual input on alpha modulation, 14 healthy subjects were asked to open and close their eyes during conditions of light and of complete darkness while simultaneous recordings of EEG and fMRI were acquired. Thus, during complete darkness the eyes-open condition is not related to visual input but only to attention allocation, allowing direct examination of its role in alpha modulation. A data-driven ridge regression classifier was applied to the EEG data in order to ascertain the contribution of the alpha rhythm to eyes-open/eyes-closed inference in both lighting conditions. Classifier results revealed significant alpha contribution during both light and dark conditions, suggesting that alpha rhythm modulation is closely linked to the change in the direction of attention regardless of the presence of visual sensory input. Furthermore, fMRI activation maps derived from an alpha modulation time-course during the complete darkness condition exhibited a right frontal cortical network associated with attention allocation. These findings support the importance of top-down processes such as attention allocation to alpha rhythm modulation, possibly as a prerequisite to its known bottom-up processing of sensory input.

The wandering mood: psychological and neural determinants of rest-related negative affect.

Rest related negative affect (RRNA) has gained scientific interest in the past decade. However, it is mostly studied within the context of mind-wandering (MW), and the relevance of other psychological and neural aspects of the resting state to its' occurrence has never been studied. Several indications associate RRNA with internally directed attention, yet the nature of this relation remains largely unknown. Moreover, the role of neural networks associated with rest related phenomenology - the default mode (DMN), executive (EXE), and salience (SAL) networks, has not been studied in this context. To this end, we explored two 5 (baseline) and 15-minute resting-state simultaneous fMRI-EEG scans of 29 participants. As vigilance has been shown to affect attention, and thus its availability for inward allocation, EEG-based vigilance levels were computed for each participant. Questionnaires for affective assessment were administered before and after scans, and retrospective reports of MW were additionally collected. Results revealed increased negative affect following rest, but only among participants who retained high vigilance levels. Among low-vigilance participants, changes in negative affect were negligible, despite reports of MW occurrence in both groups. In addition, in the high-vigilance group only, a significant increase in functional connectivity (FC) levels was found between the DMN-related ventral anterior cingulate cortex (ACC), associated with emotional processing, and the EXE-related dorsal ACC, associated with monitoring of self and other's behavior. These heightened FC levels further correlated with reported negative affect among this group. Taken together, these results demonstrate that, rather than an unavoidable outcome of the resting state, RRNA depends on internal allocation of attention at rest. Results are discussed in terms of two rest-related possible scenarios which defer in mental and neural processing, and subsequently, in the occurrence of RRNA.

Robust modeling based on optimized EEG bands for functional brain state inference.

The need to infer brain states in a data driven approach is crucial for BCI applications as well as for neuroscience research. In this work we present a novel classification framework based on Regularized Linear Regression classifier constructed from time-frequency decomposition of an EEG (electro-encephalography) signal. The regression is then used to derive a model of frequency distributions that identifies brain states. The process of classifier construction, preprocessing and selection of optimal regularization parameter by means of cross-validation is presented and discussed. The framework and the feature selection technique are demonstrated on EEG data recorded from 10 healthy subjects while requested to open and close their eyes every 30 s. This paradigm is well known in inducing Alpha power modulations that differ from low power (during eyes opened) to high (during eyes closed). The classifier was trained to infer eyes opened or eyes closed states and achieved higher than 90% classification accuracy. Furthermore, our findings reveal interesting patterns of relations between experimental conditions, EEG frequencies, regularization parameters and classifier choice. This viable tool enables identification of the most contributing frequency bands to any given brain state and their optimal combination in inferring this state. These features allow for much greater detail than the standard Fourier Transform power analysis, making it an essential method for both BCI proposes and neuroimaging research.