Awake State Research Articles

SleepMore

The availability of commercial wearable trackers equipped with features to monitor sleep duration and quality has enabled more useful sleep health monitoring applications and analyses. However, much research has reported the challenge of long-term user retention in sleep monitoring through these modalities. Since modern Internet users own multiple mobile devices, our work explores the possibility of employing ubiquitous mobile devices and passive WiFi sensing techniques to predict sleep duration as the fundamental measure for complementing long-term sleep monitoring initiatives. In this paper, we propose SleepMore, an accurate and easy-to-deploy sleep-tracking approach based on machine learning over the user's WiFi network activity. It first employs a semi-personalized random forest model with an infinitesimal jackknife variance estimation method to classify a user's network activity behavior into sleep and awake states per minute granularity. Through a moving average technique, the system uses these state sequences to estimate the user's nocturnal sleep period and its uncertainty rate. Uncertainty quantification enables SleepMore to overcome the impact of noisy WiFi data that can yield large prediction errors. We validate SleepMore using data from a month-long user study involving 46 college students and draw comparisons with the Oura Ring wearable. Beyond the college campus, we evaluate SleepMore on non-student users of different housing profiles. Our results demonstrate that SleepMore produces statistically indistinguishable sleep statistics from the Oura ring baseline for predictions made within a 5% uncertainty rate. These errors range between 15-28 minutes for determining sleep time and 7-29 minutes for determining wake time, proving statistically significant improvements over prior work. Our in-depth analysis explains the sources of errors.

State-dependent and region-specific alterations of cerebellar connectivity across stable human wakefulness and NREM sleep states

Sleep regulation and functioning may rely on systematic coordination throughout the whole brain, including the cerebellum. However, whether and how interactions between the cerebellum and other brain regions vary across sleep stages remain poorly understood. Here, using simultaneous EEG-fMRI recordings captured from 73 participants during wakefulness and non-rapid eye movement (NREM) sleep, we constructed cerebellar connectivity among intrinsic functional networks with intra-cerebellar, neocortical and subcortical regions. We uncovered that cerebellar connectivity exhibited sleep-dependent alterations: slight differences between wakefulness and N1/N2 sleep and greater changes in N3 sleep than other states. Region-specific cerebellar connectivity changes between N2 sleep and N3 sleep were also revealed: general breakdown of intra-cerebellar connectivity, enhancement of limbic-cerebellar connectivity and alterations of cerebellar connectivity with spatially specific neocortices. Further correlation analysis showed that functional connectivity between the cerebellar Control II network and regions (including the insula, hippocampus, and amygdala) correlated with delta power during N3 and beta power during N2 sleep. These findings systematically reveal altered cerebellar connectivity among intrinsic networks from wakefulness to deep sleep and highlight the potential role of the cerebellum in sleep regulation and functioning.

542: AWAKENING AFTER EXTRACORPOREAL LIFE SUPPORT: ASSESSING AWARENESS IN UNRESPONSIVE WAKEFULNESS

Introduction: Emergence from coma have many stages. Patients enter unresponsive wakefulness (UW) from coma when they open eyes with only reflexive motor responses. The development of voluntary responses to stimuli indicates a minimally conscious state (MCS). The addition of functional communication or object use signifies emergence from MCS. Cognitive motor dissociation has been reported in unresponsive patients with evidence of appropriate brain activation. We present a pediatric case where we detected awareness with bedside electroencephalogram (EEG) and the patient showed eventual emergence from UW. Description: A depressed teenage girl was admitted after suicidal attempt with fluoxetine and lamotrigine ingestion with subsequent cardiopulmonary arrest. After prolonged cardiopulmonary resuscitation, she was cannulated on veno-arterial extracorporeal membrane oxygenation. Initial head computed tomography showed preserved gray-white differentiation. On post-arrest day 5, she was successfully decannulated but remained in an unresponsive wakefulness state. Brain magnetic resonance imaging showed diffuse cerebral microhemorrhages. To assess preserved awareness, we performed protocolized stimulation with EEG. Consistent reactivity with increased frequency and amplitude were observed after both nociceptive and auditory stimulation, with greater response to stimuli from her parent. After slow improvement she emerged from UW on post-arrest day 35 and showed consistent appropriate responses. She was discharged to inpatient rehabilitation on post-arrest day 50. Discussion: Analysis of quantitative EEG data often requires advanced machine learning algorithms that were not readily available to bedside providers. Assessing EEG reactivity with protocolized stimulation is an effective alternative to assess preserved awareness. Our case adds to limited pediatric literature on emergence from coma and detection of preserved awareness early after brain injury. Clinically she was unresponsive to external stimuli but demonstrated signs of arousal to nociceptive and auditory stimuli on EEG, especially when from her parents. Our case demonstrates the feasibility of assessing preserved awareness at bedside via protocolized stimulation with EEG, which may assist in prognosis.

Relating Pupil Diameter and Blinking to Cortical Activity and Hemodynamics across Arousal States.

Arousal state affects neural activity and vascular dynamics in the cortex, with sleep associated with large changes in the local field potential and increases in cortical blood flow. We investigated the relationship between pupil diameter and blink rate with neural activity and blood volume in the somatosensory cortex in male and female unanesthetized, head-fixed mice. We monitored these variables while the mice were awake, during periods of rapid eye movement (REM), and non-rapid eye movement (NREM) sleep. Pupil diameter was smaller during sleep than in the awake state. Changes in pupil diameter were coherent with both gamma-band power and blood volume in the somatosensory cortex, but the strength and sign of this relationship varied with arousal state. We observed a strong negative correlation between pupil diameter and both gamma-band power and blood volume during periods of awake rest and NREM sleep, although the correlations between pupil diameter and these signals became positive during periods of alertness, active whisking, and REM. Blinking was associated with increases in arousal and decreases in blood volume when the mouse was asleep. Bilateral coherence in gamma-band power and in blood volume dropped following awake blinking, indicating a reset of neural and vascular activity. Using only eye metrics (pupil diameter and eye motion), we could determine the arousal state of the mouse ('Awake,' 'NREM,' 'REM') with >90% accuracy with a 5 s resolution. There is a strong relationship between pupil diameter and hemodynamics signals in mice, reflecting the pronounced effects of arousal on cerebrovascular dynamics.SIGNIFICANCE STATEMENT Determining arousal state is a critical component of any neuroscience experiment. Pupil diameter and blinking are influenced by arousal state, as are hemodynamics signals in the cortex. We investigated the relationship between cortical hemodynamics and pupil diameter and found that pupil diameter was strongly related to the blood volume in the cortex. Mice were more likely to be awake after blinking than before, and blinking resets neural activity. Pupil diameter and eye motion can be used as a reliable, noninvasive indicator of arousal state. As mice transition from wake to sleep and back again over a timescale of seconds, monitoring pupil diameter and eye motion permits the noninvasive detection of sleep events during behavioral or resting-state experiments.

Hypothalamic molecular correlates of photoperiod-induced spring migration in intact and castrated male redheaded buntings

This study investigated the molecular changes associated with neural plasticity in photoperiodic induction of spring migration in intact and castrated redheaded bunting, Emberiza bruniceps. We measured the hypothalamic mRNA expression of genes in birds that were photostimulated into winter non-migratory and spring (vernal) migratory phenotypes under short and long photoperiods, respectively. These included genes associated with the appetitive phase of reproduction (spring migration drive, th and ddc genes encoding for tyrosine hydroxylase and dopamine decarboxylase enzymes, respectively), sleep/awake state (pmch gene encoding for pro-melanin concentrating hormone; hcrt and hcrtr2 encoding for the hypocretin/orexin and its receptor, respectively) and neurogenesis (dcx and neuN coding for doublecortin and neuronal nuclear proteins, respectively). Higher th mRNA levels suggested an upregulated dopamine synthesis in the hypothalamus of spring migrants. Similarly, elevated hcrt and hcrtr2 mRNA levels suggested an increased wakefulness, and those of dcx and neuN genes suggested an enhanced neurogenesis during the spring migration state. Further, compared to intact birds, the lower th and pmch, and higher hcrtr2 and neuN mRNA levels in castrates suggested a role of testicular steroids in modulation of the appetitive phase of reproduction, sleep and awake states, and neurogenesis during spring migration period. These results provide insights into molecular changes linked with important hypothalamic molecular pathways and steroidal influence in the photoperiodic induction of spring migration in obligate migratory songbirds.

Intracellular chloride regulation mediates local sleep pressure in the cortex.

Extended wakefulness is associated with reduced performance and the build-up of sleep pressure. In the cortex, this manifests as changes in network activity. These changes show local variation depending on the waking experience, and their underlying mechanisms represent targets for overcoming the effects of tiredness. Here, we reveal a central role for intracellular chloride regulation, which sets the strength of postsynaptic inhibition via GABAA receptors in cortical pyramidal neurons. Wakefulness results in depolarizing shifts in the equilibrium potential for GABAA receptors, reflecting local activity-dependent processes during waking and involving changes in chloride cotransporter activity. These changes underlie electrophysiological and behavioral markers of local sleep pressure within the cortex, including the levels of slow-wave activity during non-rapid eye movement sleep and low-frequency oscillatory activity and reduced performance levels in the sleep-deprived awake state. These findings identify chloride regulation as a crucial link between sleep-wake history, cortical activity and behavior.

Stimulant drugs to promote the awake state and cognitive performance: do they really work?

Stimulant drugs to promote the awake state and cognitive performance: do they really work?

Dysfunction of the cardiac parasympathetic system in fatal familial insomnia: a heart rate variability study.

Although sympathetic hyperactivity with preserved parasympathetic activity has been extensively recognized in fatal familial insomnia (FFI), the symptoms of parasympathetic nervous system failure observed in some patients are difficult to explain. Using heart rate variability (HRV), this study aimed to discover evidence of parasympathetic dysfunction in patients with FFI and the difference of parasympathetic activity between patients with FFI and Creutzfeldt-Jakob disease (CJD). This study enrolled nine patients with FFI, eight patients with CJD and 18 healthy controls (HCs) from May 2013 to August 2020. All participants underwent a nocturnal video-polysomnography with lead II electrocardiography, and the data were analyzed using linear and nonlinear indices of HRV during both wake and sleep states. Compared to the HC and CJD groups, the FFI group had a continuously higher heart rate with a lower amplitude of oscillations. The low frequency (LF)/high frequency (HF) ratio and ratio of SD1 to SD2 and correlation dimension D2 (CD2) were significantly different in the FFI group compared to the HC group. The root mean square of successive differences (RMSSD), HF and SD1 in the FFI group were significantly lower than in the HC group. RMSSD, SD1, and CD2 in the FFI group were all significantly lower than in the CJD group. Cardiovascular dysautonomia in FFI may be partly attributable to parasympathetic abnormalities, not just sympathetic activation. HRV may be helpful as a noninvasive, quantitative, and effective autonomic function test for FFI diagnosis.

Machine Learning Identifies a Rat Model of Parkinson’s Disease via Sleep-Wake Electroencephalogram

Alpha-synuclein induced degeneration of the midbrain substantia nigra pars compact (SNc) dopaminergic neurons causes Parkinson’s disease (PD). Rodent studies demonstrate that nigrostriatal dopamine stimulates pallidal neurons which, via the topographical pallidocortical pathway, regulate cortical activity and functions. We hypothesize that nigrostriatal dopamine acting at the basal ganglia regulates cortical activity in sleep and wake state, and its depletion systemically alters electroencephalogram (EEG) across frequencies during sleep-wake state. Compared to control rats, 6-hydroxydopamine induced selective SNc lesions increased overall EEG power (positive synchronization) across 0.5–60 Hz during wake, NREM (non-rapid eye movement) sleep, and REM sleep. Application of machine learning (ML) to seven EEG features computed at a single or combined spectral bands during sleep-wake differentiated SNc lesions from controls at high accuracy. ML algorithms construct a model based on empirical data to make predictions on subsequent data. The accuracy of the predictive results indicate that nigrostriatal dopamine depletion increases global EEG spectral synchronization in wake, NREM sleep, and REM sleep. The EEG changes can be exploited by ML to identify SNc lesions at a high accuracy.

Association of sleep quality and diffusion MRI derived interstitial fluid content – insights in cerebral waste clearance

AbstractBackgroundDisruptions in sleep are associated with Alzheimer’s disease (AD) progression. As cerebral waste clearance is most active during sleep, these reductions in sleep quality could induce impaired clearance function. Waste products are washed through the parenchyma in the interstitial fluid (ISF), its volume being regulated by the sleep‐wake cycle. A smaller ISF‐volume is found in awake states and after sleep deprivation. IntraVoxel Incoherent Motion (IVIM) MRI can detect the diffusion of cerebral ISF (Dint) and its volume fraction (fint).Using IVIM, the current exploratory study investigates whether alterations in a proxy of ISF‐volume (fint) are related to hours of sleep or self‐reported sleep quality.MethodTwenty neurotypical elderly subjects (Table 1) underwent ultra‐high field MRI (7T research system, Siemens Healthcare GmbH, Erlangen, Germany) (Table 2).The intermediate diffusion components (Dint) in the range 1.5*10‐3<Diffusivity<4.0*10‐3 mm2/s were calculated using spectral analysis (Figure 1). The relative signal contribution of Dint was quantified by the ISF‐fraction (fint). Median fint values were extracted from the white matter (WM) and gray matter (GM).Sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI). In addition to the clinically used total PSQI (sum of all 7 components), the PSQI1 (Subjective sleep quality) and PSQI3 (Sleep duration) components were considered.Pearson correlations were computed between the PSQI scores and fint for WM and GM. To check for potential confounding influences, partial correlations were performed adjusting for age and time of MRI‐acquisition.ResultThe sample characteristics and descriptive statistics are summarized in Table 1. Worse reported sleep quality (PSQI1) significantly relates to lower WM fint (Table 3 and Figure 2). After adjusting for potential confounding influences, a trend towards significance remained (R=‐0.437, p=0.061).ConclusionThe current explorative study identified a lower diffusion MRI‐derived proxy of ISF‐volume in the WM of subjects who reported a subjective feeling of long‐term sleep deprivation. A reduction in ISF‐volume may leave less space for waste products, such as soluble Amyloid‐beta, to be cleared from in‐between the cells. Thereby, this study puts forward a potential method for future studies to investigate impaired clearance function in relation to sleep in patients with AD pathology.

Role of Oxidative Stress and Inflammation in Insomnia Sleep Disorder and Cardiovascular Diseases: Herbal Antioxidants and Anti-inflammatory Coupled with Insomnia Detection using Machine Learning.

Insomnia is well-known as trouble in sleeping and enormously influences human life due to the shortage of sleep. Reactive Oxygen Species (ROS) accrue in neurons during the waking state, and sleep has a defensive role against oxidative damage and dissipates ROS in the brain. In contrast, insomnia is the source of inequity between ROS generation and removal by an endogenous antioxidant defense system. The relationship between insomnia, depression, and anxiety disorders damages the cardiovascular systems' immune mechanisms and functions. Traditionally, polysomnography is used in the diagnosis of insomnia. This technique is complex, with a long time overhead. In this work, we have proposed a novel machine learning-based automatic detection system using the R-R intervals extracted from a single-lead electrocardiograph (ECG). Additionally, we aimed to explore the role of oxidative stress and inflammation in sleeping disorders and cardiovascular diseases, antioxidants' effects, and the psychopharmacological effect of herbal medicine. This work has been carried out in steps, which include collecting the ECG signal for normal and insomnia subjects, analyzing the signal, and finally, automatic classification. We used two approaches, including subjects (normal and insomnia), two sleep stages, i.e., wake and rapid eye movement, and three Machine Learning (ML)-based classifiers to complete the classification. A total number of 3000 ECG segments were collected from 18 subjects. Furthermore, using the theranostics approach, the role of mitochondrial dysfunction causing oxidative stress and inflammatory response in insomnia and cardiovascular diseases was explored. The data from various databases on the mechanism of action of different herbal medicines in insomnia and cardiovascular diseases with antioxidant and antidepressant activities were also retrieved. Random Forest (RF) classifier has shown the highest accuracy (subjects: 87.10% and sleep stage: 88.30%) compared to the Decision Tree (DT) and Support Vector Machine (SVM). The results revealed that the suggested method could perform well in classifying the subjects and sleep stages. Additionally, a random forest machine learning-based classifier could be helpful in the clinical discovery of sleep complications, including insomnia. The evidence retrieved from the databases showed that herbal medicine contains numerous phytochemical bioactives and has multimodal cellular mechanisms of action, viz., antioxidant, anti-inflammatory, vasorelaxant, detoxifier, antidepressant, anxiolytic, and cell-rejuvenator properties. Other herbal medicines have a GABA-A receptor agonist effect. Hence, we recommend that the theranostics approach has potential and can be adopted for future research to improve the quality of life of humans.

Association of sleep quality and diffusion MRI derived interstitial fluid content – insights in cerebral waste clearance

AbstractBackgroundDisruptions in sleep are associated with Alzheimer’s disease (AD) progression. As cerebral waste clearance is most active during sleep, these reductions in sleep quality could induce impaired clearance function. Waste products are washed through the parenchyma in the interstitial fluid (ISF), its volume being regulated by the sleep‐wake cycle. A smaller ISF‐volume is found in awake states and after sleep deprivation. IntraVoxel Incoherent Motion (IVIM) MRI can detect the diffusion of cerebral ISF (Dint) and its volume fraction (fint).Using IVIM, the current exploratory study investigates whether alterations in a proxy of ISF‐volume (fint) are related to hours of sleep or self‐reported sleep quality.MethodTwenty neurotypical elderly subjects (Table 1) underwent ultra‐high field MRI (7T research system, Siemens Healthcare GmbH, Erlangen, Germany) (Table 2).The intermediate diffusion components (Dint) in the range 1.5*10‐3<Diffusivity<4.0*10‐3 mm2/s were calculated using spectral analysis (Figure 1). The relative signal contribution of Dint was quantified by the ISF‐fraction (fint). Median fint values were extracted from the white matter (WM) and gray matter (GM).Sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI). In addition to the clinically used total PSQI (sum of all 7 components), the PSQI1 (Subjective sleep quality) and PSQI3 (Sleep duration) components were considered.Pearson correlations were computed between the PSQI scores and fint for WM and GM. To check for potential confounding influences, partial correlations were performed adjusting for age and time of MRI‐acquisition.ResultThe sample characteristics and descriptive statistics are summarized in Table 1. Worse reported sleep quality (PSQI1) significantly relates to lower WM fint (Table 3 and Figure 2). After adjusting for potential confounding influences, a trend towards significance remained (R=‐0.437, p=0.061).ConclusionThe current explorative study identified a lower diffusion MRI‐derived proxy of ISF‐volume in the WM of subjects who reported a subjective feeling of long‐term sleep deprivation. A reduction in ISF‐volume may leave less space for waste products, such as soluble Amyloid‐beta, to be cleared from in‐between the cells. Thereby, this study puts forward a potential method for future studies to investigate impaired clearance function in relation to sleep in patients with AD pathology.

Temporal intermittent rhythmic theta activity (TIRTA): A marker of epileptogenicity?

To describe a novel EEG rhythm, temporal intermittent rhythmic theta activity (TIRTA), and its potential association with epilepsy. We report TIRTA on scalp EEG in a series of 12 patients, all of whom were found to have epilepsy. The clinical and electroencephalographic characteristics of each patient were reviewed. In addition, features that may distinguish TIRTA from benign EEG patterns, including rhythmic temporal theta bursts of drowsiness (RTTBD), were identified. TIRTA was unilateral in all cases. For all patients, TIRTA was seen in the awake and drowsy states. Eight patients also had TIRTA observed during N2 sleep. The average frequency of TIRTA was 5.5Hz and the average duration of a train of TIRTA was 5.25s. In seven cases the morphology was notched in appearance. Temporal intermittent rhythmic delta activity (TIRDA) was seen in seven patients on the same side as TIRTA. Eleven patients also had ipsilateral temporal sharp waves. Abnormal MRI (6/12) and or PET (5/5) findings were ipsilateral to TIRTA. In this preliminary report we suggest that TIRTA may be a novel marker of potential epileptogenicity, possibly representing a higher frequency variant of TIRDA.

Analysis of EEG Signal Components based on Meditation and Mind-Wandering Between Experienced Meditators and Non-Meditators

Through meditation, one can acquire a calm, awake state of mind. It can assist people in becoming more conscious of themselves and their surroundings. Pain, worry, and other undesirable feelings may be significantly reduced with meditation. EEG is now one of the methods widely employed in the field of neuroscience to study how the brain works. Different capabilities of the brain are identified via electroencephalography (EEG), which uses brain data collected through electrodes. In this paper, the behavior of five participants with greater than three years of meditative experience and 5 persons with no meditative background is analyzed in resting and meditative positions. The behavioral changes in the meditators versus novices concerning EEG signal components such as alpha α, beta β, gamma ϒ, delta ẟ, and theta θ are analyzed with the help of changes observed in parameters such as power, mean, and standard deviation (SD). Then finally, the significance of the results obtained is statistically analyzed by finding the p-value using a t-test, with a 95% confidence interval. A p-value less than 0.05 is considered significant.

Conserved Population Dynamics in the Cerebro-Cerebellar System between Waking and Sleep.

Despite the importance of the cerebellum for motor learning, and the recognized role of sleep in motor memory consolidation, surprisingly little is known about neural activity in the sleeping cerebro-cerebellar system. Here, we used wireless recording from primary motor cortex (M1) and the cerebellum in three female monkeys to examine the relationship between patterns of single-unit spiking activity observed during waking behavior and in natural sleep. Across the population of recorded units, we observed similarities in the timing of firing relative to local field potential features associated with both movements during waking and up state during sleep. We also observed a consistent pattern of asymmetry in pairwise cross-correlograms, indicative of preserved sequential firing in both wake and sleep at low frequencies. Despite the overall similarity in population dynamics between wake and sleep, there was a global change in the timing of cerebellar activity relative to motor cortex, from contemporaneous in the awake state to motor cortex preceding the cerebellum in sleep. We speculate that similar population dynamics in waking and sleep may imply that cerebellar internal models are activated in both states, despite the absence of movement when asleep. Moreover, spindle frequency coherence between the cerebellum and motor cortex may provide a mechanism for cerebellar computations to influence sleep-dependent learning processes in the motor cortex.SIGNIFICANCE STATEMENT It is well known that sleep can lead to improved motor performance. One possibility is that off-line learning results from neural activity during sleep in brain areas responsible for the control of movement. In this study we show for the first time that neuronal patterns in the cerebro-cerebellar system are conserved during both movements and sleep up-states, albeit with a shift in the relative timing between areas. Additionally, we show the presence of simultaneous M1-cerebellar spike coherence at spindle frequencies associated with up-state replay and postulate that this is a mechanism whereby a cerebellar internal model can shape plasticity in neocortical circuits during sleep.

NIMG-100. THE MEDIAN NERVE STIMULATION INDUCES LONG LATENCY GAMMA MODULATIONS IN THE PRIMARY SOMATOSENSORY CORTEX, WHICH DIFFERENTIATES THE CONSCIOUS STATE

Abstract OBJECTIVE While the intraoperative assessment of the short-latency somatosensory evoked potentials (SSEPs) phase reversal in electrocorticogram (ECoG) is routinely used to locate the central sulcus (CS), its spatial-spectral patterns in the primary sensorimotor cortex and the relation to consciousness is not well characterized. Our goal was to evaluate the functional utility of using median nerve stimulation-induced ECoG sub-band modulations in pre-and post-CS to differentiate between anesthetized and awake states. Method: SSEPs were recorded from the sensorimotor cortex with ECoG during routine intraoperative cortical mapping of 8 patients in the anesthetized and awake states. We conducted a time-frequency analysis on the SSEP trace in each state to extract the spectral modulations up to 900Hz and contrasted their intensity in M1 and S1 to discern awake from anesthetized states. RESULTS We observed late gamma activity ranging from 60-250Hz, starting approximately 50ms after stimulation onset and extending up to 250ms. The late gamma activity was suppressed in the anesthetized state in both M1 (p< 0.01) and S1 (p=0.0369). Whiles in the awake state, the late gamma power increased significantly with respect to baseline both in M1 (p=0.0180) and S1 (p< 0.01). The difference in late gamma power between the anesthetized and the awake state was highly significant for M1 (p< 0.01) and for S1 (p< 0.01). CONCLUSION The results show that besides CS delineation, SSEPs long latency gamma modulations can serve as an additional biomarker to monitor the level of consciousness in neurosurgical practice.

REM sleep is associated with distinct global cortical dynamics and controlled by occipital cortex

The cerebral cortex is spontaneously active during sleep, yet it is unclear how this global cortical activity is spatiotemporally organized, and whether such activity not only reflects sleep states but also contributes to sleep state switching. Here we report that cortex-wide calcium imaging in mice revealed distinct sleep stage-dependent spatiotemporal patterns of global cortical activity, and modulation of such patterns could regulate sleep state switching. In particular, elevated activation in the occipital cortical regions (including the retrosplenial cortex and visual areas) became dominant during rapid-eye-movement (REM) sleep. Furthermore, such pontogeniculooccipital (PGO) wave-like activity was associated with transitions to REM sleep, and optogenetic inhibition of occipital activity strongly promoted deep sleep by suppressing the NREM-to-REM transition. Thus, whereas subcortical networks are critical for initiating and maintaining sleep and wakefulness states, distinct global cortical activity also plays an active role in controlling sleep states.

Detecting residual brain networks in disorders of consciousness: A resting-state fNIRS study

Functional near infrared spectroscopy (fNIRS) is an emerging non-invasive technique that allows bedside measurement of blood oxygenation level-dependent hemodynamic signals. We aimed to examine the efficacy of resting-state fNIRS in detecting the residual functional networks in patients with disorders of consciousness (DOC). We performed resting-state fNIRS in 23 DOC patients of whom 12 were in minimally conscious state (MCS) and 11 were in unresponsive wakefulness state (UWS). Ten regions of interest (ROIs) in the prefrontal cortex (PFC) were selected: both sides of Brodmann area (BA) 9, BA10, BA44, BA45, and BA46. Graph-theoretical analysis and seed-based correlation analyses were used to investigate the network topology and the strength of pairwise connections between ROIs and channels. MCS and UWS exhibited varying degrees of the loss of topological architecture, and the regional nodal properties of BA10 were significantly different between them (Nodal degree, PLeft BA10 = 0.01, PRight BA10 < 0.01; nodal efficiency, PLeft BA10 = 0.03, PRight BA10 < 0.01). Compared to healthy controls, UWS had impaired functions in both short- and long-distance connectivity, however, MCS had significantly impaired functions only in long-distance connectivity. The functional connectivity of right BA10 (AUC = 0.88) and the connections between left BA46 and right BA10 (AUC = 0.86) had excellent performance in differentiating MCS and UWS. MCS and UWS have different patterns of topological architecture and short- and long-distance connectivity in PFC. Intraconnections within BA10 and interhemispheric connections between BA10 and 46 are excellent resting-state fNIRS classifiers for distinguishing between MCS and UWS.

The EEG Features of Four Cases of Subacute Sclerosing Panencephalitis in North China: SSPE EEG Features.

Objective. To analyze the EEG features of four subacute sclerosing panencephalitis cases in North China. Methods. We retrospectively analyzed the EEG features in four patients with subacute sclerosing panencephalitis and 12 patients in control group from North China. Results. The periodic long-interval diffuse discharges were found in all of the four cases with subacute sclerosing panencephalitis. The morphology and component of periodic complexes were varied in different patients and different wakefulness states. Some EEG parameter settings help to identify periodic long-interval diffuse discharges including the slowed sweep speed, decreased sensitivity and reduced number of montages. In each patient with subacute sclerosing panencephalitis, the periodic long-interval diffuse discharges associated with two types of brief episodes (1:1) during awake period were found and none of the patients in the control group had this EEG pattern. The score system based on the periodic discharges and brief episodes also shows that all the patients with SSPE reached score 5 while none of the patients in the control group has a score greater than 3, which suggests that this EEG pattern may have diagnostic value. Conclusions. In subacute sclerosing panencephalitis, the morphology and component of periodic long-interval diffuse discharges were varied in different patients and different wakefulness states. Specific EEG parameter settings help to identify periodic long-interval diffuse discharges. Periodic long-interval diffuse discharges associated with two types of brief episodes (1:1) during awake period may strongly suggest the diagnosis of subacute sclerosing panencephalitis.

Functional network properties derived from wide-field calcium imaging differ with wakefulness and across cell type

To improve ‘bench-to-bedside’ translation, it is integral that knowledge flows bidirectionally—from animal models to humans, and vice versa. This requires common analytical frameworks, as well as open software and data sharing practices. We share a new pipeline (and test dataset) for the preprocessing of wide-field optical fluorescence imaging data—an emerging mode applicable in animal models—as well as results from a functional connectivity and graph theory analysis inspired by recent work in the human neuroimaging field. The approach is demonstrated using a dataset comprised of two test-cases: (1) data from animals imaged during awake and anesthetized conditions with excitatory neurons labeled, and (2) data from awake animals with different genetically encoded fluorescent labels that target either excitatory neurons or inhibitory interneuron subtypes. Both seed-based connectivity and graph theory measures (global efficiency, transitivity, modularity, and characteristic path-length) are shown to be useful in quantifying differences between wakefulness states and cell populations. Wakefulness state and cell type show widespread effects on canonical network connectivity with variable frequency band dependence. Differences between excitatory neurons and inhibitory interneurons are observed, with somatostatin expressing inhibitory interneurons emerging as notably dissimilar from parvalbumin and vasoactive polypeptide expressing cells. In sum, we demonstrate that our pipeline can be used to examine brain state and cell-type differences in mesoscale imaging data, aiding translational neuroscience efforts. In line with open science practices, we freely release the pipeline and data to encourage other efforts in the community.