Sleep-wake Cycling Research Articles

Altered sleep and inflammation are related to outcomes in neonatal encephalopathy.

Immune dysregulation and delayed onset of sleep wake cycling (SWC) are associated with worse outcome in neonatal encephalopathy (NE), however the association between sleep and immune dysfunction in NE remains unclear. Aimed to evaluate association of sleep and systemic inflammation with outcomes in NE. Amplitude-integrated electroencephalography (aEEG) recordings were collected on infants undergoing therapeutic hypothermia (TH). Duration to onset of (SWC) and sleep quality (SQ) were examined. Blood samples collected during the first 2 days of life. Thirteen pro- and anti-inflammatory serum cytokines were quantified. Adverse outcome defined as death or abnormal MRI brain. Earlier onset of SWC and better SQ had less adverse outcomes. SQ provided better prognostic value and showed better interobserver agreement compared to duration to SWC. Better SQ associated with lower cytokines EPO and interleukin (IL)-1β. In infants with unfavourable outcome, shorter duration to SWC was associated with higher EPO and better SQ was associated with lower TNF-α. Earlier onset of SWC or better SQ showed less systemic inflammation and fewer adverse outcomes. SQ during TH provided better prognostic information than time of onset of SWC. Modulation of circadian rhythm in infants with NE may have an immunomodulatory role, leading to improved outcomes.

Cooling and physiology during parent cuddling infants with neonatal encephalopathy in usual care: CoolCuddle-2 study.

CoolCuddle, enabling parents to cuddle their babies with neonatal encephalopathy (NE) during therapeutic hypothermia and intensive care (TH), was developed in research settings. To determine the impact of implementing CoolCuddle in usual care in six diverse neonatal intensive care units on the cooling process and intensive care. This vital sign cohort study embedded within the CoolCuddle implementation study enrolled 36 infants receiving TH for NE. Nurses received training on CoolCuddle and a standard operating procedure using an instruction video. After consenting, parents experienced up to 2 h of CoolCuddle with 30 min of pre- and post-cuddle observation. We used multilevel, clustered linear modelling to assess the physiological stability in temperature, cardio-respiratory and neurophysiology across the CoolCuddle. In 60 CoolCuddles over 93.12 h, respiratory parameters, heart rate or neurological function did not vary between the epochs (p > 0.05). During cuddle, sleep-wake cycling on amplitude-integrated EEG increased (p = 0.008) and there was weak evidence of lower pain scores (p = 0.08). No adverse effects were observed. Implementing CoolCuddle with support in usual practice maintained physiological stability and did not significantly affect the cooling process or intensive care, and may improve infant comfort. Ongoing monitoring of adverse effects when implementing CoolCuddle is recommended.

Prognostic Indicators of Reorientation of Care in Perinatal Hypoxic-Ischemic Encephalopathy Spectrum

ObjectiveTo investigate the clinical, electrographic, and neuroimaging characteristics in neonates with perinatal hypoxic-ischemic encephalopathy (HIE) who undergo reorientation of care (ROC) using standardized scoring systems. Study designA nested observational substudy within a prospective HIE cohort was conducted. Group 1 comprised infants whose parents received the medical recommendation for ROC, while Group 2 continued to receive standard care. Encephalopathy scores were monitored daily. Amplitude-integrated and continuous-video-integrated electroencephalogram (EEG) during therapeutic hypothermia were analyzed. Standardized scoring systems for cranial ultrasonography and post-rewarming brain MRI were deployed. ResultsThe study included 165 infants, with 35 in Group 1 and 130 in Group 2. By day 3, all infants in Group 1 were encephalopathic with higher Thompson scores (median 13 [IQR 10-19] vs. 0 [IQR 0-3], p<0.001). Electrographic background normalization within 48 hours occurred in 3% of Group 1 compared with 46% of Group 2 (p<0.001). Sleep-wake cycling was not observed in Group 1 and emerged in 63% of Group 2 within the first 72 hours (p<0.001). The number of anti-seizure medications received was higher in Group 1 (median 3 [IQR, 2-4] vs. 0 [IQR, 0-1], respectively; p< 0.001). Group 1 had higher cranial ultrasound injury scores (median 4 [IQR 2-7] vs. 1 [IQR 0-1], p<0.001) within 48 hours and post-rewarming brain MRI injury scores (median 33 [range 20-51] vs. 4 [range 0-28], p<0.001). ConclusionNeonates with perinatal HIE who underwent ROC presented with and maintained significantly more pronounced clinical manifestations, electrographic findings, and near-total brain injury as scored objectively on all modalities.

Remote Monitoring for Seizures During Therapeutic Hypothermia in Neonates With Hypoxic-Ischemic Encephalopathy

Neonates with hypoxic-ischemic encephalopathy (HIE) undergoing therapeutic hypothermia (TH) frequently experience seizures, which are associated with adverse outcomes. Efforts to rapidly identify seizures and reduce seizure burden may positively change neurologic and neurodevelopmental outcomes. To describe the onset, treatment, and evolution of seizures in a large cohort of newborns with HIE during TH assisted by a telehealth model and remote neuromonitoring approach. This was a prospective, observational, multicenter cohort study performed between July 2017 and December 2021 in 32 hospitals in Brazil. Participants were newborns with HIE meeting eligibility criteria and receiving TH. Data were analyzed from November 2022 to April 2023. Infants with HIE receiving TH were remotely monitored with 3-channel amplitude-integrated electroencephalography (aEEG) including raw tracing and video imaging, and bedside clinicians received assistance from trained neonatologists and neurologists. Data on modified Sarnat examination, presence, timing and seizure type, aEEG background activity, sleep-wake cycling, and antiepileptic drugs used were collected. Descriptive statistical analysis was used with independent t test, χ2, Mann-Whitney test, and post hoc analyses applied for associations. A total of 872 cooled newborns were enrolled; the median (IQR) gestational age was 39 (38-40) weeks, 518 (59.4%) were male, and 59 (6.8%) were classified as having mild encephalopathy by modified Sarnat examination, 504 (57.8%) as moderate, and 180 (20.6%) as severe. Electrographic seizures were identified in 296 newborns (33.9%), being only electrographic in 213 (71.9%) and clinical followed by electroclinical uncoupling in 50 (16.9%). Early abnormal background activity had a significant association with seizures. Infants with flat trace had the highest rate of seizures (58 infants [68.2%]) and the greatest association with the incidence of seizures (odds ratio [OR], 12.90; 95% CI, 7.57-22.22) compared with continuous normal voltage. The absence of sleep-wake cycling was also associated with a higher occurrence of seizures (OR, 2.22; 95% CI, 1.67-2.96). Seizure onset was most frequent between 6 and 24 hours of life (181 infants [61.1%]); however, seizure occurred in 34 infants (11.5%) during rewarming. A single antiepileptic drug controlled seizures in 192 infants (64.9%). The first line antiepileptic drug was phenobarbital in 294 (99.3%). In this cohort study of newborns with HIE treated with TH, electrographic seizure activity occurred in 296 infants (33.9%) and was predominantly electrographic. Seizure control was obtained with a single antiepileptic drug in 192 infants (64.9%). These findings suggest neonatal neurocritical care can be delivered at remote limited resource hospitals due to innovations in technology and telehealth.

Distinctive Amplitude-Integrated EEG Ictal Pattern and Targeted Therapy with Carbamazepine in KCNQ2 and KCNQ3 Neonatal Epilepsy: A Case Series.

Carbamazepine (CBZ) is effective in treating KCNQ2/3-related seizures, which may present with a distinctive amplitude-integrated electroencephalography (aEEG) pattern. To assess how improved recognition of the distinctive aEEG ictal pattern associated with KCNQ2/3 variants has enabled early and effective targeted therapy with CBZ. Retrospective descriptive study of five neonates with KCNQ2/3 pathogenic gene variants admitted at a level 3 neonatal intensive care unit (NICU) over an 8-year period. The distinctive ictal aEEG pattern was recognized in four neonates after an average of 61.5 hours (minimum 12 hours, maximum 120 hours) from the first electroclinical seizure and prompted the use of CBZ that was effective in all. The two most recently diagnosed patients could avoid polytherapy as they received CBZ as the first and second antiseizure medication, respectively. Three out of five patients with continuous normal voltage (CNV), sleep-wake cycling (SWC), and shorter postictal suppression had normal neurodevelopmental outcome. Regarding the remaining two infants, one was not trialed with CBZ and had a high seizure burden, both presented with a prolonged postictal suppression, no SWC, and had moderate-to-severe developmental delay. Genetic results became available after the neonatal period in all but one of the infants, who had a prenatal diagnosis. Recognition of the distinctive ictal aEEG pattern in the NICU allowed early and effective targeted therapy with CBZ in four neonates, well before genetic results became available. Furthermore, a CNV background pattern with SWC and short postictal suppression were associated with normal developmental outcomes.

Early qualitative and quantitative amplitude-integrated electroencephalogram and raw electroencephalogram for predicting long-term neurodevelopmental outcomes in extremely preterm infants in the Netherlands: a 10-year cohort study

Extremely preterm infants (<28 weeks of gestation) are at great risk of long-term neurodevelopmental impairments. Early amplitude-integrated electroencephalogram (aEEG) accompanied by raw EEG traces (aEEG-EEG) has potential for predicting subsequent outcomes in preterm infants. We aimed to determine whether and which qualitative and quantitative aEEG-EEG features obtained within the first postnatal days predict neurodevelopmental outcomes in extremely preterm infants. This study retrospectively analysed a cohort of extremely preterm infants (born before 28 weeks and 0 days of gestation) who underwent continuous two-channel aEEG-EEG monitoring during their first 3 postnatal days at Wilhelmina Children's Hospital, Utrecht, the Netherlands, between June 1, 2008, and Sept 30, 2018. Only infants who did not have genetic or metabolic diseases or major congenital malformations were eligible for inclusion. Features were extracted from preprocessed aEEG-EEG signals, comprising qualitative parameters grouped in three types (background pattern, sleep-wake cycling, and seizure activity) and quantitative metrics grouped in four categories (spectral content, amplitude, connectivity, and discontinuity). Machine learning-based regression and classification models were used to evaluate the predictive value of the extracted aEEG-EEG features for 13 outcomes, including cognitive, motor, and behavioural problem outcomes, at 2-3 years and 5-7 years. Potential confounders (gestational age at birth, maternal education, illness severity, morphine cumulative dose, the presence of severe brain injury, and the administration of antiseizure, sedative, or anaesthetic medications) were controlled for in all prediction analyses. 369 infants were included and an extensive set of 339 aEEG-EEG features was extracted, comprising nine qualitative parameters and 330 quantitative metrics. The machine learning-based regression models showed significant but relatively weak predictive performance (ranging from r=0·13 to r=0·23) for nine of 13 outcomes. However, the machine learning-based classifiers exhibited acceptable performance in identifying infants with intellectual impairments from those with optimal outcomes at age 5-7 years, achieving balanced accuracies of 0·77 (95% CI 0·62-0·90; p=0·0020) for full-scale intelligence quotient score and 0·81 (0·65-0·96; p=0·0010) for verbal intelligence quotient score. Both classifiers maintained identical performance when solely using quantitative features, achieving balanced accuracies of 0·77 (95% CI 0·63-0·91; p=0·0030) for full-scale intelligence quotient score and 0·81 (0·65-0·96; p=0·0010) for verbal intelligence quotient score. These findings highlight the potential benefits of using early postnatal aEEG-EEG features to automatically recognise extremely preterm infants with poor outcomes, facilitating the development of an interpretable prognostic tool that aids in decision making and therapy planning. European Commission Horizon 2020.

Amplitude-Integrated Electroencephalography Evolution and Magnetic Resonance Imaging Injury in Mild and Moderate to Severe Neonatal Encephalopathy.

This study aimed to describe the evolution of amplitude-integrated electroencephalography (aEEG) in neonatal encephalopathy (NE) during therapeutic hypothermia (TH) and evaluate the association between aEEG parameters and magnetic resonance imaging (MRI) injury. aEEG data of infants who underwent TH were reviewed for background, sleep wake cycling (SWC), and seizures. Conventional electroencephalography (cEEG) background was assessed from the reports. Discordance of background on aEEG and cEEG was defined if there was a difference in the severity of the background. MRI injury (total score ≥ 5) was assessed by using the Weeke scoring system. A total of 46 infants were included; 23 (50%) with mild NE and 23 (50%) with moderate to severe NE. Comparing mild NE with moderate to severe NE, the initial aEEG background differed with more mild being continuous (70 vs. 52%), with fewer being discontinuous (0 vs. 22%) and flat tracing (0 vs. 4%), whereas burst suppression (4 vs. 4%) and low voltage (26 vs. 18%) did not differ. There was a notably common discordance between the background assessment on cEEG with aEEG in 82% with continuous and 40% low voltage aEEG background. MRI abnormalities were identified in four infants with mild NE and seven infants with moderate to severe NE. MRI injury was associated with aEEG seizures in infants with moderate to severe NE. aEEG seizures are useful to predict MRI injury in moderate to severe NE infants. There is a large discrepancy between aEEG, cEEG, and MRI in neonates treated by TH. · MRI injury was identified in 29% of moderate NE infants and in 50% of severe NE infants.. · aEEG seizures were associated with MRI injury in the moderate to severe NE infants.. · MRI injury was identified in 16% infants with mild NE.. · Mild NE infants with normal aEEG were unlikely to have MRI injury.. · There was a large discrepancy between aEEG, cEEG, and MRI in infants treated by TH..

Neonatal EEG graded for severity of background abnormalities in hypoxic-ischaemic encephalopathy

This report describes a set of neonatal electroencephalogram (EEG) recordings graded according to the severity of abnormalities in the background pattern. The dataset consists of 169 hours of multichannel EEG from 53 neonates recorded in a neonatal intensive care unit. All neonates received a diagnosis of hypoxic-ischaemic encephalopathy (HIE), the most common cause of brain injury in full term infants. For each neonate, multiple 1-hour epochs of good quality EEG were selected and then graded for background abnormalities. The grading system assesses EEG attributes such as amplitude, continuity, sleep–wake cycling, symmetry and synchrony, and abnormal waveforms. Background severity was then categorised into 4 grades: normal or mildly abnormal EEG, moderately abnormal EEG, majorly abnormal EEG, and inactive EEG. The data can be used as a reference set of multi-channel EEG for neonates with HIE, for EEG training purposes, or for developing and evaluating automated grading algorithms.

Mitochondria Need Their Sleep: Redox, Bioenergetics, and Temperature Regulation of Circadian Rhythms and the Role of Cysteine-Mediated Redox Signaling, Uncoupling Proteins, and Substrate Cycles.

Although circadian biorhythms of mitochondria and cells are highly conserved and crucial for the well-being of complex animals, there is a paucity of studies on the reciprocal interactions between oxidative stress, redox modifications, metabolism, thermoregulation, and other major oscillatory physiological processes. To address this limitation, we hypothesize that circadian/ultradian interaction of the redoxome, bioenergetics, and temperature signaling strongly determine the differential activities of the sleep-wake cycling of mammalians and birds. Posttranslational modifications of proteins by reversible cysteine oxoforms, S-glutathionylation and S-nitrosylation are shown to play a major role in regulating mitochondrial reactive oxygen species production, protein activity, respiration, and metabolomics. Nuclear DNA repair and cellular protein synthesis are maximized during the wake phase, whereas the redoxome is restored and mitochondrial remodeling is maximized during sleep. Hence, our analysis reveals that wakefulness is more protective and restorative to the nucleus (nucleorestorative), whereas sleep is more protective and restorative to mitochondria (mitorestorative). The "redox-bioenergetics-temperature and differential mitochondrial-nuclear regulatory hypothesis" adds to the understanding of mitochondrial respiratory uncoupling, substrate cycling control and hibernation. Similarly, this hypothesis explains how the oscillatory redox-bioenergetics-temperature-regulated sleep-wake states, when perturbed by mitochondrial interactome disturbances, influence the pathogenesis of aging, cancer, spaceflight health effects, sudden infant death syndrome, and diseases of the metabolism and nervous system.

Neuromonitoring in neonatal critical care part II: extremely premature infants and critically ill neonates.

Neonatal intensive care has expanded from cardiorespiratory care to a holistic approach emphasizing brain health. To best understand and monitor brain function and physiology in the neonatal intensive care unit (NICU), the most commonly used tools are amplitude-integrated EEG, full multichannel continuous EEG, and near-infrared spectroscopy. Each of these modalities has unique characteristics and functions. While some of these tools have been the subject of expert consensus statements or guidelines, there is no overarching agreement on the optimal approach to neuromonitoring in the NICU. This work reviews current evidence to assist decision making for the best utilization of these neuromonitoring tools to promote neuroprotective care in extremely premature infants and in critically ill neonates. Neuromonitoring approaches in neonatal encephalopathy and neonates with possible seizures are discussed separately in the companion paper. IMPACT: For extremely premature infants, NIRS monitoring has a potential role in individualized brain-oriented care, and selective use of aEEG and cEEG can assist in seizure detection and prognostication. For critically ill neonates, NIRS can monitor cerebral perfusion, oxygen delivery, and extraction associated with disease processes as well as respiratory and hypodynamic management. Selective use of aEEG and cEEG is important in those with a high risk of seizures and brain injury. Continuous multimodal monitoring as well as monitoring of sleep, sleep-wake cycling, and autonomic nervous system have a promising role in neonatal neurocritical care.

An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation

Electroencephalogram (EEG) monitoring is recommended as routine in newborn neurocritical care to facilitate early therapeutic decisions and outcome predictions. EEG's larger-scale implementation is, however, hindered by the shortage of expertise needed for the interpretation of spontaneous cortical activity, the EEG background. We developed an automated algorithm that transforms EEG recordings to quantified interpretations of EEG background and provides simple intuitive visualisations in patient monitors. In this method-development and proof-of-concept study, we collected visually classified EEGs from infants recovering from birth asphyxia or stroke. We used unsupervised learning methods to explore latent EEG characteristics, which guided the supervised training of a deep learning-based classifier. We assessed the classifier performance using cross-validation and an external validation dataset. We constructed a novel measure of cortical function, brain state of the newborn (BSN), from the novel EEG background classifier and a previously published sleep-state classifier. We estimated clinical utility of the BSN by identification of two key items in newborn brain monitoring, the onset of continuous cortical activity and sleep-wake cycling, compared with the visual interpretation of the raw EEG signal and the amplitude-integrated (aEEG) trend. We collected 2561 h of EEG from 39 infants (gestational age 35·0-42·1 weeks; postnatal age 0-7 days). The external validation dataset included 105 h of EEG from 31 full-term infants. The overall accuracy of the EEG background classifier was 92% in the whole cohort (95% CI 91-96; range 85-100 for individual infants). BSN trend values were closely related to the onset of continuous EEG activity or sleep-wake cycling, and BSN levels showed robust difference between aEEG categories. The temporal evolution of the BSN trends showed early diverging trajectories in infants with severely abnormal outcomes. The BSN trend can be implemented in bedside patient monitors as an EEG interpretation that is intuitive, transparent, and clinically explainable. A quantitative trend measure of brain function might harmonise practices across medical centres, enable wider use of brain monitoring in neurocritical care, and might facilitate clinical intervention trials. European Training Networks Funding Scheme, the Academy of Finland, Finnish Pediatric Foundation (Lastentautiensäätiö), Aivosäätiö, Sigrid Juselius Foundation, HUS Children's Hospital, HUS Diagnostic Center, National Health and Medical Research Council of Australia.

WITHDRAWN: Mitochondria need their sleep: Sleep-wake cycling and the role of redox, bioenergetics, and temperature regulation, involving cysteine-mediated redox signaling, uncoupling proteins, and substrate cycles

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Outcome Prediction in Neonatal Hypoxic-Ischaemic Encephalopathy Using Neurophysiology and Neuroimaging

Objective: The aim of the study was to determine the predictive power of the combined use of neurophysiological (amplitude-integrated electroencephalography [aEEG], near-infrared spectroscopy [NIRS]) methods and neuroimaging (magnetic resonance imaging [MRI]) for long-term outcome prediction in neonates with hypoxic-ischaemic encephalopathy (HIE). Study Design: Prospective cohort study of 56 patients with moderate to severe HIE and hypothermia treatment at the Medical University of Vienna between 2008 and 2020. aEEG and NIRS were recorded continuously over a period of >4 days (102 h) starting at the initiation of hypothermia treatment, MRI was performed at a median age of 8 days. Receiver operating characteristic curves and area under the curve were calculated to evaluate the prognostic ability of aEEG, NIRS, and MRI parameters for outcome assessed via Bayley Scales of Infant Development 3rd edition at 2 years of age. Results: Combined aEEG and MRI parameters showed highest predictive power regarding long-term outcome. The highest area under the curve values (0.96–0.99) were obtained for aEEG (combination of background pattern and sleep-wake cycling) between 66 and 102 h after initiation of hypothermia in combination with MRI findings. NIRS parameters did not differ significantly between infants with favourable and adverse outcome. Conclusions: Combined aEEG and MRI parameter scores were more predictive than single parameter scores. No further improvement was observed when combining aEEG/MRI with NIRS data.

Inter-observer reliability for amplitude-integrated electroencephalography in the newborn with perinatal asphyxia

IntroductionAmplitude integrated electroencephalography (aEEG) is a tool widely used for neuromonitoring in the critical neonate. In the patient with perinatal asphyxia, its interpretation is key to identifying candidates for therapeutic hypothermia, detecting subclinical seizures and providing pronostic information. Our aim was to analyze the concordance in the interpretation of aEEG among neonatologists with different levels of experience. Material and methodsUnicenter retrospective study of newborns ≥ 35 weeks with perinatal asphyxia included consecutively over a two-year period and monitored with aEEG for at least 6 h. The bedside neonatologist interpreted aEEG regarding background pattern, sleep-wake cycling, and seizures. The aEEG tracings were blindly reviewed by two neonatologists with different experience. The aEEG tracings were divided into periods of 0–3 h and 3–6 h of life, and the concordance (Cohen Kappa coefficient, k), between the two examiners and that of their consensus with the bedside neonatologist, was analyzed. ResultsSeventy-five newborns were included, 5 of them were not aEEG-monitored. 132 tracings were analyzed with a very good concordance between the two examiners in the three characteristics of the aEEG. The k for the bedside neonatologist was very good for background pattern (k = 0.93), moderate (k = 0.52) for sleep-wake cycling, and weak (k = 0.32) for seizures. ConclusionsThis study supports that background pattern is easily interpreted compared to sleep-wake cycling or crisis, improving when targeted training on aEEG is received.

Longitudinal Analysis of Amplitude-Integrated Electroencephalography for Outcome Prediction in Hypoxic-Ischemic Encephalopathy

To investigate the prognostic accuracy of longitudinal analysis of amplitude-integrated electroencephalography (aEEG) background activity to predict long-term neurodevelopmental outcome in neonates with hypoxic-ischemic encephalopathy (HIE) receiving therapeutic hypothermia. This single-center observational study included 149 neonates for derivation and 55 neonates for validation with moderate-severe HIE and of gestational age ≥35weeks at a tertiary neonatal intensive care unit. Single-channel aEEG background pattern, sleep-wake cycling, and seizure activity were monitored over 84hours during therapeutic hypothermia and rewarming, then scored for each 6-hour interval. Neurodevelopmental outcome was assessed using the Bayley Scales of Infant Development, Second Edition. Favorable outcome was defined as having both a Mental Development Index (MDI) score and Psychomotor Development Index (PDI) score ≥70, and adverse outcome was defined as either an MDI or a PDI <70 or death. Regression modeling for longitudinal analysis of repeatedly measured data was applied, and area under the receiver operating characteristic curve (AUC) was calculated. Longitudinal aEEG background analysis combined with sleep-wake cycling score had excellent predictive value (AUC, 0.90; 95% CI, 0.85-0.95), better than single aEEG scores at any individual time point. The model performed well in the independent validation cohort (AUC, 0.87; 95% CI, 0.62-1.00). The reclassification rate of this model compared with the conventional analysis of aEEG background at 48hours was 18% (24 patients); 14% (18 patients) were reclassified correctly. Our results were used to develop a user-friendly online outcome prediction tool. Longitudinal analysis of aEEG background activity and sleep-wake cycling is a valuable and accurate prognostic tool.

A practical approach toward interpretation of amplitude integrated electroencephalography in preterm infants

This algorithm may be used by clinicians at the bedside for interpretation to integrate it in clinical practice for neurological surveillance of preterm infants. •Studies have reported normative data on aEEG in preterm infants for different gestational ages. • Burst suppression pattern and absent sleep-wake cycling have been described to be associated with brain pathology and adverse outcomes in preterm infants. •We have synthesized aEEG characteristics in preterm infants across the spectrum of prematurity reported in the literature. • We present a stepwise approach for clinically applicable interpretation of aEEG in preterm infants.

A Population-Based Iterated Greedy Algorithm for Maximizing Sensor Network Lifetime.

Finding dominating sets in graphs is very important in the context of numerous real-world applications, especially in the area of wireless sensor networks. This is because network lifetime in wireless sensor networks can be prolonged by assigning sensors to disjoint dominating node sets. The nodes of these sets are then used by a sleep–wake cycling mechanism in a sequential way; that is, at any moment in time, only the nodes from exactly one of these sets are switched on while the others are switched off. This paper presents a population-based iterated greedy algorithm for solving a weighted version of the maximum disjoint dominating sets problem for energy conservation purposes in wireless sensor networks. Our approach is compared to the ILP solver, CPLEX, which is an existing local search technique, and to our earlier greedy algorithm. This is performed through its application to 640 random graphs from the literature and to 300 newly generated random geometric graphs. The results show that our algorithm significantly outperforms the competitors.

Value of amplitude-integrated electroencephalogram combined with quantitative indices of cranial magnetic resonance imaging in predicting short-term neurodevelopment in moderately and late preterm infants: a prospective study.

To study the association of amplitude-integrated electroencephalogram (aEEG) and the quantitative indices biparietal width (BPW) and interhemispheric distance (IHD) of cranial magnetic resonance imaging (cMRI) with short-term neurodevelopment in moderately and late preterm infants. A total of 104 moderately and late preterm infants who were admitted to the neonatal intensive care unit from September 2018 to April 2020 were selected as the subjects for this prospective study. The Naqeeb method and sleep-wake cycling (SWC) were used for aEEG assessment within 72 hours after birth. cMRI was performed at the corrected gestational age of 37 weeks. BPW and IHD were measured at the T2 coronal position. At the corrected age of 6 months, the Developmental Screening Test for Child Under Six (DST) was used to follow up neurodevelopment. According to developmental quotient (DQ), the infants were divided into a normal DST group (78 infants with DQ≥85) and an abnormal DST group (26 infants with DQ<85). Related indices were compared between the two groups. The association between aEEG and cMRI was evaluated. Compared with the normal DST group, the abnormal DST group had significantly lower aEEG normal rate and SWC maturation rate (P<0.05), as well as a significantly larger IHD and a significantly smaller BPW (P<0.05). Immature SWC, aEEG abnormality, and a relatively large IHD were the risk factors for abnormal DST (P<0.05), and a relatively large BPW was a protective factor against abnormal DST (P<0.05). For moderately and late preterm infants, aEEG within 72 hours after birth and the quantitative indices BPW and IHD of cMRI at the corrected gestational age of 37 weeks may affect their neurodevelopmental outcome at the corrected age of 6 months.

A Greedy Heuristic for Maximizing the Lifetime of Wireless Sensor Networks Based on Disjoint Weighted Dominating Sets

Dominating sets are among the most well-studied concepts in graph theory, with many real-world applications especially in the area of wireless sensor networks. One way to increase network lifetime in wireless sensor networks consists of assigning sensors to disjoint dominating node sets, which are then sequentially used by a sleep–wake cycling mechanism. This paper presents a greedy heuristic for solving a weighted version of the maximum disjoint dominating sets problem for energy conservation purposes in wireless sensor networks. Moreover, an integer linear programming model is presented. Experimental results based on a large set of 640 problem instances show, first, that the integer linear programming model is only useful for small problem instances. Moreover, they show that our algorithm outperforms recent local search algorithms from the literature with respect to both solution quality and computation time.

Paradoxical anesthesia: Sleep-like EEG during anesthesia induced by mesopontine microinjection of GABAergic agents

General anesthetic agents are thought to induce loss-of-consciousness (LOC) and enable pain-free surgery by acting on the endogenous brain circuitry responsible for sleep-wake cycling. In clinical use, the entire CNS is exposed to anesthetic molecules with LOC and amnesia usually attributed to synaptic suppression in the cerebral cortex and immobility and analgesia to agent action in the spinal cord and brainstem. This model of patch-wise suppression has been challenged, however, by the observation that all functional components of anesthesia can be induced by focal delivery of minute quantities of GABAergic agonists to the brainstem mesopontine tegmental anesthesia area (MPTA). We compared spectral features of the cortical electroencephalogram (EEG) in rats during systemic anesthesia and anesthesia induced by MPTA microinjection. Systemic administration of (GABAergic) pentobarbital yielded the sustained, δ-band dominant EEG signature familiar in clinical anesthesia. In contrast, anesthesia induced by MPTA microinjection (pentobarbital or muscimol) featured epochs of δ-band EEG alternating with the wake-like EEG, the pattern typical of natural non-rapid-eye-movement (NREM) and REM sleep. The rats were not sleeping, however, as they remained immobile, atonic and unresponsive to noxious pinch. Recalling the paradoxical wake-like quality the EEG during REM sleep, we refer to this state as “paradoxical anesthesia”. GABAergic anesthetics appear to co-opt both cortical and spinal components of the sleep network via dedicated axonal pathways driven by MPTA neurons. Direct drug exposure of cortical and spinal neurons is not necessary, and is probably responsible for off-target side-effects of systemic administration including monotonous δ-band EEG, hypothermia and respiratory depression. Significance statementThe concept that GABAergic general anesthetic agents induce loss-of-consciousness by substituting for an endogenous neurotransmitter, thereby co-opting neural circuitry responsible for sleep-wake transitions, has gained considerable traction. However, the electroencephalographic (EEG) signatures of sleep and anesthesia differ fundamentally. We show that when the anesthetic state is generated by focal delivery of GABAergics into the mesopontine tegmental anesthesia area (MPTA) the resulting EEG repeatedly transitions between delta-wave-dominant and wake-like patterns much as in REM-NREM sleep. This suggests that systemic (clinical) anesthetic delivery, which indiscriminately floods the entire cerebrum with powerful inhibitory agents, obscures the sleep-like EEG signature associated with the less adulterated form of anesthesia obtained when the drugs are applied selectively to loci where the effective neurotransmitter substitution actually occurs.