Behavioral-state development and sleep-state differentiation during early ontogenesis

Reducing Noise in the NICU

BACKGROUND

In the neonatal intensive care unit (NICU), elevated noise negatively impacts the neurodevelopmental environment, interrupts sleep, and can affect brain development in neonates. The American Academy of Pediatrics recommends that noise levels in the NICU should not exceed 45 dB.

PURPOSE

The project aims were to: (1) decrease average noise level by 10% from baseline and (2) decrease exposure to severe noise (>65 dB) to <5% of the time.

METHODS

This quality improvement project was conducted during 2021-2022 as a pre/post observational design in a Level IV NICU in New York City. We monitored sound levels for 20-24 h, 5 d/wk. Quality improvement interventions included: novel approaches to staff education, visual cues for when noise thresholds were exceeded, parent education, including access to personal decibel meters, technical improvements to vital sign monitors and entry doors, and defined quiet times (HUSH) for 2 h each 12-hour shift.

RESULTS

Education efforts and technical improvements successfully reduced median noise levels within the stepdown unit ( P < .001), though not in the acute care NICU. In contrast, the implementation of 2-hour periods of enforced "quiet time" every 12 h effectively reduced both median noise levels and the incidence of severe noise (>65 dB) in both locations.

IMPLICATIONS FOR PRACTICE AND RESEARCH

The HUSH strategy may be a sustainable way to decrease noise in the NICU. Future projects should prioritize education and dedicated quiet times to align with recommended standards, while research should explore the long-term developmental impacts of excessive noise levels on neonatal growth.

NAPping PAnts (NAPPA): An open wearable solution for monitoring Infant's sleeping rhythms, respiration and posture

Study objectives

To develop a non-invasive and practical wearable method for long-term tracking of infants' sleep.

Methods

An infant wearable, NAPping PAnts (NAPPA), was constructed by combining a diaper cover and a movement sensor (triaxial accelerometer and gyroscope), allowing either real-time data streaming to mobile devices or offline feature computation stored in the sensor memory. A sleep state classifier (wake, N1/REM, N2/N3) was trained and tested for NAPPA recordings (N = 16649 epochs of 30 s), using hypnograms from co-registered polysomnography (PSG) as a training target in 33 infants (age 2 weeks to 18 months; Mean = 4). User experience was assessed from an additional group of 16 parents.

Results

Overnight NAPPA recordings were successfully performed in all infants. The sleep state classifier showed good overall accuracy (78 %; Range 74-83 %) when using a combination of five features related to movement and respiration. Sleep depth trends were generated from the classifier outputs to visualise sleep state fluctuations, which closely aligned with PSG-derived hypnograms in all infants. Consistently positive parental feedback affirmed the effectiveness of the NAPPA-design.

Conclusions

NAPPA offers a practical and feasible method for out-of-hospital assessment of infants' sleep behaviour. It can directly support large-scale quantitative studies and development of new paradigms in scientific research and infant healthcare. Moreover, NAPPA provides accurate and informative computational measures for body positions, respiration rates, and activity levels, each with their respective clinical and behavioural value.

Sleep as a driver of pre- and postnatal brain development

In 1966, Howard Roffwarg proposed the ontogenic sleep hypothesis, relating neural plasticity and development to rapid eye movement (REM) sleep, a hypothesis that current fetal and neonatal sleep research is still exploring. Recently, technological advances have enabled researchers to automatically quantify neonatal sleep architecture, which has caused a resurgence of research in this field as attempts are made to further elucidate the important role of sleep in pre- and postnatal brain development. This article will review our current understanding of the role of sleep as a driver of brain development and identify possible areas for future research. IMPACT: The evidence to date suggests that Roffwarg's ontogenesis hypothesis of sleep and brain development is correct. A better understanding of the relationship between sleep and the development of functional connectivity is needed. Reliable, non-invasive tools to assess sleep in the NICU and at home need to be tested in a real-world environment and the best way to promote healthy sleep needs to be understood before clinical trials promoting and optimizing sleep quality in neonates could be undertaken.

Melatonin in Human Breast Milk and Its Potential Role in Circadian Entrainment: A Nod towards Chrononutrition?

Breastfeeding is the most appropriate source of a newborn's nutrition; among the plethora of its benefits, its modulation of circadian rhythmicity with melatonin as a potential neuroendocrine transducer has gained increasing interest. Transplacental transfer assures melatonin provision for the fetus, who is devoid of melatonin secretion. Even after birth, the neonatal pineal gland is not able to produce melatonin rhythmically for several months (with an even more prolonged deficiency following preterm birth). In this context, human breast milk constitutes the main natural source of melatonin: diurnal dynamic changes, an acrophase early after midnight, and changes in melatonin concentrations according to gestational age and during the different stages of lactation have been reported. Understudied thus far are the factors impacting on (changes in) melatonin content in human breast milk and their clinical significance in chronobiological adherence in the neonate: maternal as well as environmental aspects have to be investigated in more detail to guide nursing mothers in optimal feeding schedules which probably means a synchronized instead of mistimed feeding practice. This review aims to be thought-provoking regarding the critical role of melatonin in chrononutrition during breastfeeding, highlighting its potential in circadian entrainment and therefore optimizing (neuro)developmental outcomes in the neonatal setting.

Detection of regularities in auditory sequences before and at term-age in human neonates

During the last trimester of gestation, fetuses and preterm neonates begin to respond to sensory stimulation and to discover the structure of their environment. Yet, neuronal migration is still ongoing. This late migration notably concerns the supra-granular layers neurons, which are believed to play a critical role in encoding predictions and detecting regularities. In order to gain a deeper understanding of how the brain processes and perceives regularities during this stage of development, we conducted a study in which we recorded event-related potentials (ERP) in 31-wGA preterm and full-term neonates exposed to alternating auditory sequences (e.g. "ba ga ba ga ba"), when the regularity of these sequences was violated by a repetition (e.g., ``ba ga ba ga ga''). We compared the ERPs in this case to those obtained when violating a simple repetition pattern ("ga ga ga ga ga" vs. "ga ga ga ga ba"). Our results indicated that both preterm and full-term neonates were able to detect violations of regularity in both types of sequences, indicating that as early as 31 weeks gestational age, human neonates are sensitive to the conditional statistics between successive auditory elements. Full-term neonates showed an early and similar mismatch response (MMR) in the repetition and alternating sequences. In contrast, 31-wGA neonates exhibited a two-component MMR. The first component which was only observed for simple sequences with repetition, corresponded to sensory adaptation. It was followed much later by a deviance-detection component that was observed for both alternation and repetition sequences. This pattern confirms that MMRs detected at the scalp may correspond to a dual cortical process and shows that deviance detection computed by higher-level regions accelerates dramatically with brain maturation during the last weeks of gestation to become indistinguishable from bottom-up sensory adaptation at term.

Influence of radiofrequency electromagnetic fields exposure on sleep patterns in preterm neonates

PURPOSE

The study objective was to assess the influence of radiofrequency electromagnetic fields (RF-EMF) exposure on sleep patterns in preterm newborns. We hypothesized that an increase in RF-EMF exposure levels would alter infants' sleep structure parameters.

MATERIALS AND METHODS

Individual, continuous measurements of RF-EMF levels were performed in 29 hospitalized preterm newborns throughout the first 21 days after birth. The last day, overnight sleep structure was recorded by polysomnography. Relationships between both chronic (three-week period) and acute (polysomnographic period) RF-EMF levels with sleep parameters were computed.

RESULTS

At median levels, the main chronic effect was an increase in indeterminate sleep with RF-EMF exposure. At the highest exposure levels found in our study, an increase in RF-EMF levels increased sleep fragmentation. No significant relationship was found between acute RF-EMF levels and sleep parameters.

CONCLUSIONS

Despite no consolidated disruption in sleep structure, this study is the first to show that some sleep parameters seem to have a certain sensitivity to chronic - but not acute - RF-EMF exposure in preterm newborns. Further studies are needed to confirm our results and examine possible mid- to long-term, sleep-related cardiorespiratory and neurodevelopmental outcomes.

Combining Cardiorespiratory Signals and Video-Based Actigraphy for Classifying Preterm Infant Sleep States

The classification of sleep state in preterm infants, particularly in distinguishing between active sleep (AS) and quiet sleep (QS), has been investigated using cardiorespiratory information such as electrocardiography (ECG) and respiratory signals. However, accurately differentiating between AS and wake remains challenging; therefore, there is a pressing need to include additional information to further enhance the classification performance. To address the challenge, this study explores the effectiveness of incorporating video-based actigraphy analysis alongside cardiorespiratory signals for classifying the sleep states of preterm infants. The study enrolled eight preterm infants, and a total of 91 features were extracted from ECG, respiratory signals, and video-based actigraphy. By employing an extremely randomized trees (ET) algorithm and leave-one-subject-out cross-validation, a kappa score of 0.33 was achieved for the classification of AS, QS, and wake using cardiorespiratory features only. The kappa score significantly improved to 0.39 when incorporating eight video-based actigraphy features. Furthermore, the classification performance of AS and wake also improved, showing a kappa score increase of 0.21. These suggest that combining video-based actigraphy with cardiorespiratory signals can potentially enhance the performance of sleep-state classification in preterm infants. In addition, we highlighted the distinct strengths and limitations of video-based actigraphy and cardiorespiratory data in classifying specific sleep states.

Neonatal cortical activity organizes into transient network states that are affected by vigilance states and brain injury

Early neurodevelopment is critically dependent on the structure and dynamics of spontaneous neuronal activity; however, the natural organization of newborn cortical networks is poorly understood. Recent adult studies suggest that spontaneous cortical activity exhibits discrete network states with physiological correlates. Here, we studied newborn cortical activity during sleep using hidden Markov modeling to determine the presence of such discrete neonatal cortical states (NCS) in 107 newborn infants, with 47 of them presenting with a perinatal brain injury. Our results show that neonatal cortical activity organizes into four discrete NCSs that are present in both cardinal sleep states of a newborn infant, active and quiet sleep, respectively. These NCSs exhibit state-specific spectral and functional network characteristics. The sleep states exhibit different NCS dynamics, with quiet sleep presenting higher fronto-temporal activity and a stronger brain-wide neuronal coupling. Brain injury was associated with prolonged lifetimes of the transient NCSs, suggesting lowered dynamics, or flexibility, in the cortical networks. Taken together, the findings suggest that spontaneously occurring transient network states are already present at birth, with significant physiological and pathological correlates; this NCS analysis framework can be fully automatized, and it holds promise for offering an objective, global level measure of early brain function for benchmarking neurodevelopmental or clinical research.

The Relationship Between Sleep, Epilepsy, and Development: a Review

PURPOSE OF REVIEW

To review the relationship between sleep, neurodevelopment, and epilepsy and potential underlying physiological mechanisms.

RECENT FINDINGS

Recent studies have advanced our understanding of the role of sleep in early brain development and epilepsy. Epileptogenesis has been proposed to occur when there is a failure of normal adaptive processes of synaptic and homeostatic plasticity. This sleep-dependent transformation may explain the cognitive impairment seen in epilepsy, especially when occurring early in life. The glymphatic system, a recently discovered waste clearance system of the central nervous system, has been described as a potential mechanism underlying the relationship between sleep and seizures and may account for the common association between sleep deprivation and increased seizure risk. Epilepsy and associated sleep disturbances can critically affect brain development and neurocognition. Here we highlight recent findings on this topic and emphasize the importance of screening for sleep concerns in people with epilepsy.

Sleep state organisation of moderate to late preterm infants in the neonatal unit

BACKGROUND

Sleep supports neurodevelopment and sleep architecture reflects brain maturation. This prospective observational study describes the nocturnal sleep architecture of healthy moderate to late preterm (MLP) infants in the neonatal unit at 36 weeks post menstrual age (PMA).

METHODS

MLP infants, in the neonatal unit of a tertiary hospital in Ireland from 2017 to 2018, had overnight continuous electroencephalography (cEEG) with video for a minimum 12 h at 36 weeks PMA. The total sleep time (TST) including periods of active sleep (AS), quiet sleep (QS), indeterminate sleep (IS), wakefulness and feeding were identified, annotated and quantified.

RESULTS

A total of 98 infants had cEEG with video monitoring suitable for analysis. The median (IQR) of TST in the 12 h period was 7.09 h (IQR 6.61-7.76 h), 4.58 h (3.69-5.09 h) in AS, 2.02 h (1.76-2.36 h) in QS and 0.65 h (0.48-0.89 h) in IS. The total duration of AS was significantly lower in infants born at lower GA (p = 0.007) whilst the duration of individual QS periods was significantly higher (p = 0.001).

CONCLUSION

Overnight cEEG with video at 36 weeks PMA showed that sleep state architecture is dependent on birth GA. Infants with a lower birth GA have less AS and more QS that may have implications for later neurodevelopment.

IMPACT

EEG provides objective information about the sleep organisation of the moderate to late preterm (MLP) infant. Quantitative changes in sleep states occur with each week of advancing gestational age (GA). Active sleep (AS) is the dominant sleep state that was significantly lower in infants born at lower GA. MLP infants who were exclusively fed orally had a shorter total sleep time and less AS compared to infants who were fed via nasogastric tube.

Examining time-frequency mechanisms of full-fledged deep sleep development in newborns of different gestational age in the first days of their postnatal development

Early age-related changes in EEG time-frequency characteristics during the restful sleep of newborns of different gestational ages result in the development of conventional EEG signs of deep sleep already during the first postnatal week of their life. Allocating newborns to different groups based on their gestational age and duration of postnatal period allowed demonstrating substantial intergroup differences in brain activity during sleep and wakefulness, along with significant variability in the time-frequency characteristics of brain activity. The process of conventional deep sleep development in infants born prior to the week 35 of gestation is associated with an increase in the power of alpha activity in the sensorimotor cortex of the brain.

Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels

OBJECTIVE

To develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units.

METHODS

A deep learning-based algorithm was designed and trained using 53 EEG recordings from a long-term (a)EEG monitoring in 30 near-term neonates. The results were validated using an independent dataset from 30 polysomnography recordings. In addition, we constructed Sleep State Trend (SST), a bedside-ready means for visualizing classifier outputs.

RESULTS

The accuracy of quiet sleep detection in the training data was 90%, and the accuracy was comparable (85-86 %) in all bipolar derivations available from the 4-electrode recordings. The algorithm generalized well to a polysomnography dataset, showing 81% overall accuracy despite different signal derivations. SST allowed an intuitive, clear visualization of the classifier output.

CONCLUSIONS

Fluctuations in sleep states can be detected at high fidelity from a single EEG channel, and the results can be visualized as a transparent and intuitive trend in the bedside monitors.

SIGNIFICANCE

The Sleep State Trend (SST) may provide caregivers and clinical studies a real-time view of sleep state fluctuations and its cyclicity.

Sleep architecture in neonatal and infantile onset epilepsies in the first six months of life: A scoping review

AIM

Epilepsy occurs in approximately 80 per 100,000 infants in the first year of life, ranging in severity from self-limited and likely to spontaneously resolve, to severe developmental and epileptic encephalopathies. Sleep plays a key role in early brain development and the reciprocal relationship between sleep and seizures is not yet fully understood, particularly in young children. We conducted a Scoping Review to synthesise current knowledge of sleep architecture in neonates and infants with epilepsy.

METHOD

Peer-reviewed publications from 2005 to 2022 describing sleep architecture in infants up to six months of age with unprovoked seizures were included. The analysis set was derived from EMBASE, Web of Science and PubMED using key terms "sleep, epilepsy and infant" and related descriptors. Inclusion criteria were prospectively described in a Scoping Review protocol. Sleep architecture was assessed as macro- and micro-structural elements.

RESULTS

21 publications were included in the qualitative analysis. In self-limited familial and genetic epilepsy, sleep macrostructure was generally preserved. In DEEs and in epileptic encephalopathies of genetic or structural aetiology, sleep architecture was significantly disrupted.

INTERPRETATION

Early identification of infants with epilepsy is important to ensure early and effective treatment. In the DEE spectrum, sleep architecture is significantly impacted, and abnormal sleep architecture may be associated with compromised developmental outcome. Further research is needed to identify the sequence of events in abnormal brain development, epilepsy and sleep disruption and potentially help to predict the course of epilepsy towards a self-limited epilepsy versus a DEE.

CARFS7: A guide and proforma for reading a preterm neonate's EEG

OBJECTIVES

The important role of the EEG in preterm and term babies in investigating brain function and seizures, predicting outcomes, evaluating therapeutic interventions and decision-making is being increasingly acknowledged. Development of the brain in the last trimester of pregnancy results in rapid changes in the EEG patterns in this period. Acquiring and interpreting the EEG of a preterm baby can be challenging. The aim of this study was to develop a proforma titled CARFS⁷ (Continuity, Amplitude, Reactivity, Frequency, Synchrony, Symmetry, Sleep, Sharps, Shapes, Size and Seizures) to enable neurologists to read EEGs of premature babies with greater confidence, ease and accuracy and produce a report more easily repeatable and homogenous among operators.

METHODS

The CARFS⁷proforma was developed based on a literature review and the personal experience of the authors. The parameters of the EEG evaluated and scored in the proforma are Continuity, Amplitude, Reactivity/Variability, Frequency, Synchrony, Symmetry, Sleep, Sharps, Shapes/Patterns, Size and Seizures. We also assessed the interrater reliability of the proposed scoring system incorporated in the proforma.

RESULTS

CARFS⁷ proforma incorporates a number of parameters that help evaluate the preterm EEG. The interrater reliability of the proposed scoring system in the CARFS⁷proforma was high.

CONCLUSIONS

CARFS⁷ is a user friendly proforma for reading EEGs in the preterm infant. Interrater reliability using Cohen's k shows high agreement between two child neurologists who independently rated the EEGs of 25 premature babies using this proforma. CARFS⁷ has the potential to provide, accurate, reproducible and valuable information on brain function in the preterm infant in clinical practice.

The Impact of Preterm Birth on Sleep through Infancy, Childhood and Adolescence and Its Implications

There is emergent literature on the relationship between the development of sleep-wake cycles, sleep architecture, and sleep duration during the neonatal period on neurodevelopmental outcomes among children born preterm. There is also a growing literature on techniques to assess sleep staging in preterm neonates using either EEG methods or heart and respiration rate. Upon discharge from hospital, sleep in children born preterm has been assessed using parent report, actigraphy, and polysomnography. This review describes the ontogeny and measurement of sleep in the neonatal period, the current evidence on the impact of preterm birth on sleep both in the NICU and in childhood and adolescence, and the interaction between sleep, cognition, and social-emotional outcomes in this population.

Sleep and brain evolution across the human lifespan: A mutual embrace

Sleep can be considered a window to ascertain brain wellness: it dynamically changes with brain maturation and can even indicate the occurrence of concealed pathological processes. Starting from prenatal life, brain and sleep undergo an impressive developmental journey that accompanies human life throughout all its steps. A complex mutual influence rules this fascinating course and cannot be ignored while analysing its evolution. Basic knowledge on the significance and evolution of brain and sleep ontogenesis can improve the clinical understanding of patient's wellbeing in a more holistic perspective. In this review we summarized the main notions on the intermingled relationship between sleep and brain evolutionary processes across human lifespan, with a focus on sleep microstructure dynamics.

The impact of perinatal inflammation on the electroencephalogram in preterm infants: a systematic review

BACKGROUND

To summarise the association between perinatal inflammation (PI) exposure and electroencephalography (EEG) features in preterm infants.

METHODS

This systematic review included clinical studies of preterm infants born <37 weeks of gestational age (GA), who had both a PI exposure and an EEG assessment performed during the neonatal period. Studies were identified from Medline and Embase databases on the 15th of September 2021. PI was defined by histological chorioamnionitis, clinical chorioamnionitis, or early-onset neonatal infection (EONI). The risk of bias in included studies was assessed using the Joanna Briggs Institute (JBI) appraisal tool. A narrative approach was used to synthesise results. This review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 statement.

RESULTS

Two cross-sectional studies enrolling 130 preterm children born <32 weeks of GA assessed with one-channel amplitude-integrated EEG (aEEG) during the first four days of life were included. A PI exposure was described in 39 (30%) infants and was associated with a decrease in amplitude and a reduced incidence of sleep-wake cycling patterns.

CONCLUSION

These results should be interpreted with caution because of the small number of included studies and their heterogeneity. Further clinical studies evaluating the association of PI with EEG findings are needed.

IMPACT

A method to assess developmental trajectories following perinatal inflammation is required. Insufficient data exist to determine EEG features associated with perinatal inflammation. Further clinical studies evaluating this association are needed.

Heart rate variability in neonatal seizures: Investigation and implications for management

Many factors acting during the neonatal period can affect neurological development of the infant. Neonatal seizures (NS) that frequently occur in the immature brain may influence autonomic maturation and lead to detectable cardiovascular signs. These autonomic manifestations can also have significant diagnostic and prognostic value. The analysis of Heart Rate Variability (HRV) represents the most used and feasible method to evaluate cardiac autonomic regulation. This narrative review summarizes studies investigating HRV dynamics in newborns with seizures, with the aim of highlighting the potential utility of HRV measures for seizure detection and management. While HRV analysis in critically ill newborns is influenced by many potential confounders, we suggest that it can enhance the ability to better diagnose seizures in the clinical setting. We present potential applications of the analysis of HRV, which could have a useful future role, beyond the research setting.

Back to basics: the neuronal substrates and mechanisms that underlie the electroencephalogram in premature neonates

Electroencephalography is the only clinically available technique that can address the premature neonate normal and pathological functional development week after week. The changes in the electroencephalogram (EEG) result from gradual structural and functional modifications that arise during the last trimester of pregnancy. Here, we review the structural changes over time that underlie the establishment of functional immature neural networks, the impact of certain anatomical specificities (fontanelles, connectivity, etc.) on the EEG, limitations in EEG interpretation, and the utility of high-resolution EEG (HR-EEG) in premature newborns (a promising technique with a high degree of spatiotemporal resolution). In particular, we classify EEG features according to whether they are manifestations of endogenous generators (i.e. theta activities that coalesce with a slow wave or delta brushes) or come from a broader network. Furthermore, we review publications on EEG in premature animals because the data provide a better understanding of what is happening in premature newborns. We then discuss the results and limitations of functional connectivity analyses in premature newborns. Lastly, we report on the magnetoelectroencephalographic studies of brain activity in the fetus. A better understanding of complex interactions at various structural and functional levels during normal neurodevelopment (as assessed using electroencephalography as a benchmark method) might lead to better clinical care and monitoring for premature neonates.