Quantitative analysis of maturational changes in EEG background activity in very preterm infants with a normal neurodevelopment at 1 year of age

EEG effective connectivity during the first year of life mirrors brain synaptogenesis, myelination, and early right hemisphere predominance

INTRODUCTION

The maturation of electroencephalogram (EEG) effective connectivity in healthy infants during the first year of life is described.

METHODS

Participants: A cross-sectional sample of 125 healthy at-term infants, from 0 to 12 months of age, underwent EEG in a state of quiet sleep.

PROCEDURES

The EEG primary currents at the source were described with the sLoreta method. An unmixing algorithm was applied to reduce the leakage, and the isolated effective coherence, a direct and directed measurement of information flow, was calculated.

RESULTS AND DISCUSSION

Initially, the highest indices of connectivity are at the subcortical nuclei, continuing to the parietal lobe, predominantly the right hemisphere, then expanding to temporal, occipital, and finally the frontal areas, which is consistent with the myelination process. Age-related connectivity changes were mostly long-range and bilateral. Connections increased with age, mainly in the right hemisphere, while they mainly decreased in the left hemisphere. Increased connectivity from 20 to 30 Hz, mostly at the right hemisphere. These findings were consistent with right hemisphere predominance during the first three years of life. Theta and alpha connections showed the greatest changes with age. Strong connectivity was found between the parietal, temporal, and occipital regions to the frontal lobes, responsible for executive functions and consistent with behavioral development during the first year. The thalamus exchanges information bidirectionally with all cortical regions and frequency bands.

CONCLUSIONS

The maturation of EEG connectivity during the first year in healthy infants is very consistent with synaptogenesis, reductions in synaptogenesis, myelination, and functional and behavioral development.

Early aEEG can predict neurodevelopmental outcomes at 12 to 18 month of age in VLBWI with necrotizing enterocolitis: a cohort study

Background

Studies have shown that neurological damage is common in necrotizing enterocolitis (NEC) survivors. The purpose of the study was to investigate the predictive value of amplitude-integrated electroencephalogram (aEEG) for neurodevelopmental outcomes in preterm infants with NEC.

Methods

Infants with NEC were selected, and the control group was selected based on 1:1–2 pairing by gestational age. We performed single-channel (P3–P4) aEEG in the two groups. The Burdjalov scores were compared between the two groups. Cranial magnetic resonance imaging (MRI) was performed several months after birth. The neurological outcomes at 12 to 18 months of age were compared with the Gesell Developmental Schedules (GDS). The predictive value of aEEG scores for neurodevelopmental delay was calculated.

Results

There was good consistency between the two groups regarding general conditions. In the 1st aEEG examination, the patients in NEC group had lower Co (1.0 (0.0, 2.0) vs. 2.0 (2.0, 2.0), P = 0.001), Cy (1.0 (0.0, 2.0) vs. 3.0 (3.0, 4.0), P < 0.001), LB (1.0 (0.0, 2.0) vs. 2.0 (2.0, 2.0), P < 0.001), B (1.0 (1.0, 2.0) vs. 3.0 (3.0, 3.5), P < 0.001) and T (3.0 (2.0, 8.0) vs. 10.0 (10.0, 11.5), P < 0.001), than the control group. Cranial MRI in NEC group revealed a widened interparenchymal space with decreased myelination. The abnormality rate of cranial MRI in the NEC group was higher than that in the control group (P = 0.001). The GDS assessment indicated that NEC children had inferior performance and lower mean scores than the control group in the subdomains of gross motor (71 (SD = 6.41) vs. 92 (SD = 11.37), P < 0.001), fine motor (67 (SD = 9.34) vs. 96 (SD = 13.69), adaptive behavior (76 (SD = 9.85) vs. 95 (SD = 14.38), P = 0.001), language (68 (SD = 12.65) vs. 95 (SD = 11.41), P < 0.001), personal-social responses (80 (SD = 15.15) vs. 93(SD = 14.75), P = 0.037) and in overall DQ (72 (SD = 8.66) vs. 95 (SD = 11.07), P < 0.001). The logistic binary regression analysis revealed that the NEC patients had a significantly greater risk of neurodevelopmental delay than the control group (aOR = 27.00, 95% CI = 2.561–284.696, P = 0.006). Confirmed by Spearman’s rank correlation analysis, neurodevelopmental outcomes were significantly predicted by the 1st aEEG Burdjalov score (r = 0.603, P = 0.001). An abnormal 1st Burdjalov score has predictive value for neurodevelopmental delay with high specificity (84.62%) and positive predictive value (80.00%).

Conclusions

Children with NEC are more likely to develop neurodevelopmental delay. There is high specificity and PPV of early aEEG in predicting neurodevelopmental delay.

Normal EEG during the neonatal period: maturational aspects from premature to full-term newborns

Electroencephalography (EEG) is the reference tool for the analysis of brain function, reflecting normal and pathological neuronal network activity. During the neonatal period, EEG patterns evolve weekly, according to gestational age. The first analytical criteria for the various maturational stages and standardized neonatal EEG terminology were published by a group of French neurophysiologists training in Paris (France) in 1999. These criteria, defined from analog EEG, were completed in 2010 with digital EEG analysis. Since then, this work has continued, aided by the technical progress in EEG acquisition, the improvement of knowledge on the maturating processes of neuronal networks, and the evolution of critical care. In this review, we present an exhaustive and didactic overview of EEG characteristics from extremely premature to full-term infants. This update is based on the scientific literature, enhanced by the study of normal EEGs of extremely premature infants by our group of neurophysiologists. For educational purposes, particular attention has been paid to illustrations using new digital tools.

Measuring Cot-Side the Effects of Parenteral Nutrition on Preterm Cortical Function

Early nutritional compromise after preterm birth is shown to affect long-term neurodevelopment, however, there has been a lack of early functional measures of nutritional effects. Recent progress in computational electroencephalography (EEG) analysis has provided means to measure the early maturation of cortical activity. Our study aimed to explore whether computational metrics of early sequential EEG recordings could reflect early nutritional care measured by energy and macronutrient intake in the first week of life. A higher energy or macronutrient intake was assumed to associate with improved development of the cortical activity. We analyzed multichannel EEG recorded at 32 weeks (32.4 ± 0.7) and 36 weeks (36.6 ± 0.9) of postmenstrual age in a cohort of 28 preterm infants born before 32 weeks of postmenstrual age (range: 24.3–32 weeks). We computed several quantitative EEG measures from epochs of quiet sleep (QS): (i) spectral power; (ii) continuity; (iii) interhemispheric synchrony, as well as (iv) the recently developed estimate of maturational age. Parenteral nutritional intake from day 1 to day 7 was monitored and clinical factors collected. Lower calories and carbohydrates were found to correlate with a higher reduction of spectral amplitude in the delta band. Lower protein amount associated with higher discontinuity. Both higher proteins and lipids intake correlated with a more developmental increase in interhemispheric synchrony as well as with better progress in the estimate of EEG maturational age (EMA). Our study shows that early nutritional balance after preterm birth may influence subsequent maturation of brain activity in a way that can be observed with several intuitively reasoned and transparent computational EEG metrics. Such measures could become early functional biomarkers that hold promise for benchmarking in the future development of therapeutic interventions.

Temporal evolution of quantitative EEG within 3 days of birth in early preterm infants

For the premature newborn, little is known about changes in brain activity during transition to extra-uterine life. We aim to quantify these changes in relation to the longer-term maturation of the developing brain. We analysed EEG for up to 72 hours after birth from 28 infants born <32 weeks of gestation. These infants had favourable neurodevelopment at 2 years of age and were without significant neurological compromise at time of EEG monitoring. Quantitative EEG was generated using features representing EEG power, discontinuity, spectral distribution, and inter-hemispheric connectivity. We found rapid changes in cortical activity over the 3 days distinct from slower changes associated with gestational age: for many features, evolution over 1 day after birth is equivalent to approximately 1 to 2.5 weeks of maturation. Considerable changes in the EEG immediately after birth implies that postnatal adaption significantly influences cerebral activity for early preterm infants. Postnatal age, in addition to gestational age, should be considered when analysing preterm EEG within the first few days after birth.

Quantitative Preterm EEG Analysis: The Need for Caution in Using Modern Data Science Techniques

Hemodynamic changes during neonatal transition increase the vulnerability of the preterm brain to injury. Real-time monitoring of brain function during this period would help identify the immediate impact of these changes on the brain. Neonatal EEG provides detailed real-time information about newborn brain function but can be difficult to interpret for non-experts; preterm neonatal EEG poses even greater challenges. An objective quantitative measure of preterm brain health would be invaluable during neonatal transition to help guide supportive care and ultimately protect the brain. Appropriate quantitative measures of preterm EEG must be calculated and care needs to be taken when applying the many techniques available for this task in the era of modern data science. This review provides valuable information about the factors that influence quantitative EEG analysis and describes the common pitfalls. Careful feature selection is required and attention must be paid to behavioral state given the variations encountered in newborn EEG during different states. Finally, the detrimental influence of artifacts on quantitative EEG analysis is illustrated.

Review of sleep-EEG in preterm and term neonates

Neonatal sleep is a crucial state that involves endogenous driven brain activity, important for neuronal survival and guidance of brain networks. Sequential EEG-sleep analysis in preterm infants provides insights into functional brain integrity and can document deviations of the biologically pre-programmed process of sleep ontogenesis during the neonatal period. Visual assessment of neonatal sleep-EEG, with integration of both cerebral and non-cerebral measures to better define neonatal state, is still considered the gold standard. Electrographic patterns evolve over time and are gradually time locked with behavioural characteristics which allow classification of quiet sleep and active sleep periods during the last 10weeks of gestation. Near term age, the neonate expresses a short ultradian sleep cycle, with two distinct active and quiet sleep, as well as brief periods of transitional or indeterminate sleep. Qualitative assessment of neonatal sleep is however challenged by biological and environmental variables that influence the expression of EEG-sleep patterns and sleep organization. Developing normative EEG-sleep data with the aid of automated analytic methods, can further improve our understanding of extra-uterine brain development and state organization under stressful or pathological conditions. Based on those developmental biomarkers of normal and abnormal brain function, research can be conducted to support and optimise sleep in the NICU, with the ultimate goal to improve therapeutic interventions and neurodevelopmental outcome.

A review of important electroencephalogram features for the assessment of brain maturation in premature infants

This review describes the maturational features of the baseline electroencephalogram (EEG) in the neurologically healthy preterm infant. Features such as continuity, sleep state, synchrony and transient waveforms are described, even from extremely preterm infants and includes abundant illustrated examples. The physiological significance of these EEG features and their relationship to neurodevelopment are highlighted where known. This review also demonstrates the importance of multichannel conventional EEG monitoring for preterm infants as many of the features described are not apparent if limited channel EEG monitors are used.

CONCLUSION

This review aims to provide healthcare professionals in the neonatal intensive care unit with guidance on the more common normal maturational features seen in the EEG of preterm infants.

Detecting bursts in the EEG of very and extremely premature infants using a multi-feature approach

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Machine learning approach enables accurate detection of bursts in preterm EEG.

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Features of amplitude and spectral shape capture discriminating information.

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Improves reliability of estimates of inter-burst intervals.

Aim: To develop a method that segments preterm EEG into bursts and inter-bursts by extracting and combining multiple EEG features. Methods: Two EEG experts annotated bursts in individual EEG channels for 36 preterm infants with gestational age < 30 weeks. The feature set included spectral, amplitude, and frequency-weighted energy features. Using a consensus annotation, feature selection removed redundant features and a support vector machine combined features. Area under the receiver operator characteristic (AUC) and Cohen’s kappa (κ) evaluated performance within a cross-validation procedure. Results: The proposed channel-independent method improves AUC by 4–5% over existing methods (p < 0.001, n=36), with median (95% confidence interval) AUC of 0.989 (0.973–0.997) and sensitivity–specificity of 95.8–94.4%. Agreement rates between the detector and experts’ annotations, κ=0.72 (0.36–0.83) and κ=0.65 (0.32–0.81), are comparable to inter-rater agreement, κ=0.60 (0.21–0.74). Conclusions: Automating the visual identification of bursts in preterm EEG is achievable with a high level of accuracy. Multiple features, combined using a data-driven approach, improves on existing single-feature methods.

The suppression curve as a quantitative approach for measuring brain maturation in preterm infants

OBJECTIVES

We apply the suppression curve (SC) as an automated approach to describe the maturational change in EEG discontinuity in preterm infants. This method allows to define normative values of interburst intervals (IBIs) at different postmenstrual ages (PMA).

METHODS

Ninety-two multichannel EEG recordings from 25 preterm infants (born ⩽32weeks) with normal developmental outcome at 9months, were first analysed using the Line Length method, an established method for burst detection. Subsequently, the SC was defined as the 'level of EEG discontinuity'. The mean and the standard deviation of the SC, as well as the IBIs from each recording were calculated and correlated with PMA.

RESULTS

Over the course of development, there is a decrease in EEG discontinuity with a strong linear correlation between the mean SC and PMA till 34weeks. From 30weeks PMA, differences between discontinuous and continuous EEG become smaller, which is reflected by the decrease of the standard deviation of the SC. IBIs are found to have a significant correlation with PMA.

CONCLUSIONS

Automated detection of individual maturational changes in EEG discontinuity is possible with the SC. These changes include more continuous tracing, less amplitude differences and shorter suppression periods, reflecting development of the vigilance states.

SIGNIFICANCE

The suppression curve facilitates automated assessment of EEG maturation. Clinical applicability is straight forward since values for IBIs according to PMA are generated automatically.

Unobtrusive sleep state measurements in preterm infants - A review

Sleep is important for the development of preterm infants. During sleep, neural connections are formed and the development of brain regions is triggered. In general, various rudimentary sleep states can be identified in the preterm infant, namely active sleep (AS), quiet sleep (QS) and intermediate sleep (IS). As the infant develops, sleep states change in length and organization, with these changes as important indicators of brain development. As a result, several methods have been deployed to distinguish between the different preterm infant sleep states, among which polysomnography (PSG) is the most frequently used. However, this method is limited by the use of adhesive electrodes or patches that are attached to the body by numerous cables that can disturb sleep. Given the importance of sleep, this review explores more unobtrusive methods that can identify sleep states without disturbing the infant. To this end, after a brief introduction to preterm sleep states, an analysis of the physiological characteristics associated with the different sleep states is provided and various methods of measuring these physiological characteristics are explored. Finally, the advantages and disadvantages of each of these methods are evaluated and recommendations for neonatal sleep monitoring proposed.

Multipotent adult progenitor cells for hypoxic-ischemic injury in the preterm brain

Background

Preterm infants are at risk for hypoxic-ischemic encephalopathy. No therapy exists to treat this brain injury and subsequent long-term sequelae. We have previously shown in a well-established pre-clinical model of global hypoxia-ischemia (HI) that mesenchymal stem cells are a promising candidate for the treatment of hypoxic-ischemic brain injury. In the current study, we investigated the neuroprotective capacity of multipotent adult progenitor cells (MAPC^®), which are adherent bone marrow-derived cells of an earlier developmental stage than mesenchymal stem cells and exhibiting more potent anti-inflammatory and regenerative properties.

Methods

Instrumented preterm sheep fetuses were subjected to global hypoxia-ischemia by 25 min of umbilical cord occlusion at a gestational age of 106 (term ~147) days. During a 7-day reperfusion period, vital parameters (e.g., blood pressure and heart rate; baroreceptor reflex) and (amplitude-integrated) electroencephalogram were recorded. At the end of the experiment, the preterm brain was studied by histology.

Results

Systemic administration of MAPC therapy reduced the number and duration of seizures and prevented decrease in baroreflex sensitivity after global HI. In addition, MAPC cells prevented HI-induced microglial proliferation in the preterm brain. These anti-inflammatory effects were associated with MAPC-induced prevention of hypomyelination after global HI. Besides attenuation of the cerebral inflammatory response, our findings showed that MAPC cells modulated the peripheral splenic inflammatory response, which has been implicated in the etiology of hypoxic-ischemic injury in the preterm brain.

Conclusions

In a pre-clinical animal model MAPC cell therapy improved the functional and structural outcome of the preterm brain after global HI. Future studies should establish the mechanism and long-term therapeutic effects of neuroprotection established by MAPC cells in the developing preterm brain exposed to HI. Our study may form the basis for future clinical trials, which will evaluate whether MAPC therapy is capable of reducing neurological sequelae in preterm infants with hypoxic-ischemic encephalopathy.

Functional connectivity in preterm infants derived from EEG coherence analysis

OBJECTIVE

To quantify the neuronal connectivity in preterm infants between homologous channels of both hemispheres.

METHODS

EEG coherence analysis was performed on serial EEG recordings collected from preterm infants with normal neurological follow-up. The coherence spectrum was divided in frequency bands: δnewborn(0-2 Hz), θnewborn(2-6 Hz), αnewborn(6-13 Hz), βnewborn(13-30 Hz). Coherence values were evaluated as a function of gestational age (GA) and postnatal maturation.

RESULTS

All spectra show two clear peaks in the δnewborn and θnewborn-band, corresponding to the delta and theta EEG waves observed in preterm infants. In the δnewborn-band the peak magnitude coherence decreases with GA and postnatal maturation for all channels. In the θnewborn-band, the peak magnitude coherence decreases with GA for all channels, but increases with postnatal maturation for the frontal polar channels. In the βnewborn-band a modest magnitude coherence peak was observed in the occipital channels, which decreases with GA.

CONCLUSIONS

Interhemispherical connectivity develops analogously with electrocortical maturation: signal intensities at low frequencies decrease with GA and postnatal maturation, but increase at high frequencies with postnatal maturation. In addition, peak magnitude coherence is a clear trend indicator for brain maturation.

SIGNIFICANCE

Coherence analysis can aid in the clinical assessment of the functional connectivity of the infant brain with maturation.

Probability distributions of the electroencephalogram envelope of preterm infants

OBJECTIVE

To determine the stationary characteristics of electroencephalogram (EEG) envelopes for prematurely born (preterm) infants and investigate the intrinsic characteristics of early brain development in preterm infants.

METHODS

Twenty neurologically normal sets of EEGs recorded in infants with a post-conceptional age (PCA) range of 26-44 weeks (mean 37.5 ± 5.0 weeks) were analyzed. Hilbert transform was applied to extract the envelope. We determined the suitable probability distribution of the envelope and performed a statistical analysis.

RESULTS

It was found that (i) the probability distributions for preterm EEG envelopes were best fitted by lognormal distributions at 38 weeks PCA or less, and by gamma distributions at 44 weeks PCA; (ii) the scale parameter of the lognormal distribution had positive correlations with PCA as well as a strong negative correlation with the percentage of low-voltage activity; (iii) the shape parameter of the lognormal distribution had significant positive correlations with PCA; (iv) the statistics of mode showed significant linear relationships with PCA, and, therefore, it was considered a useful index in PCA prediction.

CONCLUSION

These statistics, including the scale parameter of the lognormal distribution and the skewness and mode derived from a suitable probability distribution, may be good indexes for estimating stationary nature in developing brain activity in preterm infants.

SIGNIFICANCE

The stationary characteristics, such as discontinuity, asymmetry, and unimodality, of preterm EEGs are well indicated by the statistics estimated from the probability distribution of the preterm EEG envelopes.

Power spectral analysis of two-channel EEG in very premature infants undergoing heat loss prevention

OBJECTIVE

To evaluate whether wearing a wool cap, a routine practice used to prevent heat loss in premature infants, affects interpretation of electroencephalogram spectral analysis.

METHODS

Eighteen premature infants (median gestational age 28 weeks, range 23-32) without neurological complications were randomized to two channel (C3, C4 referred to Cz) digital electroencephalogram recordings with (90 min) and without (90 min) wearing wool cap, at 4 days of life. Electroencephalogram was analyzed automatically by measurement of burst suppression ratio and asymmetry index and by Fast Fourier Transform to calculate total absolute spectral power; relative spectral power in the δ (0.5-3.5 Hz), θ (4-7.5 Hz), α (8-12.5 Hz), and β (13-30 Hz) frequency bands; spectral edge frequency; and mean dominant frequency.

RESULTS

The use of wool cap had no effect on all electroencephalogram parameters considered. Gestational age showed an effect on relative spectral power of all considered bands, spectral edge frequency and mean dominant frequency, while no effect was seen on burst suppression ratio and asymmetry index. Neonates born at gestational weeks lower than 28 had significantly higher relative power in the δ band and lower relative power in the α and β bands.

CONCLUSIONS

Heat loss prevention using wool cap does not affect interpretation of spectral electroencephalogram. Spectral values in our group of very premature infants without neurological complications correspond to normal data reported in the literature. Maturation changes consist of reduction of relative power of the δ band, spectral edge frequency and mean dominant frequency.

Early Brain Activity Relates to Subsequent Brain Growth in Premature Infants

Recent experimental studies have shown that early brain activity is crucial for neuronal survival and the development of brain networks; however, it has been challenging to assess its role in the developing human brain. We employed serial quantitative magnetic resonance imaging to measure the rate of growth in circumscribed brain tissues from preterm to term age, and compared it with measures of electroencephalographic (EEG) activity during the first postnatal days by 2 different methods. EEG metrics of functional activity were computed: EEG signal peak-to-peak amplitude and the occurrence of developmentally important spontaneous activity transients (SATs). We found that an increased brain activity in the first postnatal days correlates with a faster growth of brain structures during subsequent months until term age. Total brain volume, and in particular subcortical gray matter volume, grew faster in babies with less cortical electrical quiescence and with more SAT events. The present findings are compatible with the idea that (1) early cortical network activity is important for brain growth, and that (2) objective measures may be devised to follow early human brain activity in a biologically reasoned way in future research as well as during intensive care treatment.

Line length as a robust method to detect high-activity events: automated burst detection in premature EEG recordings

OBJECTIVE

EEG is a valuable tool for evaluation of brain maturation in preterm babies. Preterm EEG constitutes of high voltage burst activities and more suppressed episodes, called interburst intervals (IBIs). Evolution of background characteristics provides information on brain maturation and helps in prediction of neurological outcome. The aim is to develop a method for automated burst detection.

METHODS

Thirteen polysomnography recordings were used, collected at preterm postmenstrual age of 31.4 (26.1-34.4)weeks. We developed a burst detection algorithm based on the feature line length and compared it with manual scorings of clinical experts and other published methods.

RESULTS

The line length-based algorithm is robust (84.27% accuracy, 84.00% sensitivity, 85.70% specificity). It is not critically dependent on the number of measurement channels, because two channels still provide 82% accuracy. Furthermore, it approximates well clinically relevant features, such as median IBI duration 5.45 (4.00-7.11)s, maximum IBI duration 14.02 (8.73-18.80)s and burst percentage 48.89 (35.45-60.12)%, with a median deviation of respectively 0.65s, 1.96s and 6.55%.

CONCLUSION

Automated assessment of long-term preterm EEG is possible and its use will optimize EEG interpretation in the NICU.

SIGNIFICANCE

This study takes a first step towards fully automatic analysis of the preterm brain.

Systemic G-CSF attenuates cerebral inflammation and hypomyelination but does not reduce seizure burden in preterm sheep exposed to global hypoxia-ischemia

Hypoxic-ischemic encephalopathy (HIE) is common in preterm infants, but currently no curative therapy is available. Cell-based therapy has a great potential in the treatment of hypoxic-ischemic preterm brain injury. Granulocyte-colony stimulating factor (G-CSF) is known to mobilize endogenous hematopoietic stem cells (HSC) and promotes proliferation of endogenous neural stem cells. On these grounds, we hypothesized that systemic G-CSF would be neuroprotective in a large translational animal model of hypoxic-ischemic injury in the preterm brain. Global hypoxia-ischemia (HI) was induced by transient umbilical cord occlusion in instrumented preterm sheep. G-CSF treatment (100μg/kg intravenously, during five consecutive days) was started one day before the global HI insult to ascertain mobilization of endogenous stem cells within the acute phase after global HI. Mobilization of HSC and neutrophils was studied by flow cytometry. Brain sections were stained for microglia (IBA-1), myelin basic protein (MBP) and myeloperoxidase (MPO) to study microglial proliferation, white matter injury and neutrophil invasion respectively. Electrographic seizure activity was analyzed using amplitude-integrated electroencephalogram (aEEG). G-CSF effectively mobilized CD34-positive HSC in the preterm sheep. In addition, G-CSF caused marked mobilization of neutrophils, but did not influence enhanced invasion of neutrophils into the preterm brain after global HI. Microglial proliferation and hypomyelination following global HI were reduced as a result of G-CSF treatment. G-CSF did not cause a reduction of the electrographic seizure activity after global HI. In conclusion, G-CSF induced mobilization of endogenous stem cells which was associated with modulation of the cerebral inflammatory response and reduced white matter injury in an ovine model of preterm brain injury after global HI. G-CSF treatment did not improve neuronal function as shown by seizure analysis. Our study shows that G-CSF treatment has neuroprotective potential following hypoxic-ischemic injury in the preterm brain.

Automating the analysis of EEG recordings from prematurely-born infants: a Bayesian approach

OBJECTIVE

To implement an automated analysis of EEG recordings from prematurely-born infants and thus provide objective, reproducible results.

METHODS

Bayesian probability theory is employed to compute the posterior probability for developmental features of interest in EEG recordings. Currently, these features include smooth delta waves (0.5-1.5Hz, >100μV), delta brushes (delta portion: 0.5-1.5Hz, >100μV; "brush" portion: 8-22Hz, <75μV), and interburst intervals (<10μV), though the approach taken can be generalized to identify other EEG features of interest.

RESULTS

When compared with experienced electroencephalographers, the algorithm had a true positive rate between 72% and 79% for the identification of delta waves (smooth or "brush") and interburst intervals, which is comparable to the inter-rater reliability. When distinguishing between smooth delta waves and delta brushes, the algorithm's true positive rate was between 53% and 88%, which is slightly less than the inter-rater reliability.

CONCLUSION

Bayesian probability theory can be employed to consistently identify features of EEG recordings from premature infants.

SIGNIFICANCE

The identification of features in EEG recordings provides a first step towards the automated analysis of EEG recordings from premature infants.

Topography of maturational changes in EEG burst spectral power of the preterm infant with a normal follow-up at 2 years of age

OBJECTIVE

To quantify the electroencephalography (EEG) burst frequency spectrum of preterm infants by automated analysis and to describe the topography of maturational change in spectral parameters.

METHODS

Eighteen preterm infants <32weeks gestation and normal neurological follow-up at 2years underwent weekly 4-h EEG recordings (10-20 system). The recordings (n=77) represent a large variability in postmenstrual age (PMA, 28-36weeks). We applied an automated burst detection algorithm and performed spectral analysis. The frequency spectrum was divided into δ1 (0.5-1Hz), δ2 (1-4Hz), θ (4-8Hz), α (8-13Hz) and β (13-30Hz) bands. Spectral parameters were evaluated as a function of PMA by regression analysis. Results were interpolated and topographically visualised.

RESULTS

The majority of spectral parameters show significant change with PMA. Highest correlation is found for δ and θ band. Absolute band powers decrease with increasing PMA, while relative α and β powers increase. Maturational change is largest in frontal and temporal region.

CONCLUSIONS

Topographic distribution of maturational changes in spectral parameters corresponds with studies showing ongoing gyration and postnatal white matter maturation in frontal and temporal lobes.

SIGNIFICANCE

Computer analysis of EEG may allow objective and reproducible analysis for long-term prognosis and/or stratification of clinical treatment.

Automatic detection of burst synchrony in preterm infants

Electroencephalographic characteristics are useful in assessment of the functional status of specific neuronal connections relative to postmenstrual age. Interhemispheric burst synchrony (IBS) is a measure of the functional connectivity between the hemispheres in the maturing preterm brain. An algorithm was developed to assess IBS and was used in a prospective, longitudinal EEG study on 18 very preterm infants (< 32 weeks gestational age) with normal follow-up at 2 years of age. The preterm infants underwent weekly 4-hour multi-channel EEG recordings, resulting in n = 77 EEGs. After automated detection of bursts, the algorithm defines the start and end of interhemispheric synchronous burst activity, based on selection criteria found in literature. The algorithm was designed to emulate visual inspection, providing objective results in an automated manner. This approach may be applied in clinical use and open novel avenues to automated analysis in EEG monitoring and, moreover, it may facilitate assessment of the functional status of interhemispheric connections. As such, assessment of low interhemispheric synchrony may be associated with brain injury.

Maturational changes in automated EEG spectral power analysis in preterm infants

Our study aimed at automated power spectral analysis of the EEG in preterm infants to identify changes of spectral measures with maturation. Weekly (10-20 montage) 4-h EEG recordings were performed in 18 preterm infants with GA <32 wk and normal neurological follow-up at 2 y, resulting in 79 recordings studied from 27(+4) to 36(+3) wk of postmenstrual age (PMA, GA + postnatal age). Automated spectral analysis was performed on 4-h EEG recordings. The frequency spectrum was divided in delta 1 (0.5-1 Hz), delta 2 (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) band. Absolute and relative power of each frequency band and spectral edge frequency were calculated. Maturational changes in spectral measures were observed most clearly in the centrotemporal channels. With advancing PMA, absolute powers of delta 1 to 2 and theta decreased. With advancing PMA, relative power of delta 1 decreased and relative powers of alpha and beta increased, respectively. In conclusion, with maturation, spectral analysis of the EEG showed a significant shift from the lower to the higher frequencies. Computer analysis of EEG will allow an objective and reproducible analysis for long-term prognosis and/or stratification of clinical treatment.

Automatic burst detection for the EEG of the preterm infant

To aid with prognosis and stratification of clinical treatment for preterm infants, a method for automated detection of bursts, interburst-intervals (IBIs) and continuous patterns in the electroencephalogram (EEG) is developed. Results are evaluated for preterm infants with normal neurological follow-up at 2 years. The detection algorithm (MATLAB®) for burst, IBI and continuous pattern is based on selection by amplitude, time span, number of channels and numbers of active electrodes. Annotations of two neurophysiologists were used to determine threshold values. The training set consisted of EEG recordings of four preterm infants with postmenstrual age (PMA, gestational age + postnatal age) of 29-34 weeks. Optimal threshold values were based on overall highest sensitivity. For evaluation, both observers verified detections in an independent dataset of four EEG recordings with comparable PMA. Algorithm performance was assessed by calculation of sensitivity and positive predictive value. The results of algorithm evaluation are as follows: sensitivity values of 90% ± 6%, 80% ± 9% and 97% ± 5% for burst, IBI and continuous patterns, respectively. Corresponding positive predictive values were 88% ± 8%, 96% ± 3% and 85% ± 15%, respectively. In conclusion, the algorithm showed high sensitivity and positive predictive values for bursts, IBIs and continuous patterns in preterm EEG. Computer-assisted analysis of EEG may allow objective and reproducible analysis for clinical treatment.

Maternal dexamethasone and EEG hyperactivity in preterm fetal sheep

Maternal treatment with synthetic corticosteroids such as dexamethasone (DEX)significantly reduces neonatal morbidity and mortality, but its effects on the fetal brain remain unclear. In this study we evaluated the effects of DEX on EEG activity in preterm fetal sheep. Ewes at 103 days gestation received two intramuscular injections of DEX (12 mg, n = 8) or saline vehicle (n = 7) 24 h apart. Fetal EEG activity was recorded from 6 h before until 120 h after the first injection (DEX-1). DEX-1 was associated with a marked transient rise in total EEG power, maximal at 12 h (P < 0.001), with a relative increase in delta and reduced theta, alpha and beta activity, resolving by 24 h. Continuous EEG records showed a shift to larger but less frequent transient waveforms (P < 0.001). Unexpectedly, evolving epileptiform activity, consistent with electrographic and clinical seizures, developed from 178 ± 44 min after DEX-1.Similar but smaller changes were seen after the second injection. Following the injections, total power returned to control values, but the proportion of alpha activity progressively increased vs. controls (P < 0.001), with reduced interburst interval duration and number (P < 0.001). No histological neural injury or microglial activation was seen. In summary, exposure to maternal dexamethasone was associated with dramatic, evolving low-frequency hyperactivity on fetal cortical EEG recordings, followed by sustained changes consistent with maturation of fetal sleep architecture. We postulate that these effects may contribute to improved neonatal outcomes.