A Bayesian parametric model for quantifying brain maturation from sleep-EEG in the vulnerable newborn baby

Newborn babies, particularly preterms, can exhibit early deviations in sleep maturation as seen by Electroencephalogram (EEG) recordings. This may be indicative of cognitive problems by school-age. The current 'clinically-driven' approach uses separate algorithms to first extract sleep states and then predict EEG 'brain-age'. Maturational deviations are identified when the brain-age no longer matches the Postmenstrual Age (PMA, the age since the last menstrual cycle of the mother). However, the PMA range where existing sleep staging algorithms perform optimally, is limited, which subsequently limits the PMA range for brain-age prediction. We introduce a Bayesian Parametric Model (BPM) as a single end-to-end solution to directly estimate brain-age, modelling for sleep state maturation without requiring a separately optimized sleep staging algorithm. Comparison of this model with a traditional multi-stage approach, yields a similar Krippendorff's $\alpha = 0.92$ (a performance measure ranging from 0 (chance agreement) to 1 (perfect agreement)) with the BPM performing better at younger ages <30 weeks PMA. The BPM's potential to detect maturational deviations is also explored on a few preterm babies who were abnormal at 9 months follow-up.

Mood disorders and prospective suicidality in young adults: a population-based cohort study

OBJECTIVE

To assess the prospective associations of mood disorders and suicidality in a community sample of young adults from south Brazil.

METHOD

Prospective population-based cohort study. Young adults (18-24 years old) were recruited and followed up on 5 years later; people were interviewed at their homes. Suicidality, as well as mood and anxiety disorders, was assessed using the Mini-International Neuropsychiatric Interview. The impact of mood episodes on suicidality was both evaluated when they occurred in the same wave (a current episode) and when suicidality occurred prospectively, with suicidality measured at follow-up (a past episode).

RESULTS

The sample included 1560 young adults at baseline, with 1244 reassessed at follow-up (80.6%). Depressive episodes, both current and past, had a significant impact on suicidality in the final multivariable model. Manic episodes, however, were less consistently associated with suicidality.

CONCLUSION

Depressive episodes have a strong, independent, and robust association with prospective suicidality. The association between manic episodes and suicidality, on the other hand, was dependent on the analysis and deserves further exploration.

Isolated sulfite oxidase deficiency

Isolated sulfite oxidase deficiency (ISOD) is a life-threatening, autosomal recessive disease characterized by severe neurological impairment. As no long-term effective treatment is available, distinction from other treatable diseases, such as molybdenum cofactor deficiency (MoCD) type A, should be made. We reviewed 47 patients (45 previously reported in the literature). Cases were reviewed for consanguinity, sex, age at onset, death, clinical findings (including spasticity, seizures, psychomotor retardation, feeding difficulties, ectopia lentis, microcephaly), laboratory findings [urinary sulfite, S-sulfocysteine (in plasma and urine), plasma cystine, total homocysteine, uric acid, and oxypurines in urine] and radiological findings (including cerebral/cerebellar atrophy, cystic white matter changes, ventriculomegaly). We also aligned the published SUOX gene mutations to the reference sequence NM_000456.2. Onset occurred mostly during the first 72 h of life (57%) and within the first year of life in all but two patients (96%). All patients presented with neurological abnormalities, such as neonatal axial hypotonia and/or peripheral hypertonia (100%), (pharmacoresistant) seizures (84%), or developmental delay (97%). Feeding problems were also common. As found in our review, measurement of homocysteine in plasma, amino acids in plasma/urine, and sulfite in fresh urine supports the diagnosis of ISOD. Analysis of uric acid (plasma) and oxypurines (urine) is useful to rule out MoCD. In all patients in whom brain magnetic resonance imaging/computed tomography (MRI/CT) was performed, brain abnormalities were found. The purpose of this literature review is to provide a thorough overview of clinical, neuroimaging, biochemical, and genetic findings of patients with ISOD.

Nucleon Spin and Momentum Decomposition Using Lattice QCD Simulations

We determine within lattice QCD the nucleon spin carried by valence and sea quarks and gluons. The calculation is performed using an ensemble of gauge configurations with two degenerate light quarks with mass fixed to approximately reproduce the physical pion mass. We find that the total angular momentum carried by the quarks in the nucleon is J_{u+d+s}=0.408(61)_{stat}(48)_{syst} and the gluon contribution is J_{g}=0.133(11)_{stat}(14)_{syst}, giving a total of J_{N}=0.54(6)_{stat}(5)_{syst} that is consistent with the spin sum. For the quark intrinsic spin contribution, we obtain 1/2ΔΣ_{u+d+s}=0.201(17)_{stat}(5)_{syst}. All quantities are given in the modified minimal subtraction scheme at 2 GeV. The quark and gluon momentum fractions are also computed and add up to ⟨x⟩_{u+d+s}+⟨x⟩_{g}=0.804(121)_{stat}(95)_{syst}+0.267(12)_{stat}(10)_{syst}=1.07(12)_{stat}(10)_{syst}, thus satisfying the momentum sum.

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.

Automatic quiet sleep detection based on multifractality in preterm neonates: Effects of maturation

This study investigates the multifractal formalism framework for quiet sleep detection in preterm babies. EEG recordings from 25 healthy preterm infants were used in order to evaluate the performance of multifractal measures for the detection of quiet sleep. Results indicate that multifractal analysis based on wavelet leaders is able to identify quiet sleep epochs, but the classifier performances seem to be highly affected by the infant's age. In particular, from the developed classifiers, the lowest area under the curve (AUC) has been obtained for EEG recordings at very young age (≤ 31 weeks post-menstrual age), and the maximum at full-term age (≥ 37 weeks post-menstrual age). The improvement in classification performances can be due to a change in the multifractality properties of neonatal EEG during the maturation of the infant, which makes the EEG sleep stages more distinguishable.

An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation

Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age (PMA = gestational age + postnatal age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27–42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement Kappa = 0. 93), using Sensitivity, Specificity, Detection Factor (DF = proportion of visual QS periods correctly detected by CLASS) and Misclassification Factor (MF = proportion of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31–38 weeks (median DF = 1.0, median MF 0–0.25, median Sensitivity 0.93–1.0, and median Specificity 0.80–0.91 across this age range), with minimal misclassifications at 35–36 weeks (median MF = 0). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation.

Childhood trauma, family history, and their association with mood disorders in early adulthood

OBJECTIVE

To assess the prevalence of childhood trauma and types of trauma on mood disorders among young adults in a population-based sample. We further gathered data on family history of mood disorders to test the hypothesis that childhood trauma is a mediating factor for the association between family history of mood disorder and mood disorder in adulthood.

METHOD

This is a cross-sectional study, including young adults with bipolar disorder, major depressive disorder, and matched controls without any mood disorder. Childhood trauma was assessed using the Childhood Trauma Questionnaire (CTQ). The Hicks and Tingley implementation was employed to assess whether trauma is a mediator of the effect of family history on diagnosis of any mood disorder.

RESULTS

All types of trauma were associated with both major depression and bipolar disorder, with the exception of sexual abuse, which was only associated with bipolar disorder. Moreover, family history of psychiatric illness was also associated with mood disorder in adulthood and with childhood trauma. Using the presence of any mood disorder as the outcome, a third of the effect of having any family history of mood disorder was mediated via childhood trauma.

CONCLUSION

This investigation provides further support, in a population-based sample of young adults, of the association between childhood trauma and mood disorders, with sexual abuse being specifically linked with bipolar disorder. The hypothesis that childhood trauma would function as a partial mediator of the association between family history of mood disorder and mood disorder in adulthood was also confirmed.

Non-EEG seizure detection systems and potential SUDEP prevention: State of the art: Review and update

PURPOSE

Detection of, and alarming for epileptic seizures is increasingly demanded and researched. Our previous review article provided an overview of non-invasive, non-EEG (electro-encephalography) body signals that can be measured, along with corresponding methods, state of the art research, and commercially available systems. Three years later, many more studies and devices have emerged. Moreover, the boom of smart phones and tablets created a new market for seizure detection applications.

METHOD

We performed a thorough literature review and had contact with manufacturers of commercially available devices.

RESULTS

This review article gives an updated overview of body signals and methods for seizure detection, international research and (commercially) available systems and applications. Reported results of non-EEG based detection devices vary between 2.2% and 100% sensitivity and between 0 and 3.23 false detections per hour compared to the gold standard video-EEG, for seizures ranging from generalized to convulsive or non-convulsive focal seizures with or without loss of consciousness. It is particularly interesting to include monitoring of autonomic dysfunction, as this may be an important pathophysiological mechanism of SUDEP (sudden unexpected death in epilepsy), and of movement, as many seizures have a motor component.

CONCLUSION

Comparison of research results is difficult as studies focus on different seizure types, timing (night versus day) and patients (adult versus pediatric patients). Nevertheless, we are convinced that the most effective seizure detection systems are multimodal, combining for example detection methods for movement and heart rate, and that devices should especially take into account the user's seizure types and personal preferences.

Nutri-epigenomic Studies Related to Neural Tube Defects: Does Folate Affect Neural Tube Closure Via Changes in DNA Methylation?

Neural tube defects (NTDs), affecting 1-2 per 1000 pregnancies, are severe congenital malformations that arise from the failure of neurulation during early embryonic development. The methylation hypothesis suggests that folate prevents NTDs by stimulating cellular methylation reactions. Folate is central to the one-carbon metabolism that produces pyrimidines and purines for DNA synthesis and for the generation of the methyldonor S-adenosyl-methionine. This review focuses on the relation between the folate-mediated one-carbon metabolism, DNA methylation and NTDs. Studies will be discussed that investigated global or locus-specific DNA methylation differences in patients with NTDs. Folate deficiency may increase NTD risk by decreasing DNA methylation, but to date, human studies vary widely in study design in terms of analyzing different clinical subtypes of NTDs, using different methylation quantification assays and using DNA isolated from diverse types of tissues. Some studies have focused mainly on global DNA methylation differences while others have quantified specific methylation differences for imprinted genes, transposable elements and DNA repair enzymes. Findings of global DNA hypomethylation and LINE-1 hypomethylation suggest that epigenetic alterations may disrupt neural tube closure. However, current research does not support a linear relation between red blood cell folate concentration and DNA methylation. Further studies are required to better understand the interaction between folate, DNA methylation changes and NTDs.

Direct Evaluation of the Quark Content of Nucleons from Lattice QCD at the Physical Point

We evaluate the light, strange, and charm scalar content of the nucleon using one lattice QCD ensemble generated with two degenerate light quarks with mass fixed to their physical value. We use improved techniques to evaluate the disconnected quark loops to sufficient accuracy to determine the strange and charm nucleon σ terms in addition to the light quark content σ_{πN}. We find σ_{πN}=37.2(2.6)(4.7/2.9)  MeV, σ_{s}=41.1(8.2)(7.8/5.8)  MeV, and σ_{c}=79(21)(12/8)  MeV, where the first error is statistical and the second is the systematic error due to the determination of the lattice spacing, the assessment of finite volume, and residual excited state effects.

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.

Early development of synchrony in cortical activations in the human

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We study the early development of cortical activations synchrony index (ASI).

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Cortical activations become increasingly synchronized during the last trimester.

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Interhemispheric synchrony increases more than intrahemispheric synchrony.

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Our EEG metric ASI can be directly translated to experimental animal studies.

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ASI holds promise as an early functional biomarker of brain networks.

Early intermittent cortical activity is thought to play a crucial role in the growth of neuronal network development, and large scale brain networks are known to provide the basis for higher brain functions. Yet, the early development of the large scale synchrony in cortical activations is unknown. Here, we tested the hypothesis that the early intermittent cortical activations seen in the human scalp EEG show a clear developmental course during the last trimester of pregnancy, the period of intensive growth of cortico-cortical connections. We recorded scalp EEG from altogether 22 premature infants at post-menstrual age between 30 and 44 weeks, and the early cortical synchrony was quantified using recently introduced activation synchrony index (ASI). The developmental correlations of ASI were computed for individual EEG signals as well as anatomically and mathematically defined spatial subgroups. We report two main findings. First, we observed a robust and statistically significant increase in ASI in all cortical areas. Second, there were significant spatial gradients in the synchrony in fronto-occipital and left-to-right directions. These findings provide evidence that early cortical activity is increasingly synchronized across the neocortex. The ASI-based metrics introduced in our work allow direct translational comparison to in vivo animal models, as well as hold promise for implementation as a functional developmental biomarker in future research on human neonates.