Relationship Between Early Functional and Structural Brain Developments and Brain Injury in Preterm Infants.

BACKGROUND: Recent studies explored the relationship between early brain function and brain morphology, based on the hypothesis that increased brain activity can positively affect structural brain development and that excitatory neuronal activity stimulates myelination. OBJECTIVE: To investigate the relationship between maturational features from early and serial aEEGs after premature birth and MRI metrics characterizing structural brain development and injury, measured around 30weeks postmenstrual age (PMA) and at term. Moreover, we aimed to verify whether previously developed maturational EEG features are related with PMA. DESIGN/METHODS: One hundred six extremely preterm infants received bedside aEEGs during the first 72h and weekly until week 5. 3T-MRIs were performed at 30weeks PMA and at term. Specific features were extracted to assess EEG maturation: (1) the spectral content, (2) the continuity [percentage of spontaneous activity transients (SAT%) and the interburst interval (IBI)], and (3) the complexity. Automatic MRI segmentation to assess volumes and MRI score was performed. The relationship between the maturational EEG features and MRI measures was investigated. RESULTS: Both SAT% and EEG complexity were correlated with PMA. IBI was inversely associated with PMA. Complexity features had a positive correlation with the cerebellar size at 30weeks, while event-based measures were related to the cerebellar size at term. Cerebellar width, cortical grey matter, and total brain volume at term were inversely correlated with the relative power in the higher frequency bands. CONCLUSIONS: The continuity and complexity of the EEG steadily increase with increasing postnatal age. Increasing complexity and event-based features are associated with cerebellar size, a structure with enormous development during preterm life. Brain activity is important for later structural brain development.

Motor cortex metabolite alterations in amyotrophic lateral sclerosis assessed in vivo using edited and non-edited magnetic resonance spectroscopy.

Previous MRI and proton spectroscopy (1H-MRS) studies have revealed impaired neuronal integrity and altered neurometabolite concentrations in the motor cortex of patients with amyotrophic lateral sclerosis (ALS). Here, we aim to use MRI with conventional and novel MRS sequences to further investigate neurometabolic changes in the motor cortex of ALS patients and their relation to clinical parameters. We utilized the novel HERMES (Hadamard Encoding and Reconstruction of MEGA-Edited Spectroscopy) MRS sequence to simultaneously quantify the inhibitory neurotransmitter GABA and antioxidant glutathione in ALS patients (n = 7) and healthy controls (n = 7). In addition, we have also quantified other MRS observable neurometabolites using a conventional point-resolved MR spectroscopy (PRESS) sequence in ALS patients (n = 20) and healthy controls (n = 20). We observed a trend towards decreasing glutathione concentrations in the motor cortex of ALS patients (p = 0.0842). In addition, we detected a 11% decrease in N-acetylaspartate (NAA) (p = 0.025), a 15% increase in glutamate + glutamine (Glx) (p = 0.0084) and a 21% increase in myo-inositol (mIns) (p = 0.0051) concentrations for ALS patients compared to healthy controls. Furthermore, significant positive correlations were found between GABA-NAA (p = 0.0480; Rρ = 0.7875) and NAA-mIns (p = 0.0448; Rρ = -0.4651) levels among the patients. NAA levels in the bulbar-onset patient group were found to be significantly (p = 0.0097) lower compared to the limb-onset group. A strong correlation (p < 0.0001; Rρ = -0,8801) for mIns and a weak correlation (p = 0.0066; Rρ = -0,6673) for Glx was found for the disease progression, measured by declining of the ALS Functional Rating Scale-Revised criteria (ALSFRS-R). Concentrations of mIns and Glx also correlated with disease severity measured by forced vital capacity (FVC). Results suggest that mean neurometabolite concentrations detected in the motor cortex may indicate clinical and pathological changes in ALS.

The implementation of an apnea-based perinatal stress calculator.

Early life stress in the neonatal intensive care unit (NICU) predisposes premature infants to adverse health outcomes. Although those patients experience frequent apneas and sleep-wake disturbances during their hospital stay, clinicians still rely on clinical scales to assess pain and stress burden. This study addresses the relationship between stress and apneic spells in NICU patients to implement an automatic stress detector. EEG, ECG and SpO2 were recorded from 40 patients for at least 3 hours and the stress burden was assessed using the Leuven Pain Scale. Different logistic regression models were designed to detect the presence or the absence of stress based on the signals reactivity to each apneic spell. The classification shows that stress can be detected with an area under the curve of 0.94 and a misclassification error of 19.23%. These results were obtained via SpO2 dips and EEG regularity. These findings suggest that stress deepens the physiological reaction to apneas, which could ultimately impact the neurological and behavioral development.

Non-invasive assessment of disease progression and neuroprotective effects of dietary coconut oil supplementation in the ALS SOD1G93A mouse model: A 1H-magnetic resonance spectroscopic study.

Amyotrophic Lateral Sclerosis (ALS) is an incurable neurodegenerative disease primarily characterized by progressive degeneration of motor neurons in the motor cortex, brainstem and spinal cord. Due to relatively fast progression of ALS, early diagnosis is essential for possible therapeutic intervention and disease management. To identify potential diagnostic markers, we investigated age-dependent effects of disease onset and progression on regional neurochemistry in the SOD1G93A ALS mouse model using localized in vivo magnetic resonance spectroscopy (MRS). We focused mainly on the brainstem region since brainstem motor nuclei are the primarily affected regions in SOD1G93A mice and ALS patients. In addition, metabolite profiles of the motor cortex were also assessed. In the brainstem, a gradual decrease in creatine levels were detected starting from the pre-symptomatic age of 70 days postpartum. During the early symptomatic phase (day 90), a significant increase in the levels of the inhibitory neurotransmitter γ- aminobutyric acid (GABA) was measured. At later time points, alterations in the form of decreased NAA, glutamate, glutamine and increased myo-inositol were observed. Also, decreased glutamate, NAA and increased taurine levels were seen at late stages in the motor cortex. A proof-of-concept (PoC) study was conducted to assess the effects of coconut oil supplementation in SODG93A mice. The PoC revealed that the coconut oil supplementation together with the regular diet delayed disease symptoms, enhanced motor performance, and prolonged survival in the SOD1G93A mouse model. Furthermore, MRS data showed stable metabolic profile at day 120 in the coconut oil diet group compared to the group receiving a standard diet without coconut oil supplementation. In addition, a positive correlation between survival and the neuronal marker NAA was found. To the best of our knowledge, this is the first study that reports metabolic changes in the brainstem using in vivo MRS and effects of coconut oil supplementation as a prophylactic treatment in SOD1G93A mice.

A brain-age model for preterm infants based on functional connectivity.

OBJECTIVE: In this study, the development of EEG functional connectivity during early development has been investigated in order to provide a predictive age model for premature infants. APPROACH: The functional connectivity has been assessed via the coherency function (its imaginary part (ImCoh) and its mean squared magnitude (MSC)), the phase locking value ([Formula: see text]) and the Hilbert-Schimdt dependence (HSD) in a dataset of 30 patients, partially described and employed in previous studies (Koolen et al 2016 Neuroscience 322 298-307; Lavanga et al 2017 Complexity 2017 1-13). Infants' post-menstrual age (PMA) ranges from 27 to 42 weeks. The topology of the EEG couplings has been investigated via graph-theory indices. MAIN RESULTS: Results show a sharp decrease in ImCoh indices in θ, (4-8) Hz and α, (8-16) Hz bands and MSC in ß, (16-32) Hz band with maturation, while a more modest positive correlation with PMA is found for HSD, [Formula: see text] and MSC in [Formula: see text], θ, α bands. The best performances for the PMA prediction were mean absolute error equal to 1.51 weeks and adjusted coefficient of determination [Formula: see text] equal to 0.8. SIGNIFICANCE: The reported findings suggest a segregation of the cortex connectivity, which favours a diffused tasks architecture on the brain scalp. In summary, the results indicate that the neonates' brain development can be described via lagged-interaction network features.

Does ignoring clustering in multicenter data influence the performance of prediction models? A simulation study.

Clinical risk prediction models are increasingly being developed and validated on multicenter datasets. In this article, we present a comprehensive framework for the evaluation of the predictive performance of prediction models at the center level and the population level, considering population-averaged predictions, center-specific predictions, and predictions assuming an average random center effect. We demonstrated in a simulation study that calibration slopes do not only deviate from one because of over- or underfitting of patterns in the development dataset, but also as a result of the choice of the model (standard versus mixed effects logistic regression), the type of predictions (marginal versus conditional versus assuming an average random effect), and the level of model validation (center versus population). In particular, when data is heavily clustered (ICC 20%), center-specific predictions offer the best predictive performance at the population level and the center level. We recommend that models should reflect the data structure, while the level of model validation should reflect the research question.

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.

Improved neonatal seizure detection using adaptive learning.

In neonatal intensive care units performing continuous EEG monitoring, there is an unmet need for around-the-clock interpretation of EEG, especially for recognizing seizures. In recent years, a few automated seizure detection algorithms have been proposed. However, these are suboptimal in detecting brief-duration seizures (<; 30s), which frequently occur in neonates with severe neurological problems. Recently, a multi-stage neonatal seizure detector, composed of a heuristic and a data-driven classifier was proposed by our group and showed improved detection of brief seizures. In the present work, we propose to add a third stage to the detector in order to use feedback of the Clinical Neurophysiologist and adaptively retune a threshold of the second stage to improve the performance of detection of brief seizures. As a result, the false alarm rate (FAR) of the brief seizure detections decreased by 50% and the positive predictive value (PPV) increased by 18%. At the same time, for all detections, the FAR decreased by 35% and PPV increased by 5% while the good detection rate remained unchanged.

Inferior and anterior QRS fragmentation have different prognostic value in patients who received an implantable defibrillator in primary prevention of sudden cardiac death.

AIMS: QRS fragmentation (fQRS) has been proposed as a predictor of sudden cardiac death (SCD) and all-cause mortality in ischemic (ICM) and non-ischemic cardiomyopathy patients. However the value of fQRS in patients with a LVEF <35% is a matter of debate. METHODS: All consecutive patients with an indication for an ICD in primary prevention of SCD were included in a retrospective registry from 1996 until 2013. Twelve lead electrocardiograms before implant were analyzed for the presence of fQRS in different regions. Adjusted Cox regression analysis for first appropriate ICD shock (AS) and all-cause mortality was performed. RESULTS: In total 407 patients were included with a mean follow-up of 4.2±3.3y (age 60.6±11.9y, 15.7% female and 52.8% ICM). fQRS was present in 46.7% of patients, predominantly inferior (30.7%) followed by anterior (21.4%) and lateral (11.1%) coronary artery territories. fQRS was significantly more prevalent in ICM (p=0.004). Inferior fQRS was an independent predictor of a first AS within 1y (HR 2.55, 95%CI 1.28-5.07) and 3y (HR 1.90, 95%CI 1.14-3.18) after implantation. Whereas, anterior fQRS was an independent predictor of all-cause mortality within 1y (HR 4.58, 95%CI 1.29-16.19), 3y (HR 3.92, 95%CI 1.77-8.65) and the complete follow-up (HR 2.22, 95%CI 1.33-3.69). Lateral fQRS was only a predictor of late (>3y of follow-up) all-cause mortality (HR 2.04, 95%CI 1.09-3.81). CONCLUSIONS: fQRS in a specific coronary artery territory might be promising to discriminate arrhythmic from mortality risk. Inferior fQRS was a predictor of early arrhythmia, while anterior fQRS was related to mortality.

Nonnegative Canonical Polyadic Decomposition for Tissue-Type Differentiation in Gliomas.

Magnetic resonance spectroscopic imaging (MRSI) reveals chemical information that characterizes different tissue types in brain tumors. Blind source separation techniques are used to extract the tissue-specific profiles and their corresponding distribution from the MRSI data. We focus on automatic detection of the tumor, necrotic and normal brain tissue types by constructing a 3D MRSI tensor from in vivo 2D-MRSI data of individual glioma patients. Nonnegative canonical polyadic decomposition (NCPD) is applied to the MRSI tensor to differentiate various tissue types. An in vivo study shows that NCPD has better performance in identifying tumor and necrotic tissue type in glioma patients compared to previous matrix-based decompositions, such as nonnegative matrix factorization and hierarchical nonnegative matrix factorization.

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.

Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

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.

Improved multi-stage neonatal seizure detection using a heuristic classifier and a data-driven post-processor.

OBJECTIVE: After identifying the most seizure-relevant characteristics by a previously developed heuristic classifier, a data-driven post-processor using a novel set of features is applied to improve the performance. METHODS: The main characteristics of the outputs of the heuristic algorithm are extracted by five sets of features including synchronization, evolution, retention, segment, and signal features. Then, a support vector machine and a decision making layer remove the falsely detected segments. RESULTS: Four datasets including 71 neonates (1023h, 3493 seizures) recorded in two different university hospitals, are used to train and test the algorithm without removing the dubious seizures. The heuristic method resulted in a false alarm rate of 3.81 per hour and good detection rate of 88% on the entire test databases. The post-processor, effectively reduces the false alarm rate by 34% while the good detection rate decreases by 2%. CONCLUSION: This post-processing technique improves the performance of the heuristic algorithm. The structure of this post-processor is generic, improves our understanding of the core visually determined EEG features of neonatal seizures and is applicable for other neonatal seizure detectors. SIGNIFICANCE: The post-processor significantly decreases the false alarm rate at the expense of a small reduction of the good detection rate.

Early development of synchrony in cortical activations in the human.

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.

Anxious women do not show the expected decrease in cardiovascular stress responsiveness as pregnancy advances.

Altered stress responsiveness is a risk factor for mental and physical illness. In non-pregnant populations, it is well-known that anxiety can alter the physiological regulation of stress reactivity. Characterization of corresponding risks for pregnant women and their offspring requires greater understanding of how stress reactivity and recovery are influenced by pregnancy and women's anxiety feelings. In the current study, women were presented repeatedly with mental arithmetic stress tasks in the first and third pregnancy trimester and reported their trait anxiety using the state trait anxiety inventory. Cardiovascular stress reactivity in late pregnancy was lower than reactivity in the first pregnancy trimester (heart rate (HR): t(197)=4.98, p<.001; high frequency heart rate variability (HF HRV): t(196)=-2.09, p=.04). Less attenuation of stress reactivity occurred in more anxious women (HR: b=0.15, SE=0.06, p=.008; HF HRV: b=-10.97, SE=4.79, p=.02). The study design did not allow the influence of habituation to repeated stress task exposure to be assessed separately from the influence of pregnancy progression. Although this is a limitation, the clear differences between anxious and non-anxious pregnant women are important, regardless of the extent to which differing habituation between the groups is responsible. Less dampened stress reactivity through pregnancy may pose long-term risks for anxious women and their offspring. Follow-up studies are required to determine these risks.

Probabilistic cardiac and respiratory based classification of sleep and apneic events in subjects with sleep apnea.

Current clinical standards to assess sleep and its disorders lack either accuracy or user-friendliness. They are therefore difficult to use in cost-effective population-wide screening or long-term objective follow-up after diagnosis. In order to fill this gap, the use of cardiac and respiratory information was evaluated for discrimination between different sleep stages, and for detection of apneic breathing. Alternative probabilistic visual representations were also presented, referred to as the hypnocorrogram and apneacorrogram. Analysis was performed on the UCD sleep apnea database, available on Physionet. The presence of apneic events proved to have a significant impact on the performance of a cardiac and respiratory based algorithm for sleep stage classification. WAKE versus SLEEP discrimination resulted in a kappa value of κ = 0.0439, while REM versus NREM resulted in κ = 0.298 and light sleep (N1N2) versus deep sleep (N3) in κ = 0.339. The high proportion of hypopneic events led to poor detection of apneic breathing, resulting in a kappa value of κ = 0.272. While the probabilistic representations allow to put classifier output in perspective, further improvements would be necessary to make the classifier reliable for use on patients with sleep apnea.

A simulation study of sample size demonstrated the importance of the number of events per variable to develop prediction models in clustered data.

OBJECTIVES: This study aims to investigate the influence of the amount of clustering [intraclass correlation (ICC) = 0%, 5%, or 20%], the number of events per variable (EPV) or candidate predictor (EPV = 5, 10, 20, or 50), and backward variable selection on the performance of prediction models. STUDY DESIGN AND SETTING: Researchers frequently combine data from several centers to develop clinical prediction models. In our simulation study, we developed models from clustered training data using multilevel logistic regression and validated them in external data. RESULTS: The amount of clustering was not meaningfully associated with the models' predictive performance. The median calibration slope of models built in samples with EPV = 5 and strong clustering (ICC = 20%) was 0.71. With EPV = 5 and ICC = 0%, it was 0.72. A higher EPV related to an increased performance: the calibration slope was 0.85 at EPV = 10 and ICC = 20% and 0.96 at EPV = 50 and ICC = 20%. Variable selection sometimes led to a substantial relative bias in the estimated predictor effects (up to 118% at EPV = 5), but this had little influence on the model's performance in our simulations. CONCLUSION: We recommend at least 10 EPV to fit prediction models in clustered data using logistic regression. Up to 50 EPV may be needed when variable selection is performed.

A spline-based tool to assess and visualize the calibration of multiclass risk predictions.

When validating risk models (or probabilistic classifiers), calibration is often overlooked. Calibration refers to the reliability of the predicted risks, i.e. whether the predicted risks correspond to observed probabilities. In medical applications this is important because treatment decisions often rely on the estimated risk of disease. The aim of this paper is to present generic tools to assess the calibration of multiclass risk models. We describe a calibration framework based on a vector spline multinomial logistic regression model. This framework can be used to generate calibration plots and calculate the estimated calibration index (ECI) to quantify lack of calibration. We illustrate these tools in relation to risk models used to characterize ovarian tumors. The outcome of the study is the surgical stage of the tumor when relevant and the final histological outcome, which is divided into five classes: benign, borderline malignant, stage I, stage II-IV, and secondary metastatic cancer. The 5909 patients included in the study are randomly split into equally large training and test sets. We developed and tested models using the following algorithms: logistic regression, support vector machines, k nearest neighbors, random forest, naive Bayes and nearest shrunken centroids. Multiclass calibration plots are interesting as an approach to visualizing the reliability of predicted risks. The ECI is a convenient tool for comparing models, but is less informative and interpretable than calibration plots. In our case study, logistic regression and random forest showed the highest degree of calibration, and the naive Bayes the lowest.

Classifying the auditory P300 using mobile EEG recordings without calibration phase.

One of the major drawbacks in mobile EEG Brain Computer Interfaces (BCI) is the need for subject specific training data to train a classifier. By removing the need for supervised classification and calibration phase, new users could start immediate interaction with a BCI. We propose a solution to exploit the structural difference by means of canonical polyadic decomposition (CPD) for three-class auditory oddball data without the need for subject-specific information. We achieve this by adding average event-related-potential (ERP) templates to the CPD model. This constitutes a novel similarity measure between single-trial pairs and known-templates, which results in a fast and interpretable classifier. These results have similar accuracy to those of the supervised and cross-validated stepwise LDA approach but without the need for having subject-dependent data. Therefore the described CPD method has a significant practical advantage over the traditional and widely used approach.