Early Ultrasonic Monitoring of Brain Growth and Later Neurodevelopmental Outcome in Very Preterm Infants.

BACKGROUND AND PURPOSE: In infants born very preterm, monitoring of early brain growth could contribute to prediction of later neurodevelopment. Therefore, our aim was to investigate associations between 2 early cranial ultrasound markers (corpus callosum-fastigium and corpus callosum length) and neurodevelopmental outcome and the added value of both markers in the prediction of neurodevelopmental outcome based on neonatal risk factors and head circumference in very preterm infants. MATERIALS AND METHODS: This prospective observational study included 225 infants born at <30 weeks' gestational age, of whom 153 were without any brain injury on cranial ultrasound. Corpus callosum-fastigium and corpus callosum length and head circumference were measured at birth, 29 weeks' gestational age, transfer from the neonatal intensive care unit to a level II hospital, and 2 months' corrected age. We analyzed associations of brain markers and their growth with cognitive, motor, language, and behavioral outcome at 2 years' corrected age. RESULTS: In infants without brain injury, greater corpus callosum-fastigium length at 2 months was associated with better cognitive outcome. Corpus callosum length at 2 months was positively associated with cognitive, motor, and language outcome. Faster growth of the corpus callosum length between birth and 2 months was associated with better cognitive and motor function. Prediction of neurodevelopmental outcome based on neonatal risk factors with or without head circumference was significantly improved by adding corpus callosum length. CONCLUSIONS: Both corpus callosum-fastigium and corpus callosum length on cranial ultrasound are associated with neurodevelopmental outcome of very preterm infants without brain injury at 2 years, but only corpus callosum length shows the added clinical utility in predicting neurodevelopmental outcome.

Neonatal Developmental Venous Anomalies: Clinicoradiologic Characterization and Follow-Up.

BACKGROUND AND PURPOSE: Although developmental venous anomalies have been frequently studied in adults and occasionally in children, data regarding these entities are scarce in neonates. We aimed to characterize clinical and neuroimaging features of neonatal developmental venous anomalies and to evaluate any association between MR imaging abnormalities in their drainage territory and corresponding angioarchitectural features. MATERIALS AND METHODS: We reviewed parenchymal abnormalities and angioarchitectural features of 41 neonates with developmental venous anomalies (20 males; mean corrected age, 39.9 weeks) selected through a radiology report text search from 2135 neonates who underwent brain MR imaging between 2008 and 2019. Fetal and longitudinal MR images were also reviewed. Neurologic outcomes were collected. Statistics were performed using χ2, Fisher exact, Mann-Whitney U, or t tests corrected for multiple comparisons. RESULTS: Developmental venous anomalies were detected in 1.9% of neonatal scans. These were complicated by parenchymal/ventricular abnormalities in 15/41 cases (36.6%), improving at last follow-up in 8/10 (80%), with normal neurologic outcome in 9/14 (64.2%). Multiple collectors (P = .008) and larger collector caliber (P < .001) were significantly more frequent in complicated developmental venous anomalies. At a patient level, multiplicity (P = .002) was significantly associated with the presence of ≥1 complicated developmental venous anomaly. Retrospective fetal detection was possible in 3/11 subjects (27.2%). CONCLUSIONS: One-third of neonatal developmental venous anomalies may be complicated by parenchymal abnormalities, especially with multiple and larger collectors. Neuroimaging and neurologic outcomes were favorable in most cases, suggesting a benign, self-limited nature of these vascular anomalies. A congenital origin could be confirmed in one-quarter of cases with available fetal MR imaging.

New Ultrasound Measurements to Bridge the Gap between Prenatal and Neonatal Brain Growth Assessment.

BACKGROUND AND PURPOSE: Most ultrasound markers for monitoring brain growth can only be used in either the prenatal or the postnatal period. We investigated whether corpus callosum length and corpus callosum-fastigium length could be used as markers for both prenatal and postnatal brain growth. MATERIALS AND METHODS: A 3D ultrasound study embedded in the prospective Rotterdam Periconception Cohort was performed at 22, 26 and 32 weeks' gestational age in fetuses with fetal growth restriction, congenital heart defects, and controls. Postnatally, cranial ultrasound was performed at 42 weeks' postmenstrual age. First, reliability was evaluated. Second, associations between prenatal and postnatal corpus callosum and corpus callosum-fastigium length were investigated. Third, we created reference curves and compared corpus callosum and corpus callosum-fastigium length growth trajectories of controls with growth trajectories of fetuses with fetal growth retardation and congenital heart defects. RESULTS: We included 199 fetuses; 22 with fetal growth retardation, 20 with congenital heart defects, and 157 controls. Reliability of both measurements was excellent (intraclass correlation coefficient ≥ 0.97). Corpus callosum growth trajectories were significantly decreased in fetuses with fetal growth restriction and congenital heart defects (ß = -2.295; 95% CI, -3.320-1.270; P < .01; ß = -1.267; 95% CI, -0.972-0.562; P < .01, respectively) compared with growth trajectories of controls. Corpus callosum-fastigium growth trajectories were decreased in fetuses with fetal growth restriction (ß = -1.295; 95% CI, -2.595-0.003; P = .05). CONCLUSIONS: Corpus callosum and corpus callosum-fastigium length may serve as reliable markers for monitoring brain growth from the prenatal into the postnatal period. The clinical applicability of these markers was established by the significantly different corpus callosum and corpus callosum-fastigium growth trajectories in fetuses at risk for abnormal brain growth compared with those of controls.

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.

A New Ultrasound Marker for Bedside Monitoring of Preterm Brain Growth.

BACKGROUND AND PURPOSE: Preterm neonates are at risk for neurodevelopmental impairment, but reliable, bedside-available markers to monitor preterm brain growth during hospital stay are still lacking. The aim of this study was to assess the feasibility of corpus callosum-fastigium length as a new cranial sonography marker for monitoring of preterm brain growth. MATERIALS AND METHODS: In this longitudinal prospective cohort study, cranial ultrasound was planned on the day of birth, days 1, 2, 3, and 7 of life; and then weekly until discharge in preterm infants born before 29 weeks of gestational age. Reproducibility and associations between clinical variables and corpus callosum-fastigium growth trajectories were studied. RESULTS: A series of 1-8 cranial ultrasounds was performed in 140 infants (median gestational age at birth, 27(+2) weeks (interquartile range, 26(+1) to 28(+1); 57.9% male infants). Corpus callosum-fastigium measurements showed good-to-excellent agreement for inter- and intraobserver reproducibility (intraclass correlation coefficient >0.89). Growth charts for preterm infants between 24 and 32 weeks of gestation were developed. Male sex and birth weight SD score were positively associated with corpus callosum-fastigium growth rate. CONCLUSIONS: Corpus callosum-fastigium length measurement is a new reproducible marker applicable for bedside monitoring of preterm brain growth during neonatal intensive care stay.

Neonatal disorders of germinal matrix.

The germinal matrix (GM) is a richly vascularized, transient layer near the ventricles. It produces neurons and glial cells, and is present in the foetal brain between 8 and 36 weeks of gestation. At 25 weeks, it reaches its maximum volume and subsequently withers. The GM is vulnerable to haemorrhage in preterm infants. This selective vulnerability is explained by limited astrocyte end-feet coverage of microvessels, reduced expression of fibronectin and immature tight junctions. Focal lesions in the neonatal period include haemorrhage, germinolysis and stroke. Such lesions in transient layers interrupt normal brain maturation and induce neurodevelopmental sequelae.

Developmental venous anomaly in the newborn brain.

INTRODUCTION: Cerebral developmental venous anomaly (DVA) is considered a benign anatomical variant of parenchymal venous drainage; it is the most common vascular malformation seen in the adult brain. Despite its assumed congenital origin, little is known about DVA in the neonatal brain. We report here the first cohort study of 14 neonates with DVA. METHODS: Fourteen infants (seven preterm) with DVA diagnosed neonatally using cranial ultrasound (cUS) and magnetic resonance imaging (MRI) from three tertiary neonatal units over 14 years are reviewed. RESULTS: DVA was first detected on cUS in 6 and on MRI in 8 of the 14 infants. The cUS appearances of DVA showed a focal fairly uniform area of increased echogenicity, often (86 %) adjacent to the lateral ventricle and located in the frontal lobe (58 %). Blood flow in the dilated collector vein detected by Doppler ultrasound (US) varied between cases (venous flow pattern in ten and arterialized in four). The appearance on conventional MRI was similar to findings in adults. Serial imaging showed a fairly constant appearance to the DVAs in some cases while others varied considerably regarding anatomical extent and flow velocity. CONCLUSIONS: This case series underlines that a neonatal diagnosis of DVA is possible with carefully performed cUS and MRI and that DVA tends to be an incidental finding with a diverse spectrum of imaging appearances. Serial imaging suggests that some DVAs undergo dynamic changes during the neonatal period and early infancy; this may contribute to why diagnosis is rare at this age.

Choice of diffusion tensor estimation approach affects fiber tractography of the fornix in preterm brain.

BACKGROUND AND PURPOSE: Neonatal DTI enables quantitative assessment of microstructural brain properties. Although its use is increasing, it is not widely known that vast differences in tractography results can occur, depending on the diffusion tensor estimation methodology used. Current clinical work appears to be insufficiently focused on data quality and processing of neonatal DTI. To raise awareness about this important processing step, we investigated tractography reconstructions of the fornix with the use of several estimation techniques. We hypothesized that the method of tensor estimation significantly affects DTI tractography results. MATERIALS AND METHODS: Twenty-eight DTI scans of infants born <29 weeks of gestation, acquired at 30-week postmenstrual age and without intracranial injury observed, were prospectively collected. Four diffusion tensor estimation methods were applied: 1) linear least squares; 2) weighted linear least squares; 3) nonlinear least squares, and 4) robust estimation of tensors by outlier rejection. Quality of DTI data and tractography results were evaluated for each method. RESULTS: With nonlinear least squares and robust estimation of tensors by outlier rejection, significantly lower mean fractional anisotropy values were obtained than with linear least squares and weighted linear least squares. Visualized quality of tract reconstruction was significantly higher by use of robust estimation of tensors by outlier rejection and correlated with quality of DTI data. CONCLUSIONS: Quality assessment and choice of processing methodology have considerable impact on neonatal DTI analysis. Dedicated acquisition, quality assessment, and advanced processing of neonatal DTI data must be ensured before performing clinical analyses, such as associating microstructural brain properties with patient outcome.

Optimal timing of cerebral MRI in preterm infants to predict long-term neurodevelopmental outcome: a systematic review.

SUMMARY: Advances in neonatal neuroimaging have improved detection of preterm brain injury responsible for abnormal neuromotor and cognitive development. Increasingly sophisticated MR imaging setups allow scanning during early preterm life. In this review, we investigated how brain MR imaging in preterm infants should be timed to best predict long-term outcome. Given the strong evidence that structural brain abnormalities are related to long-term neurodevelopment, MR imaging should preferably be performed at term-equivalent age. Early MR imaging is promising because it can guide early intervention studies and is indispensable in research on preterm brain injury.

Automated artifact removal as preprocessing refines neonatal seizure detection.

OBJECTIVE: The description and evaluation of algorithms using Independent Component Analysis (ICA) for automatic removal of ECG, pulsation and respiration artifacts in neonatal EEG before automated seizure detection. METHODS: The developed algorithms decompose the EEG using ICA into its underlying sources. The artifact source was identified using the simultaneously recorded polygraphy signals after preprocessing. The EEG was reconstructed without the corrupting source, leading to a clean EEG. The impact of the artifact removal was measured by comparing the performance of a previously developed seizure detector before and after the artifact removal in 13 selected patients (9 having artifact-contaminated and 4 having artifact-free EEGs). RESULTS: A significant decrease in false alarms (p=0.01) was found while the Good Detection Rate (GDR) for seizures was not altered (p=0.50). CONCLUSIONS: The techniques reduced the number of false positive detections without lowering sensitivity and are beneficial in long term EEG seizure monitoring in the presence of disturbing biological artifacts. SIGNIFICANCE: The proposed algorithms improve neonatal seizure monitoring.

Validation of a new automated neonatal seizure detection system: a clinician's perspective.

OBJECTIVE: To validate an improved automated electroencephalography (EEG)-based neonatal seizure detection algorithm (NeoGuard) in an independent data set. METHODS: EEG background was classified into eight grades based on the evolution of discontinuity and presence of sleep-wake cycles. Patients were further sub-classified into two groups; gpI: mild to moderate (grades 1-5) and gpII: severe (grades 6-8) EEG background abnormalities. Seizures were categorised as definite and dubious. Seizure characteristics were compared between gpI and gpII. The algorithm was tested on 756 h of EEG data from 24 consecutive neonates (median 25 h per patient) with encephalopathy and recorded seizures during continuous monitoring (cEEG). No selection was made regarding the quality of EEG or presence of artefacts. RESULTS: Seizure amplitudes significantly decreased with worsening EEG background. Seizures were detected with a total sensitivity of 61.9% (1285/2077). The detected seizure burden was 66,244/97,574 s (67.9%). Sensitivity per patient was 65.9%, with a mean positive predictive value (PPV) of 73.7%. After excluding four patients with severely abnormal EEG background, and predominantly having dubious seizures, the algorithm showed a median sensitivity per patient of 86.9%, PPV of 89.5% and false positive rate of 0.28 h(-1). Sensitivity tended to be better for patients in gpI. CONCLUSIONS: The algorithm detects neonatal seizures well, has a good PPV and is suited for cEEG monitoring. Changes in electrographic characteristics such as amplitude, duration and rhythmicity in relation to deteriorating EEG background tend to worsen the performance of automated seizure detection. SIGNIFICANCE: cEEG monitoring is important for detecting seizures in the neonatal intensive care unit (NICU). Our automated algorithm reliably detects neonatal seizures that are likely to be clinically most relevant, as reflected by the associated EEG background abnormality.

Time varying neonatal seizure localization.

BACKGROUND: A common cause for damage to the neonatal brain is a shortage in the oxygen supply to the brain or asphyxia. Neonatal seizures are the most frequent manifestation of neonatal neurologic disorders. Multichannel EEG recordings allow topographic localization of seizure foci. OBJECTIVES: We want to objectively determine the spatial distribution of the seizure on the scalp, the location in time and order the dominant sources in the brain based on their strength. METHODS: In this paper we combine a method based on higher order CP-decomposition with subsequent singular value decomposition (SVD). RESULTS: We illustrate the abilities of the method on simulated as well as on real neonatal seizure EEG. CONCLUSIONS: The proposed method provides reliable time and spatial information about the seizure, gives a clear overview of what is going on in the EEG and allows easy interpretation.

Neonatal seizure localization using PARAFAC decomposition.

OBJECTIVE: The description and evaluation of two EEG-based algorithms for automatic and objective determination of the seizure location in the neonatal brain as it is reflected on the scalp. METHODS: Each algorithm extracts the electrical potential distribution of the seizure over the scalp using the higher-order canonical decomposition or Parallel Factor Analysis (PARAFAC), also referred to as the CP model. This model decomposes a tensor in a sum of rank-1 components. The two algorithms differ in the way the tensor is constructed and in the type of activity they are able to extract. While the first method extracts oscillatory seizure activity, the second extracts spike train activity. RESULTS: We compared the seizure localization results of 21 seizures from 6 neonates with post-asphyxial hypoxic ischemic encephalopathy, with that based on the visual analysis of the EEG by a clinical neurophysiologist. There was a good agreement between the two methods in the localization of seizure onset in all. CONCLUSION: The techniques presented in this paper are robust, objective methods to determine neonatal seizure localization. They can be a useful tool for neonatal EEG analysis and for continuous brain function monitoring. SIGNIFICANCE: The proposed algorithms significantly improve neonatal seizure localization and monitoring.

Diagnosis of perinatal stroke II: mechanisms and clinical phenotypes.

INTRODUCTION: Here (and in an accompanying article dealing with definitions, differential diagnosis and registration), a structured sequential diagnostic flow is proposed to discern clinical phenotypes for perinatal stroke, including arterial ischaemic stroke (AIS), cerebral sinovenous thrombosis (CSVT) and haemorrhagic stroke. MATERIAL AND RESULTS: For neonatal AIS, the diagnostic sequence is infection, trauma, embolism, arteriopathy, other, primary thrombosis and unclassifiable; for neonatal CSVT, the sequence is infection, trauma, venopathy, other, primary thrombosis and unclassifiable. The proposed hierarchical diagnostic flows are an initial step towards a standard for registration of the causes of neonatal stroke. Such standardization should guide attempts at prevention and intervention. An extensive literature search and study of a retrospective cohort of 134 newborn infants with stroke suggest that embolism is the most common identifiable cause for stroke in general (25%), preceding trauma (10%) and infection (8%). Other causes, such as asphyxia, acute blood loss, extracorporeal membrane oxygenation, genetic disorders or prothrombotic conditions, are seen in <5% of cases. For neonatal AIS, the presence of an embolic phenotype is 33% in this cohort. The designation unclassifiable scored 34% for the entire stroke group and 25% for neonatal AIS. Complex arterial stroke with multiple arteries involved is often seen when the underlying cause is infection, cranial trauma or embolism. One important conclusion is that a means of prevention is avoidance of embolism from thrombosis outside the brain. CONCLUSION: To prevent the occurrence and recurrence of neonatal ischaemic stroke, clinicians must develop a standardized diagnostic approach that results in characterization of the clinical phenotype.

Diagnosis of perinatal stroke I: definitions, differential diagnosis and registration.

INTRODUCTION: Perinatal stroke can be divided into three subtypes: ischaemic stroke, either arterial or sinovenous and haemorrhagic stroke. For the sake of universal registration and to perform intervention studies, we propose a detailed diagnostic registration system for perinatal stroke taking 10 variables into account. These variables are discussed here and in the accompanying article. MATERIAL AND RESULTS: Differentiation is needed from focal brain changes as a result of disorders other than stroke, whereby accurate timing is possible only when early neonatal imaging is available. Detailed templates are presented for arterial and venous vascular classification. AIS is further subdivided into single territory and complex infarction and some stratification is proposed in the complicated stroke group. This registration system has been applied to a retrospective cohort of 134 newborns with stroke (single-centre observation from 1999 to 2007) and the results are compared with published data. By applying this registration system, intervention studies for one homogeneous stroke type (e.g. complete middle cerebral artery stroke) may be facilitated. CONCLUSION: Ten variables may be sufficient to register a perinatal stroke. These include gestational age, birthweight, gender, delivery mode, time of detection, presentation, type of stroke, vessel affected or type of cavity, imaging method at detection and clinical context.

Evolution of unilateral perinatal arterial ischemic stroke on conventional and diffusion-weighted MR imaging.

BACKGROUND AND PURPOSE: Knowledge of the sequence of signal-intensity (SI) changes on conventional and diffusion-weighted MR imaging (DWI) following perinatal arterial ischemic stroke (PAIS) is limited, adding to the difficulty in timing the onset of PAIS. We hypothesized that SI changes seen on early sequential MR imaging following PAIS should follow a similar time course. The aim of this study was to evaluate the time course of SI changes by using a simple classification that could be assessed visually from conventional imaging and DWI in term-born neonates with symptomatic unilateral PAIS. MATERIALS AND METHODS: Infants > or =36 weeks gestation with unilateral PAIS in the territory of a main cerebral artery with a first MR imaging performed within the first postnatal month were included in this study. All subsequent scans up to 3 months postnatal age were also evaluated. For the conventional MR imaging scans, a visual SI scoring system was used (-1 = lower, 0 = equal, 1 = higher) compared with the contralesional hemisphere. For the DWIs, SI of the infarcted tissue was classified into the 3 groups: 1) severe hyperintensity (HI), 2) moderate and mild HI, and 3) no HI. RESULTS: We analyzed 43 scans (mean age at first scanning, 4 days) from 21 term infants. Changes in SI on conventional T1 and T2 images were remarkably consistent among infants. The cortex was of low SI on T1 and high SI on T2 until day 6 when SIs reversed and cortical highlighting was seen for 1-2 months. The white matter was high SI on T1 in the first 8-9 days and on T2 for >2 weeks before becoming low SI. Secondary SI changes remote from the infarction were seen in the thalamus and brain stem in the first week, and atrophy was seen after 4 weeks. All DWIs showed high SI of the affected region until at least day 4, which fell to equal or below that of the contralesional hemisphere by day 12. CONCLUSIONS: The pattern of SI change on conventional imaging and DWI following PAIS was remarkably consistent among patients, suggesting that PAIS in symptomatic term-born infants occurs within a very limited timeframe around birth.

Automated neonatal seizure detection mimicking a human observer reading EEG.

OBJECTIVE: The description and evaluation of a novel patient-independent seizure detection for the EEG of the newborn term infant. METHODS: We identified characteristics of neonatal seizures by which a human observer is able to detect them. Neonatal seizures were divided into two types. For each type, a fully automated detection algorithm was developed based on the identified human observer characteristics. The first algorithm analyzes the correlation between high-energetic segments of the EEG. The second detects increases in low-frequency activity (<8 Hz) with high autocorrelation. RESULTS: The complete algorithm was tested on multi-channel EEG recordings of 21 patients with and 5 patients without electrographic seizures, totaling 217 h of EEG. Sensitivity of the combined algorithms was found to be 88%, Positive Predictive Value (PPV) 75% and the false positive rate 0.66 per hour. CONCLUSIONS: Our approach to separate neonatal seizures into two types yields a high sensitivity combined with a good PPV and much lower false positive rate than previously published algorithms. SIGNIFICANCE: The proposed algorithm significantly improves neonatal seizure detection and monitoring.