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In this paper, we introduce a new variation of the Convolutional Neural Network Inception block, called Sinc, for sleep stage classification in premature newborn babies using electroencephalogram (EEG). In practice, there are many medical centres where only a limited number of EEG channels are recorded. Existing automated algorithms mainly use multi-channel EEGs which perform poorly when fewer numbers of channels are available. The proposed Sinc utilizes multi-scale analysis to place emphasis on the temporal EEG information to be less dependent on the number of EEG channels. In Sinc, we increase the receptive fields through Inception while by additionally sharing the filters that have similar receptive fields, overfitting is controlled and the number of trainable parameters dramatically reduced. To train and test this model, 96 longitudinal EEG recordings from 26 premature infants are used. The Sinc-based model significantly outperforms state-of-the-art neonatal quiet sleep detection algorithms, with mean Kappa 0.77 ± 0.01 (with 8-channel EEG) and 0.75 ± 0.01 (with a single bipolar channel EEG). This is the first study using Inception-based networks for EEG analysis that utilizes filter sharing to improve efficiency and trainability. The suggested network can successfully detect quiet sleep stages with even a single EEG channel making it more practical especially in the hospital setting where cerebral function monitoring is predominantly used. ispartof: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS vol:26 issue:3 pages:1023-1033 ispartof: location:United States status: published

Introduction: Suprasellar tuberculoma are extremely rare in children and most of those patients present with headache, vomiting, visual disturbances, and hypofunction of the pituitary gland. In this case report, we present a girl with tuberculosis, who developed significant weight gain in combination with pituitary dysfunction, which recovered after antituberculosis treatment. Case presentation: An 11-year old girl presented with headache, fever and anorexia that progressively evolved into an encephalopathic status with cranial nerves III and VI paresis. Brain MRI showed meningeal contrast capture along cranial nerves II (including optic chiasm), III, V and VI bilaterally and multiple contrast enhancing brain parenchyma lesions. Tuberculin skin test was negative but interferon-gamma release assay was positive. The clinical and radiological working diagnosis was consistent with tuberculous meningoencephalitis. Pulse corticosteroids for three days and quadruple antituberculosis therapy were started and the girl demonstrated obvious improvement of her neurological symptoms. However, after a few months of therapy she developed remarkable weight gain (+20 kg in one year) and growth arrest. Her hormone profile revealed insulin resistance (homeostasis model assessment-estimated insulin resistance (HOMA-IR) 6.8) despite putative growth hormone deficiency (circulating insulin-like growth factor-I (IGF-I) 104 μg/L (-2.4 SD)). Follow-up brain MRI showed a decrease in basal meningitis, but increased parenchymal lesions in the suprasellar region extending medially into the nucleus lentiformis, with now a voluminous tuberculoma at this site. Antituberculosis treatment was continued for a total of 18 months. The patient improved clinically, she regained her pre-illness Body Mass Index (BMI) SDS and her growth rate increased slightly. On the hormonal side, disappearance of insulin resistance (HOMA-IR 2.5) and an increase in IGF-I (175 μg/L, -1.4 SD) was noted, and her last brain MRI showed a remarkable volume reduction of the suprasellar tuberculoma. Discussion/Conclusion: Suprasellar tuberculoma can have a very dynamic presentation during the active stage of the disease, which can be reversed by prolonged antituberculosis treatment. Previous studies showed that the tuberculous process can also cause long term and irreversible changes in the hypothalamic-pituitary axis. Prospective studies are however needed in the pediatric population to know the exact incidence and type of pituitary dysfunction.

Neonates with Congenital Heart Disease (CHD) have structural delays in brain development. To evaluate whether functional brain maturation and sleep-wake physiology is also disturbed, the Functional Brain Age (FBA) and sleep organisation on EEG during the neonatal period is investigated.We compared 15 neonates with CHD who underwent multichannel EEG with healthy term newborns of the same postmenstrual age, including subgroup analysis for d-Transposition of the Great Arteries (d-TGA) (n = 8). To estimate FBA, a prediction tool using quantitative EEG features as input, was applied. Second, the EEG was automatically classified into the 4 neonatal sleep stages. Neonates with CHD underwent neurodevelopmental testing using the Bayley Scale of Infant Development-III at 24 months.Preoperatively, the FBA was delayed in CHD infants and more so in d-TGA infants. The FBA was positively correlated with motor scores. Sleep organisation was significantly altered in neonates with CHD. The duration of the sleep cycle and the proportion of Active Sleep Stage 1 was decreased, again more marked in the d-TGA infants. Neonates with d-TGA spent less time in High Voltage Slow Wave Sleep and more in Tracé Alternant compared to healthy terms. Both FBA and sleep organisation normalised postoperatively. The duration of High Voltage Slow Wave Sleep remained positively correlated with motor scores in d-TGA infants.Altered early brain function and sleep is present in neonates with CHD. These results are intruiging, as inefficient neonatal sleep has been linked with adverse long-term outcome. Identifying how these rapid alterations in brain function are mitigated through improvements in cerebral oxygenation, surgery, drugs and nutrition may have relevance for clinical practice and outcome.

The preterm neonate can experience stressors that affect the rate of brain maturation and lead to long-term neurodevelopmental deficits. However, some neonates who are born early follow normal developmental trajectories. Extraction of data from electroencephalography (EEG) signals can be used to calculate the neonate’s brain age which can be compared to their true age. Discrepancies between true age and brain age (the brain age delta) can then be used to quantify maturational deviation, which has been shown to correlate with long-term abnormal neurodevelopmental outcomes. Nevertheless, current brain age models that are based on traditional analytical techniques are less suited to clinical cot-side monitoring due to their dependency on long-duration EEG recordings, the need to record activity across multiple EEG channels, and the manual calculation of predefined EEG features which is time-consuming and may not fully capture the wealth of information in the EEG signal. In this study, we propose an alternative deep-learning approach to determine brain age, which operates directly on the EEG, using a Convolutional Neural Network (CNN) block based on the Inception architecture (called Sinc). Using this deep-learning approach on a dataset of preterm infants with normal neurodevelopmental outcomes (where we assume brain age = postmenstrual age), we can calculate infant brain age with a Mean Absolute Error (MAE) of 0.78 weeks (equivalent to a brain age estimation error for the infant within +/− 5.5 days of their true age). Importantly, this level of accuracy can be achieved by recording only 20 minutes of EEG activity from a single channel. This compares favourably to the degree of accuracy that can be achieved using traditional methods that require long duration recordings (typically >2 hours of EEG activity) recorded from a higher density 8-electrode montage (MAE = 0.73 weeks). Importantly, the deep learning model’s brain age deltas also distinguish between neonates with normal and severely abnormal outcomes (Normal MAE = 0.71 weeks, severely abnormal MAE = 1.27 weeks, p=0.02, one-way ANOVA), making it highly suited for potential clinical applications. Lastly, in an independent dataset collected at an independent site, we demonstrate the model’s generalisability in age prediction, as accurate age predictions were also observed (MAE of 0.97 weeks).HighlightsPreterm stress exposure leads to long-term neurodevelopmental deficitsDeficits are quantifiable using EEG-based brain age prediction errorsOur deep-learning solution for brain age prediction outperforms previous approachesPredictions are achieved with only 20 mins EEG and a single bipolar channelPrediction errors correlate with long-term Bayley scale neurodevelopmental outcomes

AIM: After preterm birth, supine head midline position is supported for stable cerebral blood flow (CBF) and prevention of intraventricular haemorrhage (IVH), while prone position supports respiratory function and enables skin-to-skin care. The prone compared to supine position could lead to a change in near-infrared derived cerebral tissue oxygen saturation (rScO2), which is a surrogate for cerebral blood flow (CBF). By monitoring rScO2 neonatologists aim to stabilise CBF during intensive care and prevent brain injury. In this systematic review and meta-analysis, we investigate the effect of the body position on rScO2. METHODS: A comprehensive literature search was performed to identify all trials that included preterm infants in the first 2 weeks after birth and compared rScO2 in the prone versus supine head in midline position of the infant. A meta-analysis, including two subgroup analyses based on postnatal age (PNA) and gestational age (GA), was performed. RESULTS: Six observational cohort studies wereincluded. In the second, but not the first week after birth, a significant higher rScO2 in the prone position was found with a mean difference of 1.97% (95% CI 0.87-3.07). No rScO2 difference was observed between positions in the extremely preterm nor the preterm group. CONCLUSION: No consistent evidence was found that body position influences rScO2 in the first 2 weeks after preterm birth. Subgroup analysis suggests that in the second week after birth, the prone position might result in higher cerebral rScO2 than the supine position with head in midline. Multiple factors determine the best body position in preterms.

Brain monitoring is important in neonates with asphyxia in order to assess the severity of hypoxic ischaemic encephalopathy (HIE) and identify neonates at risk of adverse neurodevelopmental outcome. Previous studies suggest that neurovascular coupling (NVC), quantified as the interaction between electroencephalography (EEG) and near-infrared spectroscopy (NIRS)-derived regional cerebral oxygen saturation (rSO

Family firms are one of the most ubiquitous forms of business organizations worldwide. Their survival and growth are thus not only crucial for the firms themselves but also for the overall economy. One of the factors that influence their survival and development are their financing decisions. These decisions are generally described through the pecking order theory. However, not much is known about the applicability of this theory in private family firms. Given the shortcomings (both theoretically and empirically) of the current literature, we analyze 1087 incremental financing decisions from 277 family firms to develop and test a specific family firm pecking order. We integrate the elements of the socioemotional wealth perspective to theoretically explain the preferred order and introduce family capital into the pecking order model. Our findings indicate that family firms first prefer internal financing, next debt financing, followed by family capital, and last external capital. We also find that SEW considerations play a role in this financing decision. Especially the retention of control over the firm and the aim to pass the firm to the next generation appear to play an important role in determining this order. These dimensions ensure that family firms try to avoid extra capital. However, when it is needed, they will opt for family capital over external capital. This paper thus provides more insight into the reasoning behind financing decisions in private family firms.

Thyroid hormones play an essential role in central nervous system development, normal physiological brain function, and repair mechanisms. On one hand, thyroid hormone alterations influence cortical excitability, and on the other hand antiseizure medications (ASMs) are associated with alterations in thyroid hormone metabolism. Although this interaction has long been described, and epilepsy is a common and chronic neurological disease, studies describing the interplay are often small and retrospective. We performed a systematic review of the current literature on epilepsy, ASMs, and thyroid hormone metabolism according to PRISMA guidelines. Forty-seven studies were included. Most studies were retrospective cross-sectional studies (n = 25) and investigated thyroid function alterations in patients on older ASMs such as phenobarbital, phenytoin, carbamazepine, and valproate. Overall, almost one third of patients with epilepsy had thyroid hormone alterations, especially patients on valproate (25%) and carbamazepine (10%-25%). Studies with patients receiving polytherapy are scarce, but reported a higher risk for hypothyroidism in patients with older age (p = .004), female sex (p = .014), longer duration of epilepsy (p = .001), intractable epilepsy (p = .009), and polytherapy. Studies on newer ASMs are also limited, and further studies on an interplay with thyroid hormone homeostasis are essential to improve the care for epilepsy patients. ASMs are associated with alterations in thyroid hormone metabolism. Thyroid function monitoring is indicated in patients on ASMs, especially those with refractory epilepsy and those on polytherapy. We provide a practical guidance for thyroid function monitoring for the clinician taking care of patients on ASMs.

OBJECTIVE: Patients with absence epilepsy sensitivity

Encephalitis and encephalopathy in children represent a diagnostic challenge. We describe a patient with relapsing encephalitis in whom the differential diagnosis included acute disseminated encephalomyelitis, human herpesvirus 6 encephalitis, and hemophagocytic lymphohistiocytosis (HLH). Because of its rarity, HLH is often overlooked as a differential diagnosis in encephalitis, especially in the isolated CNS forms. As this case illustrates, inborn errors of immunity can underlie isolated encephalitis and should be included in the differential diagnosis of these presentations. ispartof: Neurology vol:99 issue:15 pages:660-664 ispartof: location:United States status: published

OBJECTIVE: To determine the incidence and characterise corpus callosum (CC) abnormalities in fetuses with spina bifida aperta (SBA) between 18 and 26 weeks of gestation. METHODS: This was a retrospective study on fetuses with isolated SBA and who were assessed for fetal surgery. Digitally stored ultrasound images of the brain were reviewed for the presence/absence of the CC, and the length and diameter of its constituent parts (rostrum, genu, body and splenium). We used regression analysis to determine the relationship between CC abnormalities and gestational age, head circumference, ventricle size, lesion level and lesion type. RESULTS: Nearly three-quarters of fetuses with isolated SBA had an abnormal CC (71.7%, 76/106). Partial agenesis was most common in the splenium (18.9%, 20/ 106) and the rostrum (13.2%, 14/106). The most common abnormal pattern was of a short CC with normal diameter throughout. Of note, 20.8% (22/106) had a hypoplastic genu and 28.3% (30/106) had a thick body part. Larger lateral ventricle size was associated with partial agenesis of the CC (odds ratio [OR]: 0.14, p < 0.001) and inversely associated with a shorter CC (OR: 2.60, p < 0.01). CONCLUSION: An abnormal CC is common in fetuses with isolated SBA who are referred for fetal surgery.

Nicolaides–Baraitser syndrome (NCBRS), caused by a mutation in the SMARCA2 gene, which goes along with intellectual disability, congenital malformations, especially of face and limbs, and often difficult-to-treat epilepsy, is surveyed focusing on epilepsy and its treatment. Patients were recruited via “Network Therapy of Rare Epilepsies (NETRE)” and an international NCBRS parent support group. Inclusion criterion is NCBRS-defining SMARCA2 mutation. Clinical findings including epilepsy classification, anticonvulsive treatment, electroencephalogram (EEG) findings, and neurodevelopmental outcome were collected with an electronic questionnaire. Inclusion of 25 NCBRS patients with epilepsy in 23 of 25. Overall, 85% of the participants (17/20) reported generalized seizures, the semiology varied widely. EEG showed generalized epileptogenic abnormalities in 53% (9/17), cranial magnetic resonance imaging (cMRI) was mainly inconspicuous. The five most frequently used anticonvulsive drugs were valproic acid (VPA [12/20]), levetiracetam (LEV [12/20]), phenobarbital (PB [8/20]), topiramate (TPM [5/20]), and carbamazepine (CBZ [5/20]). LEV (9/12), PB (6/8), TPM (4/5), and VPA (9/12) reduced the seizures' frequency in more than 50%. Temporary freedom of seizures (>6 months) was reached with LEV (4/12), PB (3/8), TPM (1/5, only combined with PB and nitrazepam [NZP]), and VPA (4/12). Seizures aggravation was observed under lamotrigine (LTG [2/4]), LEV (1/12), PB (1/8), and VPA (1/12). Ketogenic diet (KD) and vagal nerve stimulation (VNS) reduced seizures' frequency in one of two each. This first worldwide retrospective analysis of anticonvulsive therapy in NCBRS helps to treat epilepsy in NCBRS that mostly shows only initial response to anticonvulsive therapy, especially with LEV and VPA, but very rarely shows complete freedom of seizures in this, rather genetic than structural epilepsy.

Stress exposure and early-pain procedure might increase the level of EEG and HRV dismaturity, which can ultimately impact the development outcome of the infant., Preterm infants show a higher incidence of cognitive, social, and behavioral problems, even in the absence of major medical complications during their stay in the neonatal intensive care unit (NICU). Several authors suggest that early-life experience of stress and procedural pain could impact cerebral development and maturation resulting in an altered development of cognition, behavior, or motor patterns in later life. However, it remains very difficult to assess this impact of procedural pain on physiological development. This study describes the maturation of electroencephalogram (EEG) signals and heart rate variability in a prospective cohort of 92 preterm infants (

Premature babies are subjected to environmental stresses that can affect brain maturation and cause abnormal neurodevelopmental outcome later in life. Better understanding this link is crucial to developing a clinical tool for early outcome estimation. We defined maturational trajectories between the Electroencephalography (EEG)-derived ‘brain-age’ and postmenstrual age (the age since the last menstrual cycle of the mother) from longitudinal recordings during the baby’s stay in the Neonatal Intensive Care Unit. Data consisted of 224 recordings (65 patients) separated for normal and abnormal outcome at 9–24 months follow-up. Trajectory deviations were compared between outcome groups using the root mean squared error (RMSE) and maximum trajectory deviation (δmax). 113 features were extracted (per sleep state) to train a data-driven model that estimates brain-age, with the most prominent features identified as potential maturational and outcome-sensitive biomarkers. RMSE and δmax showed significant differences between outcome groups (cluster-based permutation test, p RMSE had a median (IQR) of 0.75 (0.60–1.35) weeks for normal outcome and 1.35 (1.15–1.55) for abnormal outcome, while δmax had a median of 0.90 (0.70–1.70) and 1.90 (1.20–2.90) weeks, respectively. Abnormal outcome trajectories were associated with clinically defined dysmature and disorganised EEG patterns, cementing the link between early maturational trajectories and neurodevelopmental outcome.

Encephalitis and encephalopathy in children represent a diagnostic challenge. We describe a patient with relapsing encephalitis in whom the differential diagnosis included acute disseminated encephalomyelitis (ADEM), human herpesvirus 6 (HHV-6) encephalitis, and hemophagocytic lymphohistiocytosis (HLH). Because of its rarity, HLH is often overlooked as a differential diagnosis in encephalitis, especially in the isolated central nervous system (CNS) forms. As this case illustrates, inborn errors of immunity (IEIs) can underlie isolated encephalitis and should be included in the differential diagnosis of these presentations.

Objective. Automated artefact detection in the neonatal electroencephalogram (EEG) is crucial for reliable automated EEG analysis, but limited availability of expert artefact annotations challenges the development of deep learning models for artefact detection. This paper proposes a semi-supervised deep learning approach for artefact detection in neonatal EEG that requires few labelled data by training a multi-task convolutional neural network (CNN). Approach. An unsupervised and a supervised objective were jointly optimised by combining an autoencoder and an artefact classifier in one multi-output model that processes multi-channel EEG inputs. The proposed semi-supervised multi-task training strategy was compared to a classical supervised strategy and other existing state-of-the-art models. The models were trained and tested separately on two different datasets, which contained partially annotated multi-channel neonatal EEG. Models were evaluated using the F1-statistic and the relevance of the method was investigated in the context of a functional brain age (FBA) prediction model. Main results. The proposed multi-task and multi-channel CNN methods outperformed state-of-the-art methods, reaching F1 scores of 86.2% and 95.7% on two separate datasets. The proposed semi-supervised multi-task training strategy was shown to be superior to a classical supervised training strategy when the amount of labels in the dataset was artificially reduced. Finally, we found that the error of a brain age prediction model correlated with the amount of automatically detected artefacts in the EEG segment. Significance. Our results show that the proposed semi-supervised multi-task training strategy can train CNNs successfully even when the amount of labels in the dataset is limited. Therefore, this method is a promising semi-supervised technique for developing deep learning models with scarcely labelled data. Moreover, a correlation between the error of FBA estimates and the amount of detected artefacts in the corresponding EEG segments indicates the relevance of artefact detection for robust automated EEG analysis.

OBJECTIVE: Our primary goal was to measure the accuracy of fully automated absence seizure detection, using a wearable electroencephalographic (EEG) device. As a secondary goal, we also tested the feasibility of automated behavioral testing triggered by the automated detection. METHODS: We conducted a phase 3 clinical trial (NCT04615442), with a prospective, multicenter, blinded study design. The input was the one-channel EEG recorded with dry electrodes embedded into a wearable headband device connected to a smartphone. The seizure detection algorithm was developed using artificial intelligence (convolutional neural networks). During the study, the predefined algorithm, with predefined cutoff value, analyzed the EEG in real time. The gold standard was derived from expert evaluation of simultaneously recorded full-array video-EEGs. In addition, we evaluated the patients' responsiveness to the automated alarms on the smartphone, and we compared it with the behavioral changes observed in the clinical video-EEGs. RESULTS: We recorded 102 consecutive patients (57 female, median age = 10 years) on suspicion of absence seizures. We recorded 364 absence seizures in 39 patients. Device deficiency was 4.67%, with a total recording time of 309 h. Average sensitivity per patient was 78.83% (95% confidence interval [CI] = 69.56%-88.11%), and median sensitivity was 92.90% (interquartile range [IQR] = 66.7%-100%). The average false detection rate was .53/h (95% CI = .32-.74). Most patients (n = 66, 64.71%) did not have any false alarms. The median F1 score per patient was .823 (IQR = .57-1). For the total recording duration, F1 score was .74. We assessed the feasibility of automated behavioral testing in 36 seizures; it correctly documented nonresponsiveness in 30 absence seizures, and responsiveness in six electrographic seizures. SIGNIFICANCE: Automated detection of absence seizures with a wearable device will improve seizure quantification and will promote assessment of patients in their home environment. Linking automated seizure detection to automated behavioral testing will provide valuable information from wearable devices. ispartof: EPILEPSIA ispartof: location:United States status: published