Person:

Chu, Catherine

Gangliosidoses are a group of rare lysosomal storage diseases (LySD) involving the accumulation of lipids in multiple organ systems, including the central and peripheral nervous systems. These disorders are inherited in an autosomal recessive pattern and are broadly grouped into 2 types. GM1 gangliosidoses (GM1) are due to a deficiency of the enzyme β-galactosidase, and GM2 diseases (Tay-Sachs, AB variant, and Sandhoff disease) are due to a deficiency of the enzyme β-hexosaminidase. GM1, first described biochemically by Dr. John S. O'Brien in the 1960s, is estimated to occur in 1 in 100,000–200,000 newborns.1 Despite being the first of the gangliosidoses identified as well as the most prevalent, GM1 often presents a diagnostic challenge to the child neurologist. GM1 type 1, the infantile form, is the most common and the most severe. Patients present with nonspecific neurologic features including hypotonia, sensory impairment, and developmental regression within the first year of life; thus, a broad differential diagnosis is often entertained. We report a complex case of infantile GM1 diagnosed following identification of an extensive dermal melanocytosis on physical examination. This distinguishing feature has been increasingly recognized as a harbinger of GM1.2,–5 In rare disorders where expensive genetic testing and invasive procedures are often used to reach a diagnosis, recognition of unique clinical features on the physical examination can be a great asset.

Background: The electroencephalogram (EEG) remains the primary tool for diagnosis of abnormal brain activity in clinical neurology and for in vivo recordings of human neurophysiology in neuroscience research. In EEG data acquisition, voltage is measured at positions on the scalp with respect to a reference electrode. When this reference electrode responds to electrical activity or artifact all electrodes are affected. Successful analysis of EEG data often involves re-referencing procedures that modify the recorded traces and seek to minimize the impact of reference electrode activity upon functions of the original EEG recordings.

New method: We provide a novel, statistically robust procedure that adapts a robust maximum-likelihood type estimator to the problem of reference estimation, reduces the influence of neural activity from the re-referencing operation, and maintains good performance in a wide variety of empirical scenarios.

Results: The performance of the proposed and existing re-referencing procedures are validated in simulation and with examples of EEG recordings. To facilitate this comparison, channel-to-channel correlations are investigated theoretically and in simulation.

Comparison with existing methods: The proposed procedure avoids using data contaminated by neural signal and remains unbiased in recording scenarios where physical references, the common average reference (CAR) and the reference estimation standardization technique (REST) are not optimal.

Conclusion:The proposed procedure is simple, fast, and avoids the potential for substantial bias when analyzing low-density EEG data.

Functional connectivity networks have become a central focus in neuroscience because they reveal key higher-dimensional features of normal and abnormal nervous system physiology. Functional networks reflect activity-based coupling between brain regions that may be constrained by relatively static anatomical connections, yet these networks appear to support tremendously dynamic behaviors. Within this growing field, the stability and temporal characteristics of functional connectivity brain networks have not been well characterized. We evaluated the temporal stability of spontaneous functional connectivity networks derived from multi-day scalp encephalogram (EEG) recordings in five healthy human subjects. Topological stability and graph characteristics of networks derived from averaged data epochs ranging from 1 s to multiple hours across different states of consciousness were compared. We show that, although functional networks are highly variable on the order of seconds, stable network templates emerge after as little as ∼100 s of recording and persist across different states and frequency bands (albeit with slightly different characteristics in different states and frequencies). Within these network templates, the most common edges are markedly consistent, constituting a network “core.” Although average network topologies persist across time, measures of global network connectivity, density and clustering coefficient, are state and frequency specific, with sparsest but most highly clustered networks seen during sleep and in the gamma frequency band. These findings support the notion that a core functional organization underlies spontaneous cortical processing and may provide a reference template on which unstable, transient, and rapidly adaptive long-range assemblies are overlaid in a frequency-dependent manner.

Background: Autism spectrum disorders (ASD) are increasingly prevalent and have a significant impact on the lives of patients and their families. Currently, the diagnosis is determined by clinical judgment and no definitive physiological biomarker for ASD exists. Quantitative biomarkers obtainable from clinical neuroimaging data – such as the scalp electroencephalogram (EEG) - would provide an important aid to clinicians in the diagnosis of ASD. The interpretation of prior studies in this area has been limited by mixed results and the lack of validation procedures. Here we use retrospective clinical data from a well-characterized population of children with ASD to evaluate the rhythms and coupling patterns present in the EEG to develop and validate an electrophysiological biomarker of ASD. Methods: EEG data were acquired from a population of ASD (n = 27) and control (n = 55) children 4–8 years old. Data were divided into training (n = 13 ASD, n = 24 control) and validation (n = 14 ASD, n = 31 control) groups. Evaluation of spectral and functional network properties in the first group of patients motivated three biomarkers that were computed in the second group of age-matched patients for validation. Results: Three biomarkers of ASD were identified in the first patient group: (1) reduced posterior/anterior power ratio in the alpha frequency range (8–14 Hz), which we label the “peak alpha ratio”, (2) reduced global density in functional networks, and (3) a reduction in the mean connectivity strength of a subset of functional network edges. Of these three biomarkers, the first and third were validated in a second group of patients. Using the two validated biomarkers, we were able to classify ASD subjects with 83 % sensitivity and 68 % specificity in a post-hoc analysis. Conclusions: This study demonstrates that clinical EEG can provide quantitative biomarkers to assist diagnosis of autism. These results corroborate the general finding that ASD subjects have decreased alpha power gradients and network connectivities compared to control subjects. In addition, this study demonstrates the necessity of using statistical techniques to validate EEG biomarkers identified using exploratory methods. Electronic supplementary material The online version of this article (doi:10.1186/s12883-015-0355-8) contains supplementary material, which is available to authorized users.

Epilepsy is a common neurological disorder in the pediatric population, affecting up to one percent of children, and for which the mainstay of treatment is anticonvulsant medication. Despite the frequent use of anticonvulsant drugs, remarkably little is known about the safety and efficacy of most of these medications in the pediatric epilepsy population. Of 34 anticonvulsants currently approved for use by the US Food and Drug Administration (FDA), only 13 have been approved for use in children. Although infants and young children are disproportionately affected by epilepsy, there are currently only three anticonvulsant medications that have been specifically evaluated and approved for use in children younger than 2 years of age. In 2012, the FDA approved levetiracetam as an adjunctive treatment for partial onset seizures in infants and children from one month of age. Here we review the available data on levetiracetam in the pediatric epilepsy population. We first discuss the pharmacological profile of levetiracetam, including its mechanism of action, formulations and dosing, and pharmacokinetics in children. We then review the available efficacy, safety, and tolerability data in children from one month of age with partial onset seizures. We conclude that the current data leading to the approval of levetiracetam for use in infants and children with partial onset seizures is encouraging, although more work needs to be done before definitive conclusions can be drawn about the efficacy of levetiracetam across different pediatric age groups.

Eight years after emigrating from Brazil, an otherwise healthy man developed rabies. An exposure prior to immigration was reported. Genetic analysis revealed a canine rabies virus variant found only in the patient’s home country, and the patient had not traveled internationally since immigrating to the United States. We describe how epidemiological, phylogenetic, and viral sequencing data provided confirmation that rabies encephalomyelitis may present after a long, multiyear incubation period, a consideration that previously has been hypothesized without the ability to exclude a more recent exposure. Accordingly, rabies should be considered in the diagnosis of any acute encephalitis, myelitis, or encephalomyelitis.

Objective: To characterize the risk for seizures over time in relation to EEG findings in hospitalized adults undergoing continuous EEG monitoring (cEEG).

Methods: Retrospective analysis of cEEG data and medical records from 625 consecutive adult inpatients monitored at a tertiary medical center. Using survival analysis methods, we estimated the time-dependent probability that a seizure will occur within the next 72-h, if no seizure has occurred yet, as a function of EEG abnormalities detected so far.

Results: Seizures occurred in 27% (168/625). The first seizure occurred early (<30 min of monitoring) in 58% (98/168). In 527 patients without early seizures, 159 (30%) had early epileptiform abnormalities, versus 368 (70%) without. Seizures were eventually detected in 25% of patients with early epileptiform discharges, versus 8% without early discharges. The 72-h risk of seizures declined below 5% if no epileptiform abnormalities were present in the first two hours, whereas 16 h of monitoring were required when epileptiform discharges were present. 20% (74/388) of patients without early epileptiform abnormalities later developed them; 23% (17/74) of these ultimately had seizures. Only 4% (12/294) experienced a seizure without preceding epileptiform abnormalities.

Conclusions: Seizure risk in acute neurological illness decays rapidly, at a rate dependent on abnormalities detected early during monitoring. This study demonstrates that substantial risk stratification is possible based on early EEG abnormalities.

Significance: These findings have implications for patient-specific determination of the required duration of cEEG monitoring in hospitalized patients.

Lennox–Gastaut syndrome (LGS) is a devastating childhood epilepsy syndrome characterized by the occurrence of multiple types of seizures and cognitive decline. Most children suffer from frequent seizures that are refractory to current medical management. Recent clinical trials have suggested that addition of clobazam may improve the clinical outcome for some LGS patients. Although clobazam has been available for over five decades, it has only recently been approved by the US Food and Drug Administration for this indication. As a 1,5-benzodiazepine, clobazam is structurally related to the widely used 1,4-benzodiazepines, which include diazepam. Clobazam has been shown to modulate GABAergic neurotransmission by positive allosteric modulation of GABAA receptors, and to increase expression of transporters for both GABA and glutamate. The active metabolite n-desmethylclobazam (norclobazam) also modulates GABAA receptors, and the relative importance of these two compounds in the clinical effectiveness of clobazam remains an open question. Clinical trials involving clobazam as an addon therapy in a variety of pediatric epilepsy populations have found a significant improvement in seizure control. In patients with LGS, clobazam may have greatest efficacy for drop seizures. Longstanding clinical experience suggests that clobazam is a safe and well tolerated antiepileptic drug with infrequent and mild adverse effects. These results suggest that adjunctive treatment with clobazam may be a reasonable option for LGS patients, particularly those who are treatment-resistant.

Why seizures spontaneously terminate remains an unanswered fundamental question of epileptology. Here we present evidence that seizures self-terminate via a discontinuous critical transition or bifurcation. We show that human brain electrical activity at various spatial scales exhibits common dynamical signatures of an impending critical transition—slowing, increased correlation, and flickering—in the approach to seizure termination. In contrast, prolonged seizures (status epilepticus) repeatedly approach, but do not cross, the critical transition. To support these results, we implement a computational model that demonstrates that alternative stable attractors, representing the ictal and postictal states, emulate the observed dynamics. These results suggest that self-terminating seizures end through a common dynamical mechanism. This description constrains the specific biophysical mechanisms underlying seizure termination, suggests a dynamical understanding of status epilepticus, and demonstrates an accessible system for studying critical transitions in nature.