Paul R. Carney

Controlled Clinical Trial of Dichloroacetate for Treatment of Congenital Lactic Acidosis in Children

OBJECTIVE. Open-label studies indicate that oral dichloroacetate (DCA) may be effective in treating patients with congenital lactic acidosis. We tested this hypothesis by conducting the first double-blind, randomized, control trial of DCA in this disease. METHODS. Forty-three patients who ranged in age from 0.9 to 19 years were enrolled. All patients had persistent or intermittent hyperlactatemia, and most had severe psychomotor delay. Eleven patients had pyruvate dehydrogenase deficiency, 25 patients had 1 or more defects in enzymes of the respiratory chain, and 7 patients had a mutation in mitochondrial DNA. Patients were preconditioned on placebo for 6 months and then were randomly assigned to receive an additional 6 months of placebo or DCA, at a dose of 12.5 mg/kg every 12 hours. The primary outcome results were (1) a Global Assessment of Treatment Efficacy, which incorporated tests of neuromuscular and behavioral function and quality of life; (2) linear growth; (3) blood lactate concentration in the fasted state and after a carbohydrate meal; (4) frequency and severity of intercurrent illnesses and hospitalizations; and (5) safety, including tests of liver and peripheral nerve function. OUTCOME. There were no significant differences in Global Assessment of Treatment Efficacy scores, linear growth, or the frequency or severity of intercurrent illnesses. DCA significantly decreased the rise in blood lactate caused by carbohydrate feeding. Chronic DCA administration was associated with a fall in plasma clearance of the drug and with a rise in the urinary excretion of the tyrosine catabolite maleylacetone and the heme precursor δ-aminolevulinate. CONCLUSIONS. In this highly heterogeneous population of children with congenital lactic acidosis, oral DCA for 6 months was well tolerated and blunted the postprandial increase in circulating lactate. However, it did not improve neurologic or other measures of clinical outcome.

Long-term prospective on-line real-time seizure prediction

OBJECTIVE Epilepsy, one of the most common neurological disorders, constitutes a unique opportunity to study the dynamics of spatiotemporal state transitions in real, complex, nonlinear dynamical systems. In this study, we evaluate the performance of a prospective on-line real-time seizure prediction algorithm in two patients from a common database. METHODS We previously demonstrated that measures of chaos and angular frequency, estimated from electroencephalographic (EEG) signals recorded at critical sites in the cerebral cortex, progressively converge (i.e. become dynamically entrained) as the epileptic brain transits from the asymptomatic interictal state to the ictal state (seizure) (Iasemidis et al., 2001, 2002a, 2003a). This observation suggested the possibility of developing algorithms to predict seizures well ahead of their occurrences. One of the central points in those investigations was the application of optimization theory, specifically quadratic zero-one programming, for the selection of the critical cortical sites. This current study combines that observation with a dynamical entrainment detection method to prospectively predict epileptic seizures. The algorithm was tested in two patients with long-term (107.54h) and multi-seizure EEG data B and C (Lehnertz and Litt, 2004). RESULTS Analysis from the 2 test patients resulted in the prediction of up to 91.3% of the impending 23 seizures, about 89+/-15min prior to seizure onset, with an average false warning rate of one every 8.27h and an allowable prediction horizon of 3h. CONCLUSIONS The algorithm provides warning of impending seizures prospectively and in real time, that is, it constitutes an on-line and real-time seizure prediction scheme. SIGNIFICANCE These results suggest that the proposed seizure prediction algorithm could be used in novel diagnostic and therapeutic applications in epileptic patients.

An essential role for the H218/AGR16/Edg‐5/LPB2 sphingosine 1‐phosphate receptor in neuronal excitability

A wealth of indirect data suggest that the H218/AGR16/Edg‐5/LPB2 sphingosine 1‐phosphate (S1P) receptor plays important roles in development. In vitro, it activates several forms of development‐related signal transduction and regulates cellular proliferation, differentiation and survival. It is expressed during embryogenesis, and mutation of an H218‐like gene in zebrafish leads to profound defects in embryonic development. Nevertheless, the in vivo functions served by H218 signalling have not been directly investigated. We report here that mice in which the H218 gene has been disrupted are unexpectedly born with no apparent anatomical or physiological defects. In addition, no abnormalities were observed in general neurological development, peripheral axon growth or brain structure. However, between 3 and 7 weeks of age, H218–/– mice have seizures which are spontaneous, sporadic and occasionally lethal. Electroencephalographic abnormalities were identified both during and between the seizures. At a cellular level, whole‐cell patch‐clamp recordings revealed that the loss of H218 leads to a large increase in the excitability of neocortical pyramidal neurons. Therefore, H218 plays an essential, unanticipated and functionally important role in the proper development and/or mediation of neuronal excitability.

A novel tensor distribution model for the diffusion-weighted MR signal☆

Diffusion MRI is a non-invasive imaging technique that allows the measurement of water molecule diffusion through tissue in vivo. The directional features of water diffusion allow one to infer the connectivity patterns prevalent in tissue and possibly track changes in this connectivity over time for various clinical applications. In this paper, we present a novel statistical model for diffusion-weighted MR signal attenuation which postulates that the water molecule diffusion can be characterized by a continuous mixture of diffusion tensors. An interesting observation is that this continuous mixture and the MR signal attenuation are related through the Laplace transform of a probability distribution over symmetric positive definite matrices. We then show that when the mixing distribution is a Wishart distribution, the resulting closed form of the Laplace transform leads to a Rigaut-type asymptotic fractal expression, which has been phenomenologically used in the past to explain the MR signal decay but never with a rigorous mathematical justification until now. Our model not only includes the traditional diffusion tensor model as a special instance in the limiting case, but also can be adjusted to describe complex tissue structure involving multiple fiber populations. Using this new model in conjunction with a spherical deconvolution approach, we present an efficient scheme for estimating the water molecule displacement probability functions on a voxel-by-voxel basis. Experimental results on both simulations and real data are presented to demonstrate the robustness and accuracy of the proposed algorithms.

Performance of a seizure warning algorithm based on the dynamics of intracranial EEG

During the past decade, several studies have demonstrated experimental evidence that temporal lobe seizures are preceded by changes in dynamical properties (both spatial and temporal) of electroencephalograph (EEG) signals. In this study, we evaluate a method, based on chaos theory and global optimization techniques, for detecting pre-seizure states by monitoring the spatio-temporal changes in the dynamics of the EEG signal. The method employs the estimation of the short-term maximum Lyapunov exponent (STL(max)), a measure of the order (chaoticity) of a dynamical system, to quantify the EEG dynamics per electrode site. A global optimization technique is also employed to identify critical electrode sites that are involved in the seizure development. An important practical result of this study was the development of an automated seizure warning system (ASWS). The algorithm was tested in continuous, long-term EEG recordings, 3-14 days in duration, obtained from 10 patients with refractory temporal lobe epilepsy. In this analysis, for each patient, the EEG recordings were divided into training and testing datasets. We used the first portion of the data that contained half of the seizures to train the algorithm, where the algorithm achieved a sensitivity of 76.12% with an overall false prediction rate of 0.17h(-1). With the optimal parameter setting obtained from the training phase, the prediction performance of the algorithm during the testing phase achieved a sensitivity of 68.75% with an overall false prediction rate of 0.15h(-1). The results of this study confirm our previous observations from a smaller number of patients: the development of automated seizure warning devices for diagnostic and therapeutic purposes is feasible and practically useful.

Structure and Receptor binding properties of a pandemic H1N1 virus hemagglutinin

The 3D-structure of the major surface viral antigen from the recent H1N1 pandemic influenza virus (A/Darwin/2001/2009) was determined to 2.8 Å resolution. The structure was used to analyze changes in the HA that have emerged during the first 11 months of the pandemic and have raised public health concerns. Receptor binding properties of this protein reveals a strict preference for human-type receptors.

Evaluation of long-term treatment of children with congenital lactic acidosis with dichloroacetate.

OBJECTIVE. The purpose of this research was to report results on long-term administration of dichloroacetate in 36 children with congenital lactic acidosis who participated previously in a controlled trial of this drug. PATIENTS AND METHODS. We conducted a randomized control trial, followed by an open-label study. Data were analyzed for each patient from the time they began treatment through May 2005. RESULTS.Subject exposure to dichloroacetate totaled 110.42 years. Median height and weight increased over time, but the standardized values declined slightly and remained below the first percentile. There were no significant changes in biochemical metabolic indices, except for a 2% rise in total protein and a 22% increase in 24-hour urinary oxalate. Both the basal and carbohydrate meal-induced rises in lactate were blunted by dichloroacetate. The median cerebrospinal fluid lactate also decreased over time. Conduction velocity decreased and distal latency increased in peroneal nerves. Mean 3-year survival for all of the subjects was 79%. CONCLUSIONS. Oral dichloroacetate is generally well tolerated in young children with congenital lactic acidosis. Although continued dichloroacetate exposure is associated with evidence of peripheral neuropathy, it cannot be determined whether this is attributable mainly to the drug or to progression of underlying disease.

Diffusion Basis Functions Decomposition for Estimating White Matter Intravoxel Fiber Geometry

In this paper, we present a new formulation for recovering the fiber tract geometry within a voxel from diffusion weighted magnetic resonance imaging (MRI) data, in the presence of single or multiple neuronal fibers. To this end, we define a discrete set of diffusion basis functions. The intravoxel information is recovered at voxels containing fiber crossings or bifurcations via the use of a linear combination of the above mentioned basis functions. Then, the parametric representation of the intravoxel fiber geometry is a discrete mixture of Gaussians. Our synthetic experiments depict several advantages by using this discrete schema: the approach uses a small number of diffusion weighted images (23) and relatively small b values (1250 s/mm2 ), i.e., the intravoxel information can be inferred at a fraction of the acquisition time required for datasets involving a large number of diffusion gradient orientations. Moreover our method is robust in the presence of more than two fibers within a voxel, improving the state-of-the-art of such parametric models. We present two algorithmic solutions to our formulation: by solving a linear program or by minimizing a quadratic cost function (both with non-negativity constraints). Such minimizations are efficiently achieved with standard iterative deterministic algorithms. Finally, we present results of applying the algorithms to synthetic as well as real data.

Extraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics.

Electrocorticogram (ECoG) recordings for neuroprosthetics provide a mesoscopic level of abstraction of brain function between microwire single neuron recordings and the electroencephalogram (EEG). Single-trial ECoG neural interfaces require appropriate feature extraction and signal processing methods to identify and model in real-time signatures of motor events in spontaneous brain activity. Here, we develop the clinical experimental paradigm and analysis tools to record broadband (1Hz to 6kHz) ECoG from patients participating in a reaching and pointing task. Motivated by the significant role of amplitude modulated rate coding in extracellular spike based brain-machine interfaces (BMIs), we develop methods to quantify spatio-temporal intermittent increased ECoG voltages to determine if they provide viable control inputs for ECoG neural interfaces. This study seeks to explore preprocessing modalities that emphasize amplitude modulation across frequencies and channels in the ECoG above the level of noisy background fluctuations in order to derive the commands for complex, continuous control tasks. Preliminary experiments show that it is possible to derive online predictive models and spatially localize the generation of commands in the cortex for motor tasks using amplitude modulated ECoG.

Seizure prediction: Methods

Epilepsy, one of the most common neurological diseases, affects over 50 million people worldwide. Epilepsy can have a broad spectrum of debilitating medical and social consequences. Although antiepileptic drugs have helped treat millions of patients, roughly a third of all patients have seizures that are refractory to pharmacological intervention. The evolution of our understanding of this dynamic disease leads to new treatment possibilities. There is great interest in the development of devices that incorporate algorithms capable of detecting early onset of seizures or even predicting them hours before they occur. The lead time provided by these new technologies will allow for new types of interventional treatment. In the near future, seizures may be detected and aborted before physical manifestations begin. In this chapter we discuss the algorithms that make these devices possible and how they have been implemented to date. We also compare and contrast these measures, and review their individual strengths and weaknesses. Finally, we illustrate how these techniques can be combined in a closed-loop seizure prevention system. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.