The Treatment of Neonatal Seizures: A Critical Review of the Evidence

In a critical review of the literature to assess the efficacy of monotherapy and subsequent combinant anticonvulsant therapy in the treatment of neonatal seizures, four studies were examined; three randomised control trials and one retrospective cohort study. Each study used phenobarbital for monotherapy with doses reaching a maximum of 40mg/kg. Anticonvulsant drugs used in conjunction with phenobarbitone for combinant therapy included midazolam, clonazepam, lorazepam, phenytoin and lignocaine. Each study used an electroencephalograph for seizure diagnosis and neonatal monitoring when determining therapy efficacy and final outcome assessments. Collectively the studies suggest neither monotherapy nor combinant therapy are entirely effective in seizure control. Monotherapy demonstrated a 29% - 50% success rate for complete seizure control whereas combinant therapy administered after the failure of monotherapy demonstrated a success rate of 43% - 100%. When these trials were combined the overall success for monotherapy was 44% (n = 34/78) and for combinant therapy 72% ( n = 56/78). Though the evidence was inconclusive, it would appear that combinant therapy is of greater benefit to infants unresponsive to monotherapy. Further research such as multi-site randomised controlled trials using standardised criteria and data collection are required within this specialised area.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.

Application of higher order spectra to identify epileptic EEG

Epilepsy is characterized by the spontaneous and seemingly unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into this phenomenon. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, we made a comparative study of the performance of Gaussian mixture model (GMM) and Support Vector Machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Results show that the selected HOS based features achieve 93.11% classification accuracy compared to 88.78% with features derived from the power spectrum for a GMM classifier. The SVM classifier achieves an improvement from 86.89% with features based on the power spectrum to 92.56% with features based on the bispectrum.

Osteopenia in a teenage boy presenting with previously undiagnosed guanidinoacetate methyltransferase (GAMT) deficiency and response to creatine supplementation

A 16 y.o. fully ambulant boy born to consanguineous Indian parents, presented for assessment of a fragility femoral neck fracture sustained against a background of autism and moderately severe intellectual disability. He had a past history of infantile eczema, and epilepsy treated with anticonvulsants from 2 to 10 years of age, with no further seizures following cessation of anticonvulsants. He had a thin body habitus (see Table 1) with long fingers and a high arched palate. He had no speech and negligible social interaction, but physical examination was otherwise unremarkable. Positive investigations revealed an undetectable serum creatinine and a urinary metabolic screen which showed an elevated GUA:Phe of 160 (< 36) and a decreased creatinine of 0.3 mmol/l (1.2–29.5) consistent with the diagnosis of guanidinoacetate methyltransferase(GAMT) deficiency. He was commenced on oral creatine 5 g three times daily. Despite improvement in physical activity, height and bone density, there was no discernable improvement in his intellectual functioning. Proton and phosphorous brain and leg magnetic resonance spectroscopy(MRS) was performed at baseline and showed an increased inorganic phosphorus peak and decreased phosphocreatine synthesis in brain and decreased creatine concentration in muscle. Following creatine treatment total brain creatine(1H-MRS) and phosphocreatine/ATP ratio (31P-MRS) content increased to 30% and 60% of control values, respectively. Brain GUA returned to normal levels.

Benign Epilepsy with Centro-temporal Spikes: Spike Triggered fMRI Shows Somato-sensory Cortex Activity

Summary:  Objective: We performed spike triggered functional MRI (fMRI) in a 12 year old girl with Benign Epilepsy with Centro-temporal Spikes (BECTS) and left-sided spikes. Our aim was to demonstrate the cerebral origin of her interictal spikes. Methods: EEG was recorded within the 3 Tesla MRI. Whole brain fMRI images were acquired, beginning 2–3 seconds after spikes. Baseline fMRI images were acquired when there were no spikes for 20 seconds. Image sets were compared with the Student's t-test. Results: Ten spike and 20 baseline brain volumes were analysed. Focal activiation was seen in the inferior left sensorimotor cortex near the face area. The anterior cingulate was more active during baseline than spikes. Conclusions: Left sided epileptiform activity in this patient with BECTS is associated with fMRI activation in the left face region of the somatosensory cortex, which would be consistent with the facial sensorimotor involvement in BECT seizures. The presence of BOLD signal change in other regions raises the possibility that the scalp recorded field of this patient with BECTs may reflect electrical change in more than one brain region.

Neurological drugs and opioids for pain management

20.1 Epilepsy and an introduction to drugs used to treat 20.1.1 Introduction to epilepsy 20.1.2 Treatment of partial seizures 20.1.3 Treatment of generalised seizures 20.1.4 Treatment of status epilepticus 20.2 Neurodegenerative disorders; principles of treatment 20.2.1 Introduction to neurodegenerative disorders 20.2.2 Parkinson’s disease 20.2.2.1 Introduction to Parkinson’s disease 20.2.2.2 Dopaminergic system 20.2.2.3 Treatment to enhance the dopaminergic system 20.2.2.4 Treatment to inhibit the cholinergic system 20.2.3 Dementia/Alzheimer’s disease 20.2.3.1 Introduction to Alzheimer’s disease 20.2.3.2 Treatment of Alzheimer’s disease 20.2.4 Amyotrophic lateral sclerosis 43.4.1 Introduction 43.4.2 Treatment 20.3. Pain and opioid analgesics 20.3.1 Introduction to pain and analgesia 20.3.2 Introduction to opioids 20.3.3 Tolerance and physical dependence 20.3.4 Effects of opioids 20.3.5 Agonists at opioid μ receptors 20.3.6 Toxicity to opioids This section deals with the neurologic drugs. The neurologic drugs are used to treat epilepsy and neurodegenerative diseases such as Parkinson’s disease and Alzheimer’s disease. The opioids for pain management are also discussed in this section.

Rapid quantification of methamphetamine: using Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) and Chemometrics

In Australia and increasingly worldwide, methamphetamine is one of the most commonly seized drugs analysed by forensic chemists. The current well-established GC/MS methods used to identify and quantify methamphetamine are lengthy, expensive processes, but often rapid analysis is requested by undercover police leading to an interest in developing this new analytical technique. Ninety six illicit drug seizures containing methamphetamine (0.1% - 78.6%) were analysed using Fourier Transform Infrared Spectroscopy with an Attenuated Total Reflectance attachment and Chemometrics. Two Partial Least Squares models were developed, one using the principal Infrared Spectroscopy peaks of methamphetamine and the other a Hierarchical Partial Least Squares model. Both of these models were refined to choose the variables that were most closely associated with the methamphetamine % vector. Both of the models were excellent, with the principal peaks in the Partial Least Squares model having Root Mean Square Error of Prediction 3.8, R2 0.9779 and lower limit of quantification 7% methamphetamine. The Hierarchical Partial Least Squares model had lower limit of quantification 0.3% methamphetamine, Root Mean Square Error of Prediction 5.2 and R2 0.9637. Such models offer rapid and effective methods for screening illicit drug samples to determine the percentage of methamphetamine they contain.

Investigation of the role of the GABRG2 gene variant in migraine

Migraine is the most common neurological disorder worldwide affecting about 12% of the worldwide population. This disorder has been classed into two main types of migraine—with and without aura. While a number of factors can influence the onset of migraine, a major factor is that of genetics. The GABAA gene encodes for the GABAA receptor. Along with other receptors, the GABAA receptor is involved in the mediation of neuronal activities. In this study, a GABRG2 gene (GABAA receptor gamma-2-subunit) SNP (rs211037) was genotyped on a migraine case–control population of 546 (273 affected and an equal number of healthy) individuals. Using specifically designed primers, a high resolution melt (HRM) assay was carried out in the genotyping process. After genotyping, results were compared in the case and control populations. Analysis of results showed no significant differences in the allele frequencies between case and control populations. Similarly no differences were detected for subtypes or for a specific gender of migraine (p > 0.05). Although this gene has been previously found to be involved in febrile seizures and there is some co-morbidity between epilepsy and migraine, we decided to investigate this marker for involvement in migraine. The results did not support a role for the tested GABRG2 variant in migraine.

Chemotherapy induced reversible posterior leukoencephalopathy syndrome

The reversible posterior leukoencephalopathy syndrome (RPLES) is a condition characterised by reversible neurological and radiological findings that has been associated with use of immunosuppressive, chemotherapeutic and more recently novel targeted therapies. We describe the case of a 50-year-old woman with advanced non-small cell lung cancer who developed status epilepticus shortly after receiving cisplatin and gemcitabine chemotherapy. The clinical, radiological and EEG findings during and post event are presented and are in keeping with a diagnosis of RPLES. Early recognition of this rare syndrome, supportive management and withdrawal of the offending agent appear to result in a reversal of the manifestations described. © 2007 Elsevier Ireland Ltd. All rights reserved.

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

The Brain Research Institute (BRI) uses various types of indirect measurements, including EEG and fMRI, to understand and assess brain activity and function. As well as the recovery of generic information about brain function, research also focuses on the utilisation of such data and understanding to study the initiation, dynamics, spread and suppression of epileptic seizures. To assist with the future focussing of this aspect of their research, the BRI asked the MISG 2010 participants to examine how the available EEG and fMRI data and current knowledge about epilepsy should be analysed and interpreted to yield an enhanced understanding about brain activity occurring before, at commencement of, during, and after a seizure. Though the deliberations of the study group were wide ranging in terms of the related matters considered and discussed, considerable progress was made with the following three aspects. (1) The science behind brain activity investigations depends crucially on the quality of the analysis and interpretation of, as well as the recovery of information from, EEG and fMRI measurements. A number of specific methodologies were discussed and formalised, including independent component analysis, principal component analysis, profile monitoring and change point analysis (hidden Markov modelling, time series analysis, discontinuity identification). (2) Even though EEG measurements accurately and very sensitively record the onset of an epileptic event or seizure, they are, from the perspective of understanding the internal initiation and localisation, of limited utility. They only record neuronal activity in the cortical (surface layer) neurons of the brain, which is a direct reflection of the type of electrical activity they have been designed to record. 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Identification of pre-spike network in patients with mesial temporal lobe epilepsy

Background: Seizures and interictal spikes in mesial temporal lobe epilepsy (MTLE) affect a network of brain regions rather than a single epileptic focus. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) studies have demonstrated a functional network in which hemodynamic changes are time-locked to spikes. However, whether this reflects the propagation of neuronal activity from a focus, or conversely the activation of a network linked to spike generation remains unknown. The functional connectivity (FC) changes prior to spikes may provide information about the connectivity changes that lead to the generation of spikes. We used EEG-fMRI to investigate FC changes immediately prior to the appearance of interictal spikes on EEG in patients with MTLE. Methods/principal findings: Fifteen patients with MTLE underwent continuous EEG-fMRI during rest. Spikes were identified on EEG and three 10 s epochs were defined relative to spike onset: spike (0–10 s), pre-spike (−10 to 0 s), and rest (−20 to −10 s, with no previous spikes in the preceding 45s). Significant spike-related activation in the hippocampus ipsilateral to the seizure focus was found compared to the pre-spike and rest epochs. The peak voxel within the hippocampus ipsilateral to the seizure focus was used as a seed region for FC analysis in the three conditions. A significant change in FC patterns was observed before the appearance of electrographic spikes. Specifically, there was significant loss of coherence between both hippocampi during the pre-spike period compared to spike and rest states. Conclusion/significance: In keeping with previous findings of abnormal inter-hemispheric hippocampal connectivity in MTLE, our findings specifically link reduced connectivity to the period immediately before spikes. This brief decoupling is consistent with a deficit in mutual (inter-hemispheric) hippocampal inhibition that may predispose to spike generation.

Genome-wide association identifies genetic variants associated with lentiform nucleus volume in N = 1345 young and elderly subjects

Deficits in lentiform nucleus volume and morphometry are implicated in a number of genetically influenced disorders, including Parkinson's disease, schizophrenia, and ADHD. Here we performed genome-wide searches to discover common genetic variants associated with differences in lentiform nucleus volume in human populations. We assessed structural MRI scans of the brain in two large genotyped samples: the Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 706) and the Queensland Twin Imaging Study (QTIM; N = 639). Statistics of association from each cohort were combined meta-analytically using a fixed-effects model to boost power and to reduce the prevalence of false positive findings. We identified a number of associations in and around the flavin-containing monooxygenase (FMO) gene cluster. The most highly associated SNP, rs1795240, was located in the FMO3 gene; after meta-analysis, it showed genome-wide significant evidence of association with lentiform nucleus volume (PMA = 4. 79 × 10-8). This commonly-carried genetic variant accounted for 2. 68 % and 0. 84 % of the trait variability in the ADNI and QTIM samples, respectively, even though the QTIM sample was on average 50 years younger. Pathway enrichment analysis revealed significant contributions of this gene to the cytochrome P450 pathway, which is involved in metabolizing numerous therapeutic drugs for pain, seizures, mania, depression, anxiety, and psychosis. The genetic variants we identified provide replicated, genome-wide significant evidence for the FMO gene cluster's involvement in lentiform nucleus volume differences in human populations.