Pregabalin for the management of partial epilepsy

Pregabalin is one of the latest antiepileptic drugs introduced for the treatment of partial epilepsy. Its efficacy and safety as adjunctive therapy in refractory partial epilepsy have been established in four double-blind placebo-controlled trials (n = 1396) and 4 long-term open-label studies (n = 1480). In 3 fixed-dose trials, the proportion of patients with a ≥50% reduction in seizure frequency across the effective dose-range (150–600 mg/day) ranged between 14% and 51%, with a clear dose-response relationship. Suppression of seizure activity could be demonstrated as early as day 2. The most frequently reported CNS-related adverse events included dizziness, somnolence, ataxia and fatigue, were usually mild or moderate, and tended to be dose related. In long-term studies, weight gain was reported as an adverse event by 24% of patients. When pregabalin dose was individualized to according to response within the 150 to 600 mg/day dose range, tolerability was considerably improved compared with use of a high-dose, fixed-dose regimen (600 mg/day) without titration. In long-term studies up to 4 years, no evidence of loss efficacy was identified. During the last year on pregabalin, 3.7% of patients were seizure-free. Pregabalin appears to be a useful addition to the therapeutic armamentariun for the management of refractory partial epilepsy

Interpreting Deep Learning Models for Epileptic Seizure Detection on EEG signals

While Deep Learning (DL) is often considered the state-of-the art for Artificial Intelligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability of neural network models. We have tackled this issue by developing interpretable DL models in the context of online detection of epileptic seizure, based on EEG signal. This has conditioned the preparation of the input signals, the network architecture, and the post-processing of the output in line with the domain knowledge. Specifically, we focused the discussion on three main aspects: 1) how to aggregate the classification results on signal segments provided by the DL model into a larger time scale, at the seizure-level; 2) what are the relevant frequency patterns learned in the first convolutional layer of different models, and their relation with the delta, theta, alpha, beta and gamma frequency bands on which the visual interpretation of EEG is based; and 3) the identification of the signal waveforms with larger contribution towards the ictal class, according to the activation differences highlighted using the DeepLIFT method. Results show that the kernel size in the first layer determines the interpretability of the extracted features and the sensitivity of the trained models, even though the final performance is very similar after post-processing. Also, we found that amplitude is the main feature leading to an ictal prediction, suggesting that a larger patient population would be required to learn more complex frequency patterns. Still, our methodology was successfully able to generalize patient inter-variability for the majority of the studied population with a classification F1-score of 0.873 and detecting 90% of the seizures.Comment: 28 pages, 11 figures, 12 table

5-HT1A gene promoter polymorphism and [18F]MPPF binding potential in healthy subjects: a PET study

<p>Abstract</p> <p>Background</p> <p>Previous Positron Emission Tomography (PET) studies of 5-HT_1Areceptors have shown an influence of several genetic factors, including the triallelic serotonin transporter gene-linked polymorphic region on the binding potential (BP_ND) of these receptors. The aim of our study was to investigate the relationship between a 5-HT_1Apromoter polymorphism and the binding potential of another selective 5-HT_1Areceptor antagonist, [¹⁸F]MPPF, in healthy subjects.</p> <p>Methods</p> <p>Thirty-five volunteers, including 23 women, underwent an [¹⁸F]MPPF scan and were genotyped for both the C(-1019)G 5-HT_1Apromoter polymorphism and the triallelic serotonin transporter gene-linked polymorphic region. We used a simplified reference tissue model to generate parametric images of BP_ND. Whole brain Statistical Parametric Mapping and raphe nuclei region of interest analyses were performed to look for an association of [¹⁸F]MPPF BP_NDwith the C(-1019)G 5-HT_1Apromoter polymorphism.</p> <p>Results</p> <p>Among the 35 subjects, 5-HT_1Apromoter genotypes occurred with the following frequencies: three G/G, twenty-one G/C, and eleven C/C. No difference of [¹⁸F]MPPF BP_NDbetween groups was observed, except for two women who were homozygote carriers for the G allele and showed greater binding potential compared to other age-matched women over the frontal and temporal neocortex. However, the biological relevance of this result remains uncertain due to the very small number of subjects with a G/G genotype. These findings were not modified by excluding individuals carrying the S/S genotype of the serotonin transporter gene-linked polymorphic region.</p> <p>Conclusions</p> <p>We failed to observe an association between the C(-1019)G 5-HT_1Apromoter polymorphism and [¹⁸F]MPPF binding in healthy subjects. However our data suggest that the small number of women homozygote for the G allele might have greater [¹⁸F]MPPF BP_NDrelative to other individuals. This finding should be confirmed in a larger sample.</p

EpiDeNet: An Energy-Efficient Approach to Seizure Detection for Embedded Systems

Epilepsy is a prevalent neurological disorder that affects millions of individuals globally, and continuous monitoring coupled with automated seizure detection appears as a necessity for effective patient treatment. To enable long-term care in daily-life conditions, comfortable and smart wearable devices with long battery life are required, which in turn set the demand for resource-constrained and energy-efficient computing solutions. In this context, the development of machine learning algorithms for seizure detection faces the challenge of heavily imbalanced datasets. This paper introduces EpiDeNet, a new lightweight seizure detection network, and Sensitivity-Specificity Weighted Cross-Entropy (SSWCE), a new loss function that incorporates sensitivity and specificity, to address the challenge of heavily unbalanced datasets. The proposed EpiDeNet-SSWCE approach demonstrates the successful detection of 91.16% and 92.00% seizure events on two different datasets (CHB-MIT and PEDESITE, respectively), with only four EEG channels. A three-window majority voting-based smoothing scheme combined with the SSWCE loss achieves 3x reduction of false positives to 1.18 FP/h. EpiDeNet is well suited for implementation on low-power embedded platforms, and we evaluate its performance on two ARM Cortex-based platforms (M4F/M7) and two parallel ultra-low power (PULP) systems (GAP8, GAP9). The most efficient implementation (GAP9) achieves an energy efficiency of 40 GMAC/s/W, with an energy consumption per inference of only 0.051 mJ at high performance (726.46 MMAC/s), outperforming the best ARM Cortex-based solutions by approximately 160x in energy efficiency. The EpiDeNet-SSWCE method demonstrates effective and accurate seizure detection performance on heavily imbalanced datasets, while being suited for implementation on energy-constrained platforms.Comment: 5 pages, 4 tables, 1 figure, Accepted at BioCAS 202

Tailoring SVM Inference for Resource-Efficient ECG-Based Epilepsy Monitors

Event detection and classification algorithms are resilient towards aggressive resource-aware optimisations. In this paper, we leverage this characteristic in the context of smart health monitoring systems. In more detail, we study the attainable benefits resulting from tailoring Support Vector Machine (SVM) inference engines devoted to the detection of epileptic seizures from ECG-derived features. We conceive and explore multipleoptimisations, each effectively reducing resource budgets while minimally impacting classification performance. These strategies can be seamlessly combined, which results in 12.5X and 16X gains in energy and area, respectively, with a negligible loss, 3.2% in classification performance

Linking brain structure, activity and cognitive function through computation

Understanding the human brain is a “Grand Challenge” for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generative multiscale models, which enable the investigation of causation across scales and are guided by principles and theories of brain function, are instrumental for linking brain structure and function. An example of a resource enabling such an integrated approach to neuroscientific discovery is the BigBrain, which spatially anchors tissue models and data across different scales and ensures that multiscale models are supported by the data, making the bridge to both basic neuroscience and medicine. Research at the intersection of neuroscience, computing and robotics has the potential to advance neuro-inspired technologies by taking advantage of a growing body of insights into perception, plasticity and learning. To render data, tools and methods, theories, basic principles and concepts interoperable, the Human Brain Project (HBP) has launched EBRAINS, a digital neuroscience research infrastructure, which brings together a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating insights and perspectives for societal benefits