Seizure control in patients with epilepsy: the physician vs. medication factors

<p>Abstract</p> <p>Background</p> <p>Little is known about the relationship between types of healthcare providers and outcomes in patients with epilepsy. This study compares the relative effects of provider type (epileptologist vs. other neurologist) and pharmacologic treatment (newer vs. older antiepileptic drugs) on seizure control in patients with epilepsy.</p> <p>Methods</p> <p>We conducted a retrospective study of patients with medication-resistant epilepsy. Consecutive charts of 200 patients were abstracted using a standard case report form. For each patient, data included seizure frequency and medication use prior to, and while being treated by an epileptologist. Changes in seizure frequency were modeled using a generalized linear model.</p> <p>Results</p> <p>After transferring care from a general neurologist to specialized epilepsy center, patients experienced fewer seizures (p < 0.001) and were more frequently seizure-free (p < 0.001). The improved seizure control was not related to treatment with newer vs. older antiepileptic drugs (p = 0.305).</p> <p>Conclusion</p> <p>Our findings suggest an association between subspecialty epilepsy care and improved seizure control in patients with medication-resistant epilepsy. Further research should prospectively determine whether patients with medication-resistant epilepsy would benefit from being routinely referred to an epilepsy specialist.</p

AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software.

Objective: To develop a novel software method (AR2) for reducing muscle contamination of ictal scalp electroencephalogram (EEG), and validate this method on the basis of its performance in comparison to a commercially available software method (AR1) to accurately depict seizure-onset location. Methods: A blinded investigation used 23 EEG recordings of seizures from 8 patients. Each recording was uninterpretable with digital filtering because of muscle artifact and processed using AR1 and AR2 and reviewed by 26 EEG specialists. EEG readers assessed seizure-onset time, lateralization, and region, and specified confidence for each determination. The two methods were validated on the basis of the number of readers able to render assignments, confidence, the intra-class correlation (ICC), and agreement with other clinical findings. Results: Among the 23 seizures, two-thirds of the readers were able to delineate seizure-onset time in 10 of 23 using AR1, and 15 of 23 using AR2 (

Parametric Design, Manufacturing and Simulation of On-Demand Fixed Wing UAVs

AIAA Scitech 2021 Conference Paper.As the market for Unmanned Aerial Vehicles (UAVs) continues to expand, an unfulfilled need has been identified for tailor-made solutions leveraging an end-to-end process for the design and manufacture of the vehicle. The use of computer aided design combined with new manufacturing techniques allows small UAVs to be parametrically sized and quickly prototyped and deployed. This parametrization technique can be used throughout the entire design process to create optimized, attritable, on-demand solutions that can be adapted to evolving customer requirements. High-level requirements are mapped to quantitative design constraints and an automated process uses these constraints to design and manufacture a vehicle within a specified amount of time. The proposed framework is demonstrated with the generation of a fixed wing UAV solution for the detection and tracking of wildlife in remote areas. National Parks seek to prevent illegal poaching but often lack either the resources to monitor endangered animals, or the budget to purchase UAVs specially designed for wildlife tracking. First, mission requirements are identified and define a design space from which an optimal design point is selected. This design point sizes a UAV model, which is then optimized to minimize manufacturing time with the objective to yield a ready-to-fly solution within 48 hours. A flight simulation of the mission is then performed to ensure that the vehicle will fly as designed. Structural limitations of the UAV are accounted for and linked to parameters of the flight control algorithm to ensure that the UAV can safely fly its mission

The Virtual-Spine Platform—Acquiring, visualizing, and analyzing individual sitting behavior

Back pain is a serious medical problem especially for those people sitting over long periods during their daily work. Here we present a system to help users monitoring and examining their sitting behavior. The Virtual-Spine Platform (VSP) is an integrated system consisting of a real-time body position monitoring module and a data visualization module to provide individualized, immediate, and accurate sitting behavior support. It provides a comprehensive spine movement analysis as well as accumulated data visualization to demonstrate behavior patterns within a certain period. The two modules are discussed in detail focusing on the design of the VSP system with adequate capacity for continuous monitoring and a web-based interactive data analysis method to visualize and compare the sitting behavior of different persons. The data was collected in an experiment with a small group of subjects. Using this method, the behavior of five subjects was evaluated over a working day, enabling inferences and suggestions for sitting improvements. The results from the accumulated data module were used to elucidate the basic function of body position recognition of the VSP. Finally, an expert user study was conducted to evaluate VSP and support future developments

REM Sleep, Prefrontal Theta, and the Consolidation of Human Emotional Memory

Both emotion and sleep are independently known to modulate declarative memory. Memory can be facilitated by emotion, leading to enhanced consolidation across increasing time delays. Sleep also facilitates offline memory processing, resulting in superior recall the next day. Here we explore whether rapid eye movement (REM) sleep, and aspects of its unique neurophysiology, underlie these convergent influences on memory. Using a nap paradigm, we measured the consolidation of neutral and negative emotional memories, and the association with REM-sleep electrophysiology. Subjects that napped showed a consolidation benefit for emotional but not neutral memories. The No-Nap control group showed no evidence of a consolidation benefit for either memory type. Within the Nap group, the extent of emotional memory facilitation was significantly correlated with the amount of REM sleep and also with right-dominant prefrontal theta power during REM. Together, these data support the role of REM-sleep neurobiology in the consolidation of emotional human memories, findings that have direct translational implications for affective psychiatric and mood disorders

On the Perception of Newcomers: Toward an Evolved Psychology of Intergenerational Coalitions

Human coalitions frequently persist through multiple, overlapping membership generations, requiring new members to cooperate and coordinate with veteran members. Does the mind contain psychological adaptations for interacting within these intergenerational coalitions? In this paper, we examine whether the mind spontaneously treats newcomers as a motivationally privileged category. Newcomers—though capable of benefiting coalitions—may also impose considerable costs (e.g., they may free ride on other members, they may be poor at completing group tasks). In three experiments we show (1) that the mind categorizes coalition members by tenure, including newcomers; (2) that tenure categorization persists in the presence of orthogonal and salient social dimensions; and (3) that newcomers elicit a pattern of impressions consistent with their probable ancestral costs. These results provide preliminary evidence for a specialized component of human coalitional psychology: an evolved concept of newcomer

Cortical Plasticity Induced by Transcranial Magnetic Stimulation during Wakefulness Affects Electroencephalogram Activity during Sleep

BACKGROUND:Sleep electroencephalogram (EEG) brain oscillations in the low-frequency range show local signs of homeostatic regulation after learning. Such increases and decreases of slow wave activity are limited to the cortical regions involved in specific task performance during wakefulness. Here, we test the hypothesis that reorganization of motor cortex produced by long-term potentiation (LTP) affects EEG activity of this brain area during subsequent sleep. METHODOLOGY/PRINCIPAL FINDINGS:By pairing median nerve stimulation with transcranial magnetic stimulation over the contralateral motor cortex, one can potentiate the motor output, which is presumed to reflect plasticity of the neural circuitry. This paired associative stimulation increases M1 cortical excitability at interstimulus intervals of 25 ms. We compared the scalp distribution of sleep EEG power following paired associative stimulation at 25 ms to that following a control paradigm with 50 ms intervals. It is shown that the experimental manipulation by paired associative stimulation at 25 ms induces a 48% increase in amplitude of motor evoked potentials. This LTP-like potentiation, induced during waking, affects delta and theta EEG power in both REM and non-REM sleep, measured during the following night. Slow-wave activity increases in some frontal and prefrontal derivations and decreases at sites neighboring and contralateral to the stimulated motor cortex. The magnitude of increased amplitudes of motor evoked potentials by the paired associative stimulation at 25 ms predicts enhancements of slow-wave activity in prefrontal regions. CONCLUSIONS/SIGNIFICANCE:An LTP-like paradigm, presumably inducing increased synaptic strength, leads to changes in local sleep regulation, as indexed by EEG slow-wave activity. Enhancement and depression of slow-wave activity are interpreted in terms of a simultaneous activation of both excitatory and inhibitory circuits consequent to the paired associative stimulation at 25 ms

Multiway Array Decomposition Analysis of EEGs in Alzheimer’s Disease

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer’s disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied three state of the art multiway array decomposition (MAD) methods to extract features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE). We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease