Improving Deep Brain Stimulation Electrode Performance in vivo Through Use of Conductive Hydrogel Coatings

Active implantable neurological devices like deep brain stimulators have been used over the past few decades to treat movement disorders such as those in people with Parkinson’s disease and more recently, in psychiatric conditions like obsessive compulsive disorder. Electrode-tissue interfaces that support safe and effective targeting of specific brain regions are critical to success of these devices. Development of directional electrodes that activate smaller volumes of brain tissue requires electrodes to operate safely with higher charge densities. Coatings such as conductive hydrogels (CHs) provide lower impedances and higher charge injection limits (CILs) than standard platinum electrodes and support safer application of smaller electrode sizes. The aim of this study was to examine the chronic in vivo performance of a new low swelling CH coating that supports higher safe charge densities than traditional platinum electrodes. A range of hydrogel blends were engineered and their swelling and electrical performance compared. Electrochemical performance and stability of high and low swelling formulations were compared during insertion into a model brain in vitro and the formulation with lower swelling characteristics was chosen for the in vivo study. CH-coated or uncoated Pt electrode arrays were implanted into the brains of 14 rats, and their electrochemical performance was tested weekly for 8 weeks. Tissue response and neural survival was assessed histologically following electrode array removal. CH coating resulted in significantly lower voltage transient impedance, higher CIL, lower electrochemical impedance spectroscopy, and higher charge storage capacity compared to uncoated Pt electrodes in vivo, and this advantage was maintained over the 8-week implantation. There was no significant difference in evoked potential thresholds, signal-to-noise ratio, tissue response or neural survival between CH-coated and uncoated Pt groups. The significant electrochemical advantage and stability of CH coating in the brain supports the suitability of this coating technology for future development of smaller, higher fidelity electrode arrays with higher charge density requirement.

Interpreting the Effect of Stimulus Parameters on the Electrically Evoked Compound Action Potential and on Neural Health Estimates

Variations in the condition of the neural population along the length of the cochlea can degrade the spectral and temporal representation of sounds conveyed by CIs, thereby limiting speech perception. One measurement that has been proposed as an estimate of neural survival (the number of remaining functional neurons) or neural health (the health of those remaining neurons) is the effect of stimulation parameters, such as the interphase gap (IPG), on the amplitude growth function (AGF) of the electrically evoked compound action potential (ECAP). The extent to which such measures reflect neural factors, rather than non-neural factors (e.g. electrode orientation, electrode-modiolus distance, and impedance), depends crucially upon how the AGF data are analysed. However, there is currently no consensus in the literature for the correct method to interpret changes in the ECAP AGF due to changes in stimulation parameters. We present a simple theoretical model for the effect of IPG on ECAP AGFs, along with a re-analysis of both animal and human data that measured the IPG effect. Both the theoretical model and the re-analysis of the animal data suggest that the IPG effect on ECAP AGF slope (IPG slope effect), measured using either a linear or logarithmic input-output scale, does not successfully control for the effects of non-neural factors. Both the model and the data suggest that the appropriate method to estimate neural health is by measuring the IPG offset effect, defined as the dB offset between the linear portions of ECAP AGFs for two stimuli differing only in IPG.

Neuropharmacology of nicotine dependence

The occurrence of withdrawal symptoms is regarded as the key to mediating smoking relapse amongst smokers. The present study acknowledged the high relapse rates emerging from the inability to address the causes of powerful addiction effects besides identifying chronic disorders caused by nicotine. The present study explored nicotine addiction and its effects on different smoking patterns to provide an informative platform to design interventions that would deliver effective ways of quitting smoking. The study utilized systemic reviews on publications of previous studies obtained from scholarly journal databases, including PubMed, Medline, EBSCO Host, Google Scholar, and Cochrane. Moreover, the study used secondary information obtained from health organizations using filters and keywords to retrieve relevant information. The use of search keywords and filters limited the study to relevant peer-reviewed journals. The study utilized information retrieved from 35 studies obtained from peer-reviewed journals on “nicotine dependence,” “smoking cessation,” and “pharmacology of nicotine dependence and addiction.” The drug tolerance arising in nicotine dependence involved minimized tolerance often occurring during recurrent administration of drugs translated to neuroadaptation. The brain tends to develop challenges in the absence of nicotine, particularly when individuals quit smoking, thus compelling them to backslide from their abstinence. Higher nicotine dependence demotivates individuals from quitting smoking, making the cessation interventions unfruitful, worsened by the inability to understand the causative factors. The solution to overcome nicotine dependence alongside tobacco usage involves a complex treatment technique that would aim to reduce the probability of relapse. Nicotine dependence, Nicotine addiction, Tobacco addiction cycle, Neural survival, Pharmacology of nicotine

Lipids: Role in Embryonic Development (Review)

Aims: We propose to briefly review the specific role of lipids in embryonic structures development. Results: Lipids are organic substances insoluble in water, divided into several classes, such as fatty acids, glycolipids, phospholipids, ceramides, sphingolipids, and stereo-lipids. They participate in processes of cellular metabolism and embryonic development which are associated with signalling, proliferation and cell migration. They act in developmental processes such as calcification and bone mineralization, pulmonary maturity, cellular differentiation, and neural survival, epithelial cells polarization and muscle formation, in which phospholipids as a major group, work more regularly. Lipids during embryonic development work directly as transport molecules or cell markers. In addition to an imbalance in its enzymatic and protein precursors (such as choline kinase), lipids can increase or decrease lipid concentration in cells, prevent its biotransformation, or affect its synergy with other molecules, leading to failures in the formation of organs such as the heart, brain, and bones. This aims to further the understanding of these processes and highlight its feasibility for future clinical applications. Conclusion: Lipids maintain cell membrane integrity in blastocysts, transport calcium to nerve and bone cells, facilitate neural apoptosis, and promote pulmonary maturation. These results aid in the understanding and prediction of alterations in lipidic metabolic syndromes in several pathological disorders during organ development.

SAFE: A Neural Survival Analysis Model for Fraud Early Detection

Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the existing approaches adopt classifiers to predict fraudsters given their activity sequences along time. The main drawback of classification models is that the prediction results between consecutive timestamps are often inconsistent. In this paper, we propose a survival analysis based fraud early detection model, SAFE, which maps dynamic user activities to survival probabilities that are guaranteed to be monotonically decreasing along time. SAFE adopts recurrent neural network (RNN) to handle user activity sequences and directly outputs hazard values at each timestamp, and then, survival probability derived from hazard values is deployed to achieve consistent predictions. Because we only observe the user suspended time instead of the fraudulent activity time in the training data, we revise the loss function of the regular survival model to achieve fraud early detection. Experimental results on two real world datasets demonstrate that SAFE outperforms both the survival analysis model and recurrent neural network model alone as well as state-of-theart fraud early detection approaches.