Quantification of Chronic Microelectrode Signal Quality over Time

The developing field of brain machine interface contains enormous potential for therapeutic benefit. One of the most direct interfaces is the penetrating microelectrode array. However, the failure of chronically implanted neural probes limits the usefulness of penetrating microelectrodes for human brain machine interfaces. Over the course of several weeks after implantation, neural probes lose their ability to record signals due to a variety of tissue reactions including neuronal loss and glial scarring. Several forms of surface enhancements and drug delivery solutions have been proposed. However, in order to systematically evaluate these techniques, a reliable chronic recording model is needed that can offer quantification of recording quality, longevity and reliability. The results of this study are twofold. We present several parameters that may be used as metrics for quantifying the decay of signal quality in a microelectrode array. Second, we consider the effects of a potential surface modification for improving these parameters. In this study, we characterized the quality of neural recordings obtained from microelectrode arrays (16-channel, NeuroNexus, Inc, 16-channel, MicroProbes for Life Science) implanted chronically in the barrel cortex of adult rats. Signal to noise ratio of unit waveforms, local field potential and the ability of the implants to respond to a variety of stimulation parameters were evaluated as measures of the survival of the probe. L1 is a neural adhesion molecule that can specifically promote neurite outgrowth and neuronal survival. Previous in-vitro studies have suggested that that a surface modification of L1 may be able to increase the neuronal density local to the probe. We compared the signal degradation of L1 modified probes and control probes over 8 weeks. The data suggests trends towards improved signal to noise ratio in the L1 coated probes

Prediction and Hazard Estimation of Polycyclic Aromatic Hydrocarbon Transformation Products

Polycyclic Aromatic Hydrocarbons (PAHs) are a group of compounds containing at least two aromatic rings. Generated from natural or industrial processes, their degradation half-lives can range from weeks to months, as they undergo numerous environmental reactions resulting in diverse transformation products (TPs). While some PAHs possess known hazardous properties, relatively little is known about the hazards of their TPs. An increase in mutagenicity (the ability to cause genetic errors) has been observed as PAHs biodegrade. Since numerous TPs can be generated from each original PAH, evaluating which structures contribute to a potential increase in mutagenicity becomes a complex problem for remediators and regulators. The objective of this work was to build tools utilizing new and existing approaches to predict the most likely PAH TPs, identify which contributed to mutagenicity, and test the tools via an empirical experiment. To achieve this objective, a network-based tool was developed to refine datasets of over 20,000 predicted PAH TPs to less than 200, for the parent PAHs acenaphthene, anthracene, fluorene, and phenanthrene, creating a manageable number of the highest likelihood compounds. Within this subset, the tool predicted up to 48% of PAH TPs found by previous empirical studies, aiding in the first step, predicting likely TPs. To address the second step of PAH degradation risk assessment, a method to predict the hazard – here mutagenicity - of the most likely TPs was needed, as available tools were not designed for biodegradation-induced mutagenicity. A QSAR for PAH TP mutagenicity was developed which outperformed the best available QSARs when evaluating for biodegradation-induced mutagenicity and suggested that certain structural features corresponded to mutagenic mechanisms. Finally, the predictive tools were tested in an empirical study, aiming to identify the approximate time in a PAH’s degradation that mutagens emerge. Biodegradation cultures with phenanthrene and fluorene suggested that the networks tool and the QSAR together could help target the occurrence of mutagenicity in a PAH’s degradation timeline. Overall, this work provided two computational tools, the networks model for predicting the likely TPs, and the QSAR for estimating the mutagenicity of actively degrading PAHs and demonstrated their utility in biodegradation experiments