Cyanide causes toxic effects by inhibiting cytochrome c oxidase, resulting in cellular hypoxia and cytotoxic anoxia, and can eventually lead to death. Cyanide exposure can be verified by direct analysis of cyanide concentrations or analyzing its metabolites, including thiocyanate (SCN(-)) and 2-amino-2-thiazoline-4-carboxylic acid (ATCA) in blood. To determine the behavior of these markers following cyanide exposure, a toxicokinetics study was performed in three animal models: (i) rats (250-300 g), (ii) rabbits (3.5-4.2 kg) and (iii) swine (47-54 kg). Cyanide reached a maximum in blood and declined rapidly in each animal model as it was absorbed, distributed, metabolized and eliminated. Thiocyanate concentrations rose more slowly as cyanide was enzymatically converted to SCN(-). Concentrations of ATCA did not rise significantly above the baseline in the rat model, but rose quickly in rabbits (up to a 40-fold increase) and swine (up to a 3-fold increase) and then fell rapidly, generally following the relative behavior of cyanide. Rats were administered cyanide subcutaneously and the apparent half-life (t1/2) was determined to be 1,510 min. Rabbits were administered cyanide intravenously and the t1/2 was determined to be 177 min. Swine were administered cyanide intravenously and the t1/2 was determined to be 26.9 min. The SCN(-) t1/2 in rats was 3,010 min, but was not calculated in rabbits and swine because SCN(-) concentrations did not reach a maximum. The t1/2 of ATCA was 40.7 and 13.9 min in rabbits and swine, respectively, while it could not be determined in rats with confidence. The current study suggests that cyanide exposure may be verified shortly after exposure by determining significantly elevated cyanide and SCN(-) in each animal model and ATCA may be used when the ATCA detoxification pathway is significant.

Objective

For intracortical brain-machine interfaces (BMIs), action potential voltage waveforms are often sorted to separate out individual neurons. If these neurons contain independent tuning information, this process could increase BMI performance. However, the sorting of action potentials ('spikes') requires high sampling rates and is computationally expensive. To explicitly define the difference between spike sorting and alternative methods, we quantified BMI decoder performance when using threshold-crossing events versus sorted action potentials.

Approach

We used data sets from 58 experimental sessions from two rhesus macaques implanted with Utah arrays. Data were recorded while the animals performed a center-out reaching task with seven different angles. For spike sorting, neural signals were sorted into individual units by using a mixture of Gaussians to cluster the first four principal components of the waveforms. For thresholding events, spikes that simply crossed a set threshold were retained. We decoded the data offline using both a Naïve Bayes classifier for reaching direction and a linear regression to evaluate hand position.

Main results

We found the highest performance for thresholding when placing a threshold between -3 and -4.5 × Vrms. Spike sorted data outperformed thresholded data for one animal but not the other. The mean Naïve Bayes classification accuracy for sorted data was 88.5% and changed by 5% on average when data were thresholded. The mean correlation coefficient for sorted data was 0.92, and changed by 0.015 on average when thresholded.

Significance

For prosthetics applications, these results imply that when thresholding is used instead of spike sorting, only a small amount of performance may be lost. The utilization of threshold-crossing events may significantly extend the lifetime of a device because these events are often still detectable once single neurons are no longer isolated.