Technological advances have allowed background-subtracted fast-scan cyclic voltammetry to emerge as a powerful tool for monitoring molecular fluctuations in living brain tissue; however, there has been little progress to date in advancing electrode calibration procedures. Variability in the performance of these handmade electrodes renders calibration necessary for accurate quantification; however, experimental protocol makes standard post-calibration difficult, or in some cases impossible. We have developed a model that utilizes information contained in the background charging current to predict electrode sensitivity to dopamine, ascorbic acid, hydrogen peroxide, and pH shifts at any point in an electrochemical experiment. Analysis determined a high correlation between predicted sensitivity and values obtained using the traditional post-calibration method, across all analytes. To validate this approach in vivo, calibration factors obtained with this model at electrodes in brain tissue were compared to values obtained at these electrodes using a traditional ex vivo calibration. Both demonstrated equal powers of predictability for dopamine concentrations. This advance enables in situ electrode calibration, allowing researchers to track changes in electrode sensitivity over time and eliminating the need to generalize calibration factors between electrodes or across multiple days in an experiment.

The dopaminergic neurons of the nigrostriatal dopamine (DA) projection from the substantia nigra to the dorsal striatum become dysfunctional and slowly degenerate in Parkinson's disease, a neurodegenerative disorder that afflicts more than one million Americans. There is no specific known cause for idiopathic Parkinson's disease; however, multiple lines of evidence implicate oxidative stress as an underlying factor in both the initiation and progression of the disease. This involves the enhanced generation of reactive oxygen species, including hydrogen peroxide (H 2 O 2 ), whose role in complex biological processes is not well understood. Using fast-scan cyclic voltammetry at bare carbon-fiber microelectrodes, we have simultaneously monitored and quantified H 2 O 2 and DA fluctuations in intact striatal tissue under basal conditions and in response to the initiation of oxidative stress. Furthermore, we have assessed the effect of acute increases in local H 2 O 2 concentration on both electrically evoked DA release and basal DA levels. Increases in endogenous H 2 O 2 in the dorsal striatum attenuated electrically evoked DA release, and also decreased basal DA levels in this brain region. These novel results will help to disambiguate the chemical mechanisms underlying the progression of neurodegenerative disease states, such as Parkinson's disease, that involve oxidative stress.

Neurotransmission occurs on a millisecond timescale, but conventional methods for monitoring non-electroactive neurochemicals are limited by slow sampling rates. Despite a significant global market, a sensor capable of measuring the dynamics of rapidly fluctuating, non-electroactive molecules at a single recording site with high sensitivity, electrochemical selectivity, and a subsecond response time is still lacking. To address this need, we have enabled the real-time detection of dynamic glucose fluctuations in live brain tissue using background-subtracted, fast-scan cyclic voltammetry. The novel microbiosensor consists of a simple carbon fiber surface modified with an electrodeposited chitosan hydrogel encapsulating glucose oxidase. The selectivity afforded by voltammetry enables quantitative and qualitative measurements of enzymatically-generated H2O2 without the need for additional strategies to eliminate interferents. The microbiosensors possess a sensitivity and limit of detection for glucose of 19.4 ± 0.2 nA mM−1 and 13.9 ± 0.7 μM, respectively. They are stable, even under deviations from physiological normoxic conditions, and show minimal interference from endogenous electroactive substances. Using this approach, we have quantitatively and selectively monitored pharmacologically, evoked glucose fluctuations with unprecedented chemical and spatial resolution. Furthermore, this novel biosensing strategy is widely applicable to the immobilization of any H2O2 producing enzyme, enabling rapid monitoring of many non-electroactive enzyme substrates.

Hydrogen peroxide (HO) is a reactive oxygen species that serves as an important signaling molecule in normal brain function. At the same time, excessive HO concentrations contribute to myriad pathological consequences resulting from oxidative stress. Studies to elucidate the diverse roles that HO plays in complex biological environments have been hindered by the lack of robust methods for probing dynamic HO fluctuations in living systems with molecular specificity. Background-subtracted fast-scan cyclic voltammetry at carbon-fiber microelectrodes provides a method of detecting rapid HO fluctuations with high temporal and spatial resolution in brain tissue. However, HO fluctuations can be masked by local changes in pH (ΔpH), because the voltammograms for these species can have significant peak overlap, hindering quantification. We present a method for removing ΔpH-related contributions from complex voltammetric data. By employing two distinct potential waveforms per scan, one in which HO is electrochemically silent and a second in which both ΔpH and HO are redox active, a clear distinction between HO and ΔpH signals is established. A partial least-squares regression (PLSR) model is used to predict the ΔpH signal and subtract it from the voltammetric data. The model has been validated both in vitro and in vivo using k-fold cross-validation. The data demonstrate that the double waveform PLSR model is a powerful tool that can be used to disambiguate and evaluate naturally occurring HO fluctuations in vivo.

Neurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times (Fellous, Tiesinga, Thomas, & Sejnowski, 2004). Finding events in data with realistic spiking statistics is challenging because events belonging to different spike patterns may overlap. We propose a method for finding spiking events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across multiple trials to detect and separate responses obtained during different brain states. The procedure can also be applied to spike trains from multiple simultaneously recorded neurons in order to identify volleys of near-synchronous activity or to distinguish between excitatory and inhibitory neurons. The procedure was tested using artificial data as well as recordings in vitro in response to fluctuating current waveforms.