C–FSCV: Compressive Fast-Scan Cyclic Voltammetry for Brain Dopamine Recording

Abstract

This paper presents a novel compressive sensing framework for recording brain dopamine levels with fast-scan cyclic voltammetry (FSCV) at a carbon-fiber microelectrode. Termed compressive FSCV (C-FSCV), this approach compressively samples the measured total current in each FSCV scan and performs basic FSCV processing steps, e.g., background current averaging and subtraction, directly with compressed measurements. The resulting background-subtracted faradaic currents, which are shown to have a block-sparse representation in the discrete cosine transform domain, are next reconstructed from their compressively sampled counterparts with the block sparse Bayesian learning algorithm. Using a previously recorded dopamine dataset, consisting of electrically evoked signals recorded in the dorsal striatum of an anesthetized rat, the C-FSCV framework is shown to be efficacious in compressing and reconstructing brain dopamine dynamics and associated voltammograms with high fidelity (correlation coefficient, ), while achieving compression ratio, CR, values as high as ~ 5. Moreover, using another set of dopamine data recorded 5 minutes after administration of amphetamine (AMPH) to an ambulatory rat, C-FSCV once again compresses (CR = 5) and reconstructs the temporal pattern of dopamine release with high fidelity ( ), leading to a true-positive rate of 96.4% in detecting AMPH-induced dopamine transients.

Electrochemical Detection of Dopamine Based on Functionalized Electrodes

“…It has been used, among other techniques, to detect dopamine, noradrenaline, serotonin, and indoleamine with carbon fiber electrodes because neurotransmitters oxidize at a low voltage [8,9]. It has been reported that FSCV was used in vitro, in vivo, on neuron cells and brain slices, and on anaesthetized or freely moving animals [10][11][12]. The voltammograms (current vs. voltage) obtained by these techniques are in theory specific to a given substance and may be used to determine precisely each neurotransmitter, as explained by Hermans et al [13].…”

Bioelectronic Sensor Nodes for the Internet of Bodies

“…Various implementations of thresholding for spike detection have been reported (102,103). Compression techniques have also been demonstrated on neural spikes using optimized vector quantization methods (104), CS (105)(106)(107), wavelet transform (108,109), Walsh-Hadamard transform (98), and spiking band power calculation (99,110). Wavelet transform-and Walsh-Hadamard transform-based techniques retain information on both the occurrence and the shape of the spike.…”