On the use of compressive sensing (CS) for brain dopamine recording with fast-scan cyclic voltammetry (FSCV)

摘要

This paper presents a novel compressive sensing (CS) framework for recording of electrically evoked brain dopamine levels using fast-scan cyclic voltammetry (FSCV) at a carbon-fiber microelectrode (CFM). This approach utilizes the discrete cosine transform (DCT) as the sparsifying domain for the background-subtracted faradaic (dopamine) currents, along with the block sparse Bayesian learning (BSBL) algorithm for signal reconstruction. Furthermore, basic FSCV processing steps such as background current averaging and in situ background current subtraction in each FSCV scan can be directly performed with compressed measurements. The effectiveness of the approach is demonstrated using previously recorded dopamine concentration changes from the dorsal striatum of an anesthetized laboratory rat that are evoked via electrical stimulation of the medial forebrain bundle (MFB). Specifically, the proposed framework can reconstruct the brain dopamine dynamics and their associated cyclic voltammograms with a high degree of fidelity (correlation coefficients of >0.97) for a compression ratio, CR, value as high as ～5.