Miniature FSCV Devices: A Review

• Published in: IEEE sensors journal Vol. 21; no. 12; pp. 13006 - 13018
• Main Authors: Corva, Dean M., Adams, Scott D., Bennet, Kevin E., Berk, Michael, Kouzani, Abbas Z.
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.06.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain; Current measurement; Data processing; device; Electric potential; Electrodes; Fast-scan cyclic voltammetry; Miniaturization; Monitoring; neural engineering; neurochemical sensing; neuroscience; neurotransmitter; Neurotransmitters; Onboard data processing; Post-processing; Power capacitors; Sensors; Target recognition; Temporal resolution; Waveforms
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2021.3069950

Fast-scan cyclic voltammetry (FSCV) is an electrochemical sensing method used for measuring the concentration of neurotransmitters within the brain. FSCV is typically conducted using benchtop equipment, providing accurate and high temporal resolution measurements. However, the large size and high cost of benchtop FSCV systems pose significant limitations in widespread preclinical use of FSCV. Recent technological improvements have facilitated the miniaturization of FSCV equipment, creating systems that can be mounted on the subject as they are small and lightweight. One key remaining bottleneck in the emerging miniaturized FSCV systems is that their data typically needs to be transmitted to an external computer for analysis. This paper presents a review on the current miniature FSCV devices. It discusses the latest improvements in voltage scan waveforms that help increase the sensitivity of measuring specific analytes, for use in miniaturized FSCV. It also tabulates analytes that have been detected using FSCV. It also details current issues with FSCV and the latest achievements in post-processing of FSCV data to better identify target analytes. Furthermore, the emerging miniature FSCV devices that have integrated on-board data processing capabilities to mitigate external data processing are discussed. These advances can accelerate neurochemistry research, reducing time spent analysing data on external computers, and allowing for investigation into questions on neurotransmitter dynamics in target brain regions.