Name
Abstract | ||
|---|---|---|
Fast-scan cyclic voltammetry (FSCV) with background subtraction method has been widely used for detecting neurotransmitters in the brain. The most common application of FSCV is measuring phasic change of dopamine (DA) in the brain evoked by an external stimulus. The background subtraction method has greatly improved FSCV application to the neuroscience field, however, tonic dopamine concentration which is as vital as phasic change cannot be measured because the background is subtracted. In this study, we developed a tailoring FSCV technique which can manipulate background current by modifying the waveform voltage point. By using this technique, the last background current generated by last waveform in multiple pulses is tailored to the front background current. As a result, background current is cancelled out by subtracting the front voltammogram and tailored (last) voltammogram. On the other hand, DA oxidation/reduction pattern remained between front and last voltammogram, so that, tailoring FSCV can detect tonic DA concentration without background subtraction method. The tailoring technique is evaluated by comparing with commercialized enzyme-linked immunosorbent assay (ELISA) kits. By measuring endogenously released DA from DA cell, tailoring method showed significant correlation with ELISA result. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/MeMeA.2017.7985858 | 2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA) |
Keywords | Field | DocType |
Fast-scan cyclic voltammetry,Dopamine,Tonic DA concentration | Biomedical engineering,Background subtraction,Tonic (music),Waveform,Electronic engineering,Dopamine,Cyclic voltammetry,Fast-scan cyclic voltammetry,Materials science | Conference |
ISBN | Citations | PageRank |
978-1-5090-2985-3 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoonbae Oh | 1 | 0 | 0.68 |
| Yu Min Kang | 2 | 0 | 0.34 |
| Cheonho Park | 3 | 0 | 0.34 |
| Hojin Shin | 4 | 0 | 0.34 |
| In Young Kim | 5 | 250 | 32.24 |
| Dong Pyo Jang | 6 | 1 | 2.39 |
| Kevin E. Bennet | 7 | 13 | 4.94 |
| Kendall H. Lee | 8 | 2 | 1.74 |