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High density carbon fiber arrays for chronic electrophysiology, fast scan cyclic voltammetry, and correlative anatomy
- Source :
- Journal of Neural Engineering. 17:056029
- Publication Year :
- 2020
- Publisher :
- IOP Publishing, 2020.
-
Abstract
- Objective. Multimodal measurements at the neuronal level allow for detailed insight into local circuit function. However, most behavioral studies focus on one or two modalities and are generally limited by the available technology. Approach. Here, we show a combined approach of electrophysiology recordings, chemical sensing, and histological localization of the electrode tips within tissue. The key enabling technology is the underlying use of carbon fiber electrodes, which are small, electrically conductive, and sensitive to dopamine. The carbon fibers were functionalized by coating with Parylene C, a thin insulator with a high dielectric constant, coupled with selective re-exposure of the carbon surface using laser ablation. Main results. We demonstrate the use of this technology by implanting 16 channel arrays in the rat nucleus accumbens. Chronic electrophysiology and dopamine signals were detected 1 month post implant. Additionally, electrodes were left in the tissue, sliced in place during histology, and showed minimal tissue damage. Significance. Our results validate our new technology and methods, which will enable a more comprehensive circuit level understanding of the brain.
- Subjects :
- Materials science
0206 medical engineering
Biomedical Engineering
Fast-scan cyclic voltammetry
Insulator (electricity)
02 engineering and technology
Dielectric
engineering.material
03 medical and health sciences
Cellular and Molecular Neuroscience
chemistry.chemical_compound
0302 clinical medicine
Coating
Parylene
Carbon Fiber
Animals
Electrodes
Laser ablation
020601 biomedical engineering
Carbon
Electrophysiological Phenomena
Rats
Electrophysiology
chemistry
Electrode
engineering
Microelectrodes
030217 neurology & neurosurgery
Biomedical engineering
Subjects
Details
- ISSN :
- 17412552 and 17412560
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- Journal of Neural Engineering
- Accession number :
- edsair.doi.dedup.....6af970006c3ec0e8df5ae063ab8955cb