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Neural implants are being used increasingly for research, controlling prosthetics and treatment of disorders such as Parkinson's disease, epilepsy, and sensory loss1,2. Measuring and/or controlling both the chemical and electrical composition of the brain is the basis for all neural implants. However, it is important to administer a treatment only when the neural tissue is in the aberrant state to reduce side effects3. For instance, deep brain stimulators for epilepsy treatment should only apply an electrical pulse to the brain during a seizure. Some side effects may be dystonia, loss of memory, disorientation, impaired cognitive function, induced hallucinations, depression or anti-depression3,4. In many devices, a closed loop system is therefore necessary to record electrical activity and to trigger stimulation when an abnormal state is detected. Recording electrodes are also used to control prosthetic devices. It is critical to record the target neural activity with the highest possible signal-to-noise ratio to achieve the most accurate triggering and device control. A large signal-to-noise ratio is also highly desirable for research applications, as more reliable data can be obtained, resulting in fewer required test subjects. This will also allow a greater understanding of the mechanisms and pathways involved in neural stimulation and recording.
After a neural implant has been placed into the brain, an immune response is triggered5,6. The time course of the response is generally divided into acute and chronic phases, each consisting of different biological processes7. The immune response can have dramatic effects on the performance of the implant, such as isolation of the electrodes from the target neurons by encapsulation in a glial scar or chemical degradation of the implant materials8. This can reduce the signal-to-noise ratio of a recording electrode and the power output of a stimulating electrode, and lead to electrode failure9. Careful choice of implant design and materials are necessary to prevent failure over the implant lifetime.
Many different materials and implant designs have been developed recently to improve the signal-to-noise ratio and implant stability for neural recording. Electrode materials have included platinum, iridium, tungsten, iridium oxide, tantalum oxide, graphene, carbon nanotubes, doped conducting polymers, and more recently hydrogels. Substrate materials tested also includes silicon, silicon oxide, silicon nitride, silk, Teflon, polyimide, and silicone. Various electrode modifications have also been investigated, using coatings such as laminin, neurotrophins, or self-assembled monolayers and treatments using electrochemical, plasma and optical techniques. Implant designs could be 1-, 2- or 3-dimensional with the electrodes generally at the tip of an insulating probe or along the edge of a shank for penetrating electrodes or in a 2-dimensional array for cortex surface implants. Regardless of electrode design or material, previous literature has typically demonstrated the performance of the new implant without reference to other implant constructs. This prevents a systematic evaluation of their properties.
This protocol provides a method for comparing different electrode materials via a range of analytical and electrophysiological techniques. It is based on a recently published article which compared 4 different doped conducting polymer coatings (polypyrrole (Ppy) and poly-3,4-ethylenedioxythiophene (PEDOT) doped with sulfate (SO4) or para-toluene sulfonate (pTS)) and 4 different coating thicknesses10. This article found one material, PEDOT-pTS with a 45 sec deposition time, had the highest signal-to-noise ratio and spike count with the smallest background noise and that these parameters were dependent on electrode impedance. PEDOT-pTS also displayed superior acute biostability compared to the other doped conducting polymers and bare iridium electrodes. The protocol allows the critical parameters controlling the signal-to-noise ratio and stability to be determined and used to further improve the performance of neural recording electrodes.