Polydopamine-doped conductive polymer microelectrodes for neural recording and stimulation

Background: Microelectrodes have been widely used to detect and modulate the activities of neuronal networks. Various materials have been applied to microelectrode fabrication, and the conductive polymer is one of the most intensively explored material. The properties of conductive polymer highly depend on the incorporated material, so selecting it is essential. The mussel-inspired biomolecule, polydopamine (pDA), is known to provide unique chemical and mechanical properties to biological interfaces. New Method: pDA was incorporated into poly(3,4-ethylenedioxythiophene) (PEDOT) resulting in polydopamine PEDOT hybrid (PEDOT/pDA) microelectrode by an electrochemical deposition method. The electrical properties, such as impedance, charge storage capacity (CSC), and charge injection limit (CIL), of PEDOT/pDA microelectrodes, were characterized. Results: PEDOT/pDA microelectrodes had low impedance, high CSC, and high CIL, which are prerequisite for neuronal signal recording and stimulation. Then, neuronal recordings and electrical stimulations were conducted to verify the functionality of the PEDOT/pDA microelectrodes. Spontaneous and evoked extracellular neuronal signals were successfully measured from cultured rat hippocampal neuronal networks, and the recorded signals showed excellent signal-to-noise ratio for the detection of extracellular spikes. Comparison with Existing Methods: Compared with existing conductive polymer based neural electrodes, the PEDOT/pDA microelectrode had chemically functional material, pDA, embedded in the electrode, while it had comparable level of impedance and CSC and CIL for neural stimulation and recordings. Conclusions: We have shown that it is possible to fabricate a microelectrode array of pDA doped PEDOT microelectrodes and validated its performance for neuronal signal recording and electrical stimulation. The PEDOT/pDA microelectrode with excellent electrical performance and biocompatibility will be a promising tool for studying neuronal networks.