On-demand electrically controlled melatonin release from PEDOT/SNP composite improves quality of chronic neural recording

Author:

Zhu Ying,Yang Yuxi,Ni Gongang,Li Shijin,Liu Wei,Gao Zhongbao,Zhang Xiao,Zhang Qi,Wang Changyong,Zhou Jin

Abstract

Long-time and high-quality signal acquisition performance from implantable electrodes is the key to establish stable and efficient brain-computer interface (BCI) connections. The chronic performance of implantable electrodes is hindered by the inflammatory response of brain tissue. In order to solve the material limitation of biological interface electrodes, we designed sulfonated silica nanoparticles (SNPs) as the dopant of Poly (3,4-ethylenedioxythiophene) (PEDOT) to modify the implantable electrodes. In this work, melatonin (MT) loaded SNPs were incorporated in PEDOT via electrochemical deposition on nickel-chromium (Ni-Cr) alloy electrode and carbon nanotube (CNT) fiber electrodes, without affecting the acute neural signal recording capacity. After coating with PEDOT/SNP-MT, the charge storage capacity of both electrodes was significantly increased, and the electrochemical impedance at 1 kHz of the Ni-Cr alloy electrodes was significantly reduced, while that of the CNT electrodes was significantly increased. In addition, this study inspected the effect of electrically triggered MT release every other day on the quality and longevity of neural recording from implanted neural electrodes in rat hippocampus for 1 month. Both MT modified Ni-Cr alloy electrodes and CNT electrodes showed significantly higher spike amplitude after 26-day recording. Significantly, the histological studies showed that the number of astrocytes around the implanted Ni-Cr alloy electrodes was significantly reduced after MT release. These results demonstrate the potent outcome of PEDOT/SNP-MT treatment in improving the chronic neural recording quality possibly through its anti-inflammatory property.

Publisher

Frontiers Media SA

Subject

Biomedical Engineering,Histology,Bioengineering,Biotechnology

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