Changes Over Time in the Electrode/Brain Interface Impedance: An Ex-Vivo Study.


Journal

IEEE transactions on biomedical circuits and systems
ISSN: 1940-9990
Titre abrégé: IEEE Trans Biomed Circuits Syst
Pays: United States
ID NLM: 101312520

Informations de publication

Date de publication:
06 2023
Historique:
medline: 14 7 2023
pubmed: 9 6 2023
entrez: 9 6 2023
Statut: ppublish

Résumé

Closed-loop neural implants based on continuous brain activity recording and intracortical microstimulation are extremely effective and promising devices to monitor and address many neurodegenerative diseases. The efficiency of these devices depends on the robustness of the designed circuits which rely on precise electrical equivalent models of the electrode/brain interface. This is true in the case of amplifiers for differential recording, voltage or current drivers for neurostimulation, and potentiostats for electrochemical bio-sensing. This is of paramount importance, especially for the next generation of wireless and ultra-miniaturised CMOS neural implants. Circuits are usually designed and optimized considering the electrode/brain impedance with a simple electrical equivalent model whose parameters are stationary over time. However, the electrode/brain interfacial impedance varies simultaneously in frequency and in time after implantation. The aim of this study is to monitor the impedance changes occurring on microelectrodes inserted in ex-vivo porcine brains to derive an opportune electrode/brain model describing the system and its evolution in time. In particular, impedance spectroscopy measurements have been performed for 144 hours to characterise the evolution of the electrochemical behaviour in two different setups analysing both the neural recording and the chronic stimulation scenarios. Then, different equivalent electrical circuit models have been proposed to describe the system. Results showed a decrease in the resistance to charge transfer, attributed to the interaction between biological material and the electrode surface. These findings are crucial to support circuit designers in the field of neural implants.

Identifiants

pubmed: 37294653
doi: 10.1109/TBCAS.2023.3284691
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

495-506

Auteurs

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Classifications MeSH