Quantifying long-term microelectrode array functionality using chronic in vivo impedance testing

Abhishek Prasad, Justin C. Sanchez

Research output: Contribution to journalArticle

74 Scopus citations

Abstract

Long-term acquisition of high-quality neural recordings is a cornerstone of neuroprosthetic system design. Mitigating the experimental variability of chronically implanted arrays has been a formidable task because the sensor recording sites can be influenced by biotic and abiotic responses. Several studies have implicated changes in electrical interface impedance as a preliminary marker to infer electrode viability. Microelectrode impedance plays an important role in the monitoring of low amplitude and high-resolution extracellular neural signals. In this work, we seek to quantify long-term microelectrode array functionality and derive an impedance-based predictor for electrode functionality that correlates the recording site electrical properties with the functional neuronal recordings in vivo. High temporal resolution metrics of this type would allow one to assess, predict, and improve electrode performance in the future. In a large cohort of animals, we performed daily impedance measurements and neural signal recordings over long periods (up to 21 weeks) of time in rats using tungsten microwire arrays implanted into the somatosensory cortex. This study revealed that there was a time-varying trend in the modulation of impedance that was related to electrode performance. Single units were best detected from electrodes at time points when the electrode entered into the 40-150 KΩ impedance range. This impedance trend was modeled across the full cohort of animals to predict future electrode performance. The model was tested on data from all animals and was able to provide predictions of electrode performance chronically. Insight from this study can be combined with knowledge of electrode materials and histological analysis to provide a more comprehensive predictive model of electrode failure in the future.

Original languageEnglish (US)
Article number026028
JournalJournal of Neural Engineering
Volume9
Issue number2
DOIs
StatePublished - Apr 1 2012

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience

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