Ultrasmall implantable composite microelectrodes with bioactive surfaces for chronic neural interfaces

By T. Kozai, N. Langhals, Paras R. Patel, Xiaopei Deng, Huanan Zhang, Karen L. Smith, Joerg Lahann, Nicholas A. Kotov, Daryl R. Kipke. Nature Materials, 2012

664 citations recorded by the source corpus.

Implantable neural microelectrodes that can record extracellular biopotentials from small, targeted groups of neurons are critical for neuroscience research and emerging clinical applications including brain-controlled prosthetic devices. The crucial material-dependent problem is developing microelectrodes that record neural activity from the same neurons for years with high fidelity and reliability. Here, we report the development of an integrated composite electrode consisting of a carbon-fibre core, a poly(p-xylylene)-based thin-film coating that acts as a dielectric barrier and that is functionalized to control intrinsic biological processes, and a poly(thiophene)-based recording pad. The resulting implants are an order of magnitude smaller than traditional recording electrodes, and more mechanically compliant with brain tissue. They were found to elicit much reduced chronic reactive tissue responses and enabled single-neuron recording in acute and early chronic experiments in rats. This technology, taking advantage of new composites, makes possible highly selective and stealthy neural interface devices towards realizing long-lasting implants.

Subfields: Cellular and Molecular Neuroscience, Electrical and Electronic Engineering, Polymers and Plastics

View Paper: https://www.semanticscholar.org/paper/95f8a36bd5f96433b3c1187417d67af078621c37

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

  1. Christoph Weder
  2. Mehdi Jorfi
  3. John Skousen
  4. Jeffrey R. Capadona

In Polymers and Plastics

  1. Jeffrey R. Capadona
  2. Christoph Weder

Citations in the corpus