Back

Electrochemical Roughening and Carbon Nanotube Coating of Tetrodes for Chronic Single-Unit Recording

Xia, Z.; Arias-Gil, G.; Deckert, M.; Vollmer, M.; Curran, A.; Herrera-Molina, R.; Brosch, M.; Krug, K.; Schmidt, B.; Ohl, F. W.; Lippert, M. T.; Takagaki, K.

2019-08-21 bioengineering
10.1101/738245 bioRxiv
Show abstract

Recording from single neurons in the brain for long periods of time has been a central goal in both basic neuroscience and translational neurology, in order to understand mechanisms underlying brain processes such as learning and to understand the pathogenesis of neurodynamic disease states 1. Recent advances in materials engineering, digital signal acquisition, and analysis algorithms have brought us closer to achieving this goal, and the possibility has gathered much public attention 2,3. However, it remains a challenge to record from the same units for weeks to months. Here, we record many high-quality tetrode neuronal signals reliably over long periods of time in both deep and superficial areas of the brain. We achieve this by combining electrochemical roughening and carbon nanotube coating of a flexible platinum/iridium substrate, with materials, packaging, and insertion optimized to minimize tip movement with brain pulsation. This \"Magdeburger\" probe enables recordings with long-term signal stability and high signal-to-noise ratio at a reasonable cost in both rodent brains and in substantially larger primate brains. Robust tetrode tracking of identified neurons over longer time periods, in multiple independently targeted areas of the brain, will allow fundamental advances in the study of cognitive learning, aging, and pathogenesis, and opens new possibilities for brain interfaces in humans.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.