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Taking A Closer Look at Brain with Super-Small Needles

By Shreepanjali Mod November 28, 2016

A team of engineers recently developed very small needle-electrodes that are capable of probing into the highly complicated brain circuits. These needles measure some 5-um is diameter and permit in vivo recording of brain signals. The technology can really help in facilitation of brain-machine interface development. Our brain has a very complex network of neurons that mandates specific amount of finesse. It is for this reason one needs needle technology that has diameter than 10µm. A needle with a larger diameter will definitely lead to damage of brain tissues. The device substrate needs to be minimized for enhancing the accessibility of electrode as well as reducing damage to the tissues.

Super small needle technology for brain

These 5µm needles are manufactured over 1-mm x 1-mm silicon block modules that can be assembled into numerous device packages like flexible or rigid printed circuit boards. These needles were tested over mice and the results achieved were better than expected. Shota Yamagiwa and Hirohito Sawahata, the two members from this research team say, “We demonstrated the high design variability in the packaging of our electrode device and in vivo neuronal recordings were performed by simply placing the device on a mouse’s brain. We were very surprised that high-quality signals of a single unit were stably recorded over a long period using the 5-μm-diameter needle.”

The team working on this project is very optimistic about the results and believes that some similar electrode concepts that will help scientists in coming through with new neurophysiologic concepts. Another researcher from this team, Takeshi Kawano, says, “Our silicon needle technology offers low-invasive neuronal recordings and provides novel methodologies for electrophysiology; therefore, it has the potential to enhance experimental neuroscience. We expect the development of applications to solve the mysteries of the brain and the development of brain-machine interfaces.”


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