Reconfigurable neural probes for chronic electrical recording

Abstract

Electrical interfaces with the nervous system are critical for enabling neurally-controlled prostheses and for creating better therapies for neurological disorders. Microelectrodes that penetrate into brain tissue to record extracellular electrical activity are one such neural interface technology known for their unparalleled spatial and temporal signal resolution. However, a major challenge with reliable chronic recording with such interfaces is tissue reaction, wherein a sheath of glial cells encapsulates the neural probe, electrically insulating the probe from the surrounding neurons. This work presents the design, fabrication, and characterization of a thin polymer probe whose body can be deflected and locked prior to insertion via a glue, storing mechanical energy in the device. After inserting into the brain and waiting for the initial glial sheath to form, the device can be deployed by melting the glue, causing the recording tip of the device to penetrate into fresh tissue. It is hypothesized that small tip dimensions (10-20 [mu]m) should prevent the formation of an additional glial sheath post-deployment and thus enable chronic recording. Four successive generations of devices were fabricated, characterized in benchtop tests, and subsequently tested in the rat model and in the optogenetic Thyl-ChR2-YFP mouse model. The electrical and mechanical functionality of the probe was confirmed under acute in vivo conditions in the medial prefrontal cortex of optogenetic Thyl-ChR2-YFP mice. Neural activity was successfully recorded from the probe immediately after insertion using laser stimulation of the mouse brain. Applying saline to the water-soluble glue caused the probe to deploy and neural activity was successfully recorded post-deployment. These results demonstrate the first in vivo deployment and electrical recordings from a reconfigurable neural probe with small dimensions (< 20 [mu]m) and hold promise for the creation of chronic recording neural interfaces.