Neural Interfaces for Large Brains

We have recently received an NIH R01 grant to develop high-density optoelectrical neural interfaces for non-human primates with a path towards clinical applications in humans. These neural interfaces can be used for precise localization of epilepsy foci or intraoperative recording during implantation of deep brain stimulation (DBS) electrodes for mitigating Parkinson's disease.
 
Study of non-human primate (NHP) brains is important for understanding brain function and dysfunction in humans. Given the large size of the brain in NHPs and humans, realizing neural interfaces for recording or stimulation of neuronal activity in an NHP brain poses an engineering challenge, since on one hand we would like to make the cross section of these probes very small and on the other hand, they have to be long. Manufacturing, handling and implanting such long aspect ratio neural probes can be challenging.
 
Silicon is the most commonly used material platform for realizing neural interfaces for smaller brains in rodents. However, due to its brittleness, Silicon is not suitable for making robust devices with extremely high aspect ratio. Therefore, such unforgiving requirements for interfacing with NHP brains necessitate a quest for other material platforms as well as novel microfabrication methodologies.
 
We are working to design robust and reliable opto-electrical neural interfaces with high density of recording and stimulating channels in material platforms such as stainless steel  and Parylene C to realize mechanically robust neural interfaces for chronic neural recording and stimulation. We are developing novel microfabrication and micromachining processes to manufacture mass-producible, high density devices that can be made available to a wide user base for fundamental neuroscience studies as well as clinical applications.

Conference Presentation:

  1. Zabir Ahmed, Jay Reddy, Tobias Teichert, M. Chamanzar, “High-density Steeltrodes: A novel platform for High Resolution Recording in Primates,” IEEE NER (2019).