Highly scalable multichannel mesh electronics for stable chronic brain electrophysiology
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CitationFu, Tian-Ming, Guosong Hong, Robert D. Viveros, Tao Zhou, and Charles M. Lieber. 2017. “Highly scalable multichannel mesh electronics for stable chronic brain electrophysiology.” Proceedings of the National Academy of Sciences of the United States of America 114 (47): E10046-E10055. doi:10.1073/pnas.1717695114. http://dx.doi.org/10.1073/pnas.1717695114.
AbstractImplantable electrical probes have led to advances in neuroscience, brain−machine interfaces, and treatment of neurological diseases, yet they remain limited in several key aspects. Ideally, an electrical probe should be capable of recording from large numbers of neurons across multiple local circuits and, importantly, allow stable tracking of the evolution of these neurons over the entire course of study. Silicon probes based on microfabrication can yield large-scale, high-density recording but face challenges of chronic gliosis and instability due to mechanical and structural mismatch with the brain. Ultraflexible mesh electronics, on the other hand, have demonstrated negligible chronic immune response and stable long-term brain monitoring at single-neuron level, although, to date, it has been limited to 16 channels. Here, we present a scalable scheme for highly multiplexed mesh electronics probes to bridge the gap between scalability and flexibility, where 32 to 128 channels per probe were implemented while the crucial brain-like structure and mechanics were maintained. Combining this mesh design with multisite injection, we demonstrate stable 128-channel local field potential and single-unit recordings from multiple brain regions in awake restrained mice over 4 mo. In addition, the newly integrated mesh is used to validate stable chronic recordings in freely behaving mice. This scalable scheme for mesh electronics together with demonstrated long-term stability represent important progress toward the realization of ideal implantable electrical probes allowing for mapping and tracking single-neuron level circuit changes associated with learning, aging, and neurodegenerative diseases.
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