4.8 Article

Supramolecular Peptide Hydrogel-Based Soft Neural Interface Augments Brain Signals through a Three-Dimensional Electrical Network

Journal

ACS NANO
Volume 14, Issue 1, Pages 664-675

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acsnano.9b07396

Keywords

hydrogel-based neural interface; supramolecular peptide; carbon nanotube; brain signals; three-dimensional electrical network

Funding

  1. Institute for Basic Science [IBS-R015-D1]
  2. National Research Foundation of Korea (NRF) [NRF-2019R1A2C1085712, NRF-2017M3A7B4041973, NRF-2018R1D1A1B07050569, NRF-2016M3A7B4910538]
  3. Basic Science Research Program through the NRF - Ministry of Education [NRF-2017R1A6A1A03015642]
  4. National Research Foundation of Korea [IBS-R015-D1-2020-A00] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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Recording neural activity from the living brain is of great interest in neuroscience for interpreting cognitive processing or neurological disorders. Despite recent advances in neural technologies, development of a soft neural interface that integrates with neural tissues, increases recording sensitivity, and prevents signal dissipation still remains a major challenge. Here, we introduce a biocompatible, conductive, and biostable neural interface, a supramolecular beta-peptide-based hydrogel that allows signal amplification via tight neural/hydrogel contact without neuroinflammation. The non-biodegradable beta-peptide forms a multihierarchical structure with conductive nanomaterial, creating a three-dimensional electrical network, which can augment brain signal efficiently. By achieving seamless integration in brain tissue with increased contact area and tight neural tissue coupling, the epidural and intracortical neural signals recorded with the hydrogel were augmented, especially in the high frequency range. Overall, our tissuelike chronic neural interface will facilitate a deeper understanding of brain oscillation in broad brain states and further lead to more efficient brain-computer interfaces.

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