A Radial Modulus‐Gradient Fiber for Chronic Recording and Decoding in Deep Brain
Fiber electronics provide the most promising platform for the detection, modulation, and reconstruction of biosignals in the brain. However, preserving stable communication between fiber electronics and cellular-scale targets in the deep brain is critical but challenging because of their mechanical mismatch. Here, our study fills this gap by developing a radial modulus-gradient fiber (RMGF), which can bridge high-modulus conductive components (MPa) and low-modulus brain tissue (kPa) to well eliminate the mechanical mismatch at the entire neural‒device interface. The RMGF exhibits strain-insensitive electrical properties (<0.2% resistance fluctuation over 700,000 stretching‒release cycles). As an example, the RMGF enables unprecedented five-month continuous tracking of single neurons in the dorsal lateral geniculate nucleus of freely moving cats, and allows reconstruction of visual stimuli with the use of only three neurons, with a high correlation coefficient of 0.95, approaching the theoretical limit of the unscented Kalman filter (0.97). The results indicate that dorsal lateral geniculate nucleus neurons maintain stable tuning properties (spatial frequency sensitivity, ON/OFF characteristics, and X-cell classification) and reveal a minimal effective ensemble for efficient encoding of information within deep thalamic circuits. This RMGF represents a platform for chronic recording at the single-cell level and investigating fundamental mechanisms in the deep tissues.
Authors
- Yuguo Yu (ORCID: https://orcid.org/0000-0003-0603-2890)
- B L Wang
- Jinyan Li (ORCID: https://orcid.org/0000-0003-1833-7413)
- Huisheng Peng (ORCID: https://orcid.org/0000-0002-2142-2945)
- Zhengqi Han
- Yiqing Yang (ORCID: https://orcid.org/0009-0004-9081-459X)
- Biqin Dong (ORCID: https://orcid.org/0000-0003-2283-4498)
- Jiahao Shen (ORCID: https://orcid.org/0000-0002-6624-9592)
- K Zhang (ORCID: https://orcid.org/0009-0000-8570-3298)
- Hui Li (ORCID: https://orcid.org/0000-0001-9198-3951)
- Mingyi Huang (ORCID: https://orcid.org/0000-0001-8096-7876)
- Sihui Yu (ORCID: https://orcid.org/0000-0002-5240-1098)
- Qingquan Han
- Peining Chen (ORCID: https://orcid.org/0000-0003-0566-1660)
- Jiaheng Liang
- Z.H. Liu (ORCID: https://orcid.org/0000-0002-9199-6493)
- Songlin Zhang
- Xiaofei Wang (ORCID: https://orcid.org/0000-0002-3248-6805)
- Cheng Cao (ORCID: https://orcid.org/0000-0002-4143-5970)
- Yi-Xiang Wang (ORCID: https://orcid.org/0000-0002-4021-4228)
- Peng Zhai (ORCID: https://orcid.org/0009-0002-0809-0111)
- Hang Guan
- Xiangran Cheng
- Chengqiang Tang
- Qi Tong
- Haixin Zhong
- Chen Zhao
- Xuemei Sun
- Lihua Zhang
- Ya Huang
- Jiawei Chen
- Jue Deng
- Ziyi Xie
- Liyuan Wang
- Hongbo Yu
Institutions
- Fudan University (CN)
- Center for Excellence in Brain Science and Intelligence Technology (CN)
- Yiwu Science and Technology Research Institute (CN)
- Shanghai Center for Brain Science and Brain-Inspired Technology (CN)
Publication Details
- Journal
- Advanced Materials
- Published
- 2026-04-21
- DOI
- https://doi.org/10.1002/adma.202519697
- Primary Topic
- Neuroscience and Neural Engineering
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Shanghai
- National Natural Science Foundation of China
- Ministry of Science and Technology of the People's Republic of China
- China Postdoctoral Science Foundation
- National Key Research and Development Program of China