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A Radial Modulus-gradient Fiber For Chronic Recording And Decoding In Deep Brain.

Home
A Radial Modulus-gradient Fiber For Chronic Recording And Decoding In Deep Brain.

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A Radial Modulus-Gradient Fiber for Chronic Recording and Decoding in Deep Brain.

Liyuan Wang¹, Chengqiang Tang¹, Zhengqi Han²

¹State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Institute of Fiber Materials and Devices, and Laboratory of Advanced Materials, Fudan University, Shanghai, China.

Advanced Materials (Deerfield Beach, Fla.)

|April 22, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Researchers developed a novel radial modulus-gradient fiber (RMGF) to overcome mechanical mismatch in brain implants. This fiber enables stable, long-term neural recording and precise reconstruction of sensory information from deep brain circuits.

Keywords:

carbon nanotube fiber fiber devices gradient‐modulus hydrogel neural recordings

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Area of Science:

Neuroscience
Biomedical Engineering
Materials Science

Background:

Stable neural recording in the deep brain is crucial for understanding brain function.
Mechanical mismatch between rigid electronics and soft brain tissue causes signal instability and device failure.

Purpose of the Study:

To develop a fiber electronic device that eliminates mechanical mismatch for chronic neural recording.
To demonstrate the efficacy of the new device for long-term single-neuron tracking and information encoding in the deep brain.

Main Methods:

Development of a radial modulus-gradient fiber (RMGF) to bridge high-modulus electronics and low-modulus brain tissue.
Testing of RMGF's strain-insensitive electrical properties over extensive cycling.
Chronic implantation of RMGF in the dorsal lateral geniculate nucleus of freely moving cats for five months.

Main Results:

RMGF successfully eliminated mechanical mismatch at the neural-device interface.
The device exhibited highly stable electrical properties with <0.2% resistance fluctuation over 700,000 cycles.
Five-month continuous single-neuron tracking was achieved, enabling visual stimuli reconstruction with high accuracy (0.95 correlation).

Conclusions:

RMGF provides a stable platform for chronic, single-cell level recording in deep brain tissues.
The study reveals stable neural tuning properties and identifies minimal neural ensembles for information encoding.
This technology advances our ability to investigate fundamental mechanisms in deep neural circuits.