Evidence map›Paper›PMID 42445514›Full record

ArticleNational science review2026

MRI-compatible soft fiber bioelectronics for multimodal assessment of electrical neural stimulation on whole-brain activation.

Wenjun Li, Xiao Li, Haibo Yang, Chengqiang Tang, Zhenyu Wang, Qianfeng Wang, Yingnan Nie, Ziwei Liu, Yiqing Yang, He Wang and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in National science review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Wenjun LiState Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Institute of Fiber Materials and Devices, Fudan University, Shanghai 200438, China.
Xiao LiInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200438, China.
Haibo YangInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200438, China.
Chengqiang TangState Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Institute of Fiber Materials and Devices, Fudan University, Shanghai 200438, China.
Zhenyu WangInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200438, China.
Qianfeng WangInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200438, China.
Yingnan NieInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200438, China.
Ziwei LiuState Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Institute of Fiber Materials and Devices, Fudan University, Shanghai 200438, China.
Yiqing YangState Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Institute of Fiber Materials and Devices, Fudan University, Shanghai 200438, China.
He WangInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200438, China.
Songlin ZhangState Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Institute of Fiber Materials and Devices, Fudan University, Shanghai 200438, China.
Xiao-Yong ZhangInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200438, China.
Shouyan WangInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200438, China.
Huisheng PengState Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Institute of Fiber Materials and Devices, Fudan University, Shanghai 200438, China.
Xuemei SunState Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Institute of Fiber Materials and Devices, Fudan University, Shanghai 200438, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deciphering mechanisms of electrical neural stimulation using multimodal approaches combining electrophysiology and magnetic resonance imaging (MRI) is pivotal for advancing neuromodulation therapies. However, this paradigm has been hindered by the lack of high-performance neural electrodes that are compatible with ultra-high-field MRI while possessing exceptional electrochemical properties. Here, we report an MRI-compatible fiber neural electrode (MFE) fabricated from structurally optimized conductive polymer fiber emulating brain tissue characteristics. The MFE induces little-to-no MRI artifacts at 11.7 T and combines low modulus, low impedance and high charge-injection limit, enabling precise neural stimulation and recording. Utilizing these MFEs, we investigated frequency-dependent whole-brain responses to electrical stimulation of the medial prefrontal cortex in wild-type and autism-model rats, revealing responses potentially relevant to autism intervention. This was achieved through electrical stimulation synchronized with electrophysiological recording and multimodal MRI, including functional MRI, diffusion-weighted imaging (tissue structural assessment) and magnetic resonance spectroscopy (metabolite profiling). Our MFE enables previously unattained simultaneous acquisition of multimodal information, providing a powerful tool for in-depth mechanistic studies of neuromodulation.

Indexed as

conductive polymerfiberflexible electronicsMRI‐compatible

Identifiers

PMID42445514
PMCPMC13358296

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.