Evidence map›Paper›PMID 41160812›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

An Active, Multimodal Neural Interface for Real-Time Monitoring of Cortical Electrical, Thermal, and Optical Dynamics.

Jiahao Li, Yifei Lu, Zhongzheng Li, Lu Jin, Lianjie Zhou, Ke Ding, Junhan Liu, Bofan Hu, Pengchuan Liu, Dongqi An and 9 more

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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

19 authors.

Jiahao LiInstitute of Optoelectronics & College of Future Information Technology, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, China.ORCID https://orcid.org/0009-0003-6574-7455
Yifei LuInstitute of Optoelectronics & College of Future Information Technology, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, China.
Zhongzheng LiInstitute of Optoelectronics & College of Future Information Technology, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, China.
Lu JinYiwu Research Institute of Fudan University, Yiwu, Zhejiang, 322000, China.
Lianjie ZhouInstitute of Optoelectronics & College of Future Information Technology, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, China.
Ke DingInstitute of Optoelectronics & College of Future Information Technology, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, China.
Junhan LiuInstitute of Optoelectronics & College of Future Information Technology, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, China.
Bofan HuInstitute of Optoelectronics & College of Future Information Technology, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, China.
Pengchuan LiuInstitute of Optoelectronics & College of Future Information Technology, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, China.
Dongqi AnState Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, School of Mechanics and Aerospace Engineering, Dalian University of Technology, Dalian, 116024, China.
Fuying LiangInstitute of Optoelectronics & College of Future Information Technology, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, China.
Yuhang HuInstitute of Optoelectronics & College of Future Information Technology, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, China.
Yuting ShaoDepartment of Ophthalmology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, China.
Yifan DingInstitute of Optoelectronics & College of Future Information Technology, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, China.
Lichao MaInstitute of Optoelectronics & College of Future Information Technology, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, China.
Rui LiState Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, School of Mechanics and Aerospace Engineering, Dalian University of Technology, Dalian, 116024, China.
Yongfeng MeiInstitute of Optoelectronics & College of Future Information Technology, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, China.
Rongjun ZhangInstitute of Optoelectronics & College of Future Information Technology, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, China.
Enming SongInstitute of Optoelectronics & College of Future Information Technology, College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, China.ORCID https://orcid.org/0009-0004-6640-8647

Funding

Lingang Laboratory LGL-8998-09National Natural Science Foundation of China 62204057National Natural Science Foundation of China 62304044National Natural Science Foundation of China 82304124National Natural Science Foundation of China U2230108Science and Technology Commission of Shanghai Municipality 22ZR1406400State Key Laboratory of Integrated Chips and Systems SKLICS-K202515STI 2030-Major Project 2022ZD0209900
6 · The paper itself

Abstract

Chronic neurophysiological monitoring devices facilitate the timely diagnosis and treatment of episodic or recurrent neurological disorders. Compared with passive electrodes, silicon-based active transistors provide intrinsic signal amplification and, when combined with capacitive-coupling measurement mechanisms, enable high-density, high-fidelity recordings. However, most existing systems remain limited to single-modality electrical sensing and fail to address the growing demands of contemporary neurodynamic research. Here, a chronically implantable, large-area cortical interface capable of real-time multimodal monitoring of electrical, thermal, and photodynamic signals is presented. Building upon a silicon-transistor array for neural electrical detection, the device integrates thin-film metal resistors for temperature sensing while preserving mechanical flexibility sufficient for stable, long-term tissue contact. By leveraging the photoelectric effect of silicon transistors and functional multiplexing of active elements, the interface also achieves precise photodynamic measurement. In vitro experiments confirm long-term stability and channel isolation. In vivo evaluation in Sprague-Dawley rats, together with biocompatibility assessments, demonstrates reliable performance under physiological conditions. The technology used in this multifunctional platform has universal applicability in neural interfaces, offering continuous multimodal neurodynamic data acquisition with potential utility in monitoring, diagnosing, and treating chronic neurological conditions such as epilepsy and brain tumors.

Indexed as

Neurophysiological MonitoringAnimalsElectrodes, ImplantedRatsRats, Sprague-DawleyTransistors, Electronicelectrocorticogramsmultifunctional transistor arrayneural interfacephotodetectorstemperature sensors

Identifiers

PMID41160812
PMCPMC12806190

What OpenQuestion holds

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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 OpenQuestion graph.