Evidence map›Paper›PMID 41144819›Full record

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

Deciphering Neural Mechanisms Underlying Marmoset Dynamic Natural Behaviors Using a Miniaturized Wireless Large-Scale Coverage Neural Recorder.

Hongru Liu, Xinyuan Cao, Jiyong Li, Lingyi Zheng, Jingwei Li, Qianbing Li, Min Xie, Huimin Li, Xiaolong Wang, Yuyu Wu and 13 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. Article
  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

23 authors.

Hongru LiuSchool of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.
Xinyuan CaoDepartment of Neurology of the Second Affiliated Hospital and Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, 310029, China.
Jiyong LiSchool of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.ORCID https://orcid.org/0009-0000-0183-6393
Lingyi ZhengSchool of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.
Jingwei LiSchool of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.
Qianbing LiDepartment of Neurology of the Second Affiliated Hospital and Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, 310029, China.
Min XieDepartment of Neurology of the Second Affiliated Hospital and Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, 310029, China.
Huimin LiSchool of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.
Xiaolong WangSchool of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.
Yuyu WuDepartment of Neurology of the Second Affiliated Hospital and Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, 310029, China.
Xiangyu ZhangDepartment of Neurology of the Second Affiliated Hospital and Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, 310029, China.
Yizheng WangDepartment of Neurology of the Second Affiliated Hospital and Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, 310029, China.
Xize GaoSchool of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.
Tiancheng ShengSchool of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.
Nianzhen DuSchool of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.
Chengao XuDepartment of Mechanical Engineering, Tsinghua University, Beijing, 100084, China.
Kai ZhouDepartment of Neurology of the Second Affiliated Hospital and Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, 310029, China.
Jing XuDepartment of Mechanical Engineering, Tsinghua University, Beijing, 100084, China.
Changxiang YanDepartment of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, 100093, China.
Lianqing LiuState Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China.
Lixia GaoDepartment of Neurology of the Second Affiliated Hospital and Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, 310029, China.
Xinjian LiDepartment of Neurology of the Second Affiliated Hospital and Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, 310029, China.
Mingjun ZhangSchool of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.ORCID https://orcid.org/0009-0008-9358-9972

Funding

National Key Research and Development Program of China 2023YFB4705500National Natural Science Foundation of China 62350710211
6 · The paper itself

Abstract

Deciphering neural mechanisms underlying dynamic natural behaviors of freely moving species requires long-term recordings of large-scale brain activities. However, most conventional neural recorders are limited by their weights and measures, electrode coverage, and signal throughput, hindering the dissection of underlying neural mechanisms. This study reports real-time large-scale recordings and deciphering of brain activities from frontal and temporal cortices of freely moving marmoset across various natural behavioral repertoire using a miniaturized wireless neural recorder comprising a custom-designed 120-channel flexible µECoG array. Behavior-specific highly resolved spatiotemporal neural dynamics are observed, including alpha-band activations during drinking, anticipatory responses before vocalization, and transient high-gamma increase during vigilance to human intruders. Three phases of drinking behavior are identified using multi-area neural features captured by the recorder with an accuracy exceeding 87%. After over 16 months (March 13, 2024-August 1, 2025, remaining actively recording) of recordings, the neural signals acquired using the recorder maintain high fidelity and low attenuation during both the resting and drinking states, enabling potential long-term dissection of the neural mechanisms of natural behaviors in freely moving marmosets.

Indexed as

Behavior, AnimalBrainCallithrixWireless TechnologyAnimalsFemaleMalefreely moving neural recordinghigh‐density flexible µECoG arrayneural dynamics underlying natural behaviorsnon‐human primatewireless neural recorder

Identifiers

PMID41144819
PMCPMC12786299

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.