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Intravascular delivery of an ultraflexible neural electrode array for recordings of cortical spiking activity

Author

Listed:
  • Xingzhao Wang

    (Chinese Academy of Sciences)

  • Shun Wu

    (Chinese Academy of Sciences)

  • Hantao Yang

    (Shanghai Geriatric Medical Center
    Zhongshan Hospital)

  • Yu Bao

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Zhi Li

    (Fudan University)

  • Changchun Gan

    (Chinese Academy of Sciences)

  • Yuanyuan Deng

    (ShanghaiTech University)

  • Junyan Cao

    (University of Shanghai for Science and Technology)

  • Xue Li

    (Chinese Academy of Sciences)

  • Yun Wang

    (Zhongshan Hospital
    Fudan University)

  • Chi Ren

    (Chinese Academy of Sciences)

  • Zhigang Yang

    (Zhongshan Hospital)

  • Zhengtuo Zhao

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

Abstract

Although intracranial neural electrodes have significantly contributed to both fundamental research and clinical treatment of neurological diseases, their implantation requires invasive surgery to open craniotomies, which can introduce brain damage and disrupt normal brain functions. Recent emergence of endovascular neural devices offers minimally invasive approaches for neural recording and stimulation. However, existing endovascular neural devices are unable to resolve single-unit activity in large animal models or human patients, impeding a broader application as neural interfaces in clinical practice. Here, we present the ultraflexible implantable neural electrode as an intravascular device (uFINE-I) for recording brain activity at single-unit resolution. We successfully implanted uFINE-Is into the sheep occipital lobe by penetrating through the confluence of sinuses and recorded both local field potentials (LFPs) and multi-channel single-unit spiking activity under spontaneous and visually evoked conditions. Imaging and histological analysis revealed minimal tissue damage and immune response. The uFINE-I provides a practical solution for achieving high-resolution neural recording with minimal invasiveness and can be readily transferred to clinical settings for future neural interface applications such as brain-machine interfaces (BMIs) and the treatment of neurological diseases.

Suggested Citation

  • Xingzhao Wang & Shun Wu & Hantao Yang & Yu Bao & Zhi Li & Changchun Gan & Yuanyuan Deng & Junyan Cao & Xue Li & Yun Wang & Chi Ren & Zhigang Yang & Zhengtuo Zhao, 2024. "Intravascular delivery of an ultraflexible neural electrode array for recordings of cortical spiking activity," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53720-5
    DOI: 10.1038/s41467-024-53720-5
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    References listed on IDEAS

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    1. Leigh R. Hochberg & Daniel Bacher & Beata Jarosiewicz & Nicolas Y. Masse & John D. Simeral & Joern Vogel & Sami Haddadin & Jie Liu & Sydney S. Cash & Patrick van der Smagt & John P. Donoghue, 2012. "Reach and grasp by people with tetraplegia using a neurally controlled robotic arm," Nature, Nature, vol. 485(7398), pages 372-375, May.
    2. Leigh R. Hochberg & Mijail D. Serruya & Gerhard M. Friehs & Jon A. Mukand & Maryam Saleh & Abraham H. Caplan & Almut Branner & David Chen & Richard D. Penn & John P. Donoghue, 2006. "Neuronal ensemble control of prosthetic devices by a human with tetraplegia," Nature, Nature, vol. 442(7099), pages 164-171, July.
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