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Zhou C, Tian Y, Li G, Ye Y, Gao L, Li J, Liu Z, Su H, Lu Y, Li M, Zhou Z, Wei X, Qin L, Tao TH, Sun L. Through-polymer, via technology-enabled, flexible, lightweight, and integrated devices for implantable neural probes. MICROSYSTEMS & NANOENGINEERING 2024; 10:54. [PMID: 38654844 PMCID: PMC11035623 DOI: 10.1038/s41378-024-00691-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 02/29/2024] [Accepted: 03/11/2024] [Indexed: 04/26/2024]
Abstract
In implantable electrophysiological recording systems, the headstage typically comprises neural probes that interface with brain tissue and integrated circuit chips for signal processing. While advancements in MEMS and CMOS technology have significantly improved these components, their interconnection still relies on conventional printed circuit boards and sophisticated adapters. This conventional approach adds considerable weight and volume to the package, especially for high channel count systems. To address this issue, we developed a through-polymer via (TPV) method inspired by the through-silicon via (TSV) technique in advanced three-dimensional packaging. This innovation enables the vertical integration of flexible probes, amplifier chips, and PCBs, realizing a flexible, lightweight, and integrated device (FLID). The total weight of the FLIDis only 25% that of its conventional counterparts relying on adapters, which significantly increased the activity levels of animals wearing the FLIDs to nearly match the levels of control animals without implants. Furthermore, by incorporating a platinum-iridium alloy as the top layer material for electrical contact, the FLID realizes exceptional electrical performance, enabling in vivo measurements of both local field potentials and individual neuron action potentials. These findings showcase the potential of FLIDs in scaling up implantable neural recording systems and mark a significant advancement in the field of neurotechnology.
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Grants
- This work was partially supported by the National Key R & D Program of China (Grant Nos. 2021ZD0201600, 2022YFF0706504, 2022ZD0209300, 2019YFA0905200, 2021YFC2501500, 2021YFF1200700, 2022ZD0212300), National Natural Science Foundation of China (Grant No. 61974154), Key Research Program of Frontier Sciences, CAS (Grant No. ZDBS-LY-JSC024), Shanghai Pilot Program for Basic Research-Chinese Academy of Science, Shanghai Branch (Grant No. JCYJ-SHFY-2022-01 and JCYJ-SHFY-2022-0xx), Shanghai Municipal Science and Technology Major Project (Grant No. 2021SHZDZX), CAS Pioneer Hundred Talents Program, Shanghai Pujiang Program (Grant Nos. 21PJ1415100, 19PJ1410900), the Science and Technology Commission Foundation of Shanghai (Nos. 21JM0010200 and 21142200300), Shanghai Rising-Star Program (Grant No. 22QA1410900), Shanghai Sailing Program (No. 22YF1454700), the Innovative Research Team of High-level Local Universities in Shanghai, the Jiangxi Province 03 Special Project and 5G Project (Grant No. 20212ABC03W07), Fund for Central Government in Guidance of Local Science and Technology Development (Grant No. 20201ZDE04013), Special Fund for Science and Technology Innovation Strategy of Guangdong Province (Grant Nos. 2021B0909060002, 2021B0909050004).
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Affiliation(s)
- Cunkai Zhou
- College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Ye Tian
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, China
| | - Gen Li
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, China
| | - Yifei Ye
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Lusha Gao
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Jiazhi Li
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Ziwei Liu
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Haoyang Su
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Yunxiao Lu
- College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Meng Li
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Zhitao Zhou
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Xiaoling Wei
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Lunming Qin
- College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China
| | - Tiger H. Tao
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, China
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- Neuroxess Co., Ltd. (Jiangxi), Nanchang, Jiangxi China
- Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai, Guangdong China
- Tianqiao and Chrissy Chen Institute for Translational Research, Shanghai, China
| | - Liuyang Sun
- College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
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Sun Y, Xiao Z, Chen B, Zhao Y, Dai J. Advances in Material-Assisted Electromagnetic Neural Stimulation. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2400346. [PMID: 38594598 DOI: 10.1002/adma.202400346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 03/26/2024] [Indexed: 04/11/2024]
Abstract
Bioelectricity plays a crucial role in organisms, being closely connected to neural activity and physiological processes. Disruptions in the nervous system can lead to chaotic ionic currents at the injured site, causing disturbances in the local cellular microenvironment, impairing biological pathways, and resulting in a loss of neural functions. Electromagnetic stimulation has the ability to generate internal currents, which can be utilized to counter tissue damage and aid in the restoration of movement in paralyzed limbs. By incorporating implanted materials, electromagnetic stimulation can be targeted more accurately, thereby significantly improving the effectiveness and safety of such interventions. Currently, there have been significant advancements in the development of numerous promising electromagnetic stimulation strategies with diverse materials. This review provides a comprehensive summary of the fundamental theories, neural stimulation modulating materials, material application strategies, and pre-clinical therapeutic effects associated with electromagnetic stimulation for neural repair. It offers a thorough analysis of current techniques that employ materials to enhance electromagnetic stimulation, as well as potential therapeutic strategies for future applications.
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Affiliation(s)
- Yuting Sun
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhifeng Xiao
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Bing Chen
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Yannan Zhao
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jianwu Dai
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Tianjin Key Laboratory of Biomedical Materials, Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300192, China
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Pimenta S, Freitas JR, Correia JH. Flexible neural probes: a review of the current advantages, drawbacks, and future demands. J Zhejiang Univ Sci B 2024; 25:153-167. [PMID: 38303498 PMCID: PMC10835206 DOI: 10.1631/jzus.b2300337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/04/2023] [Indexed: 02/03/2024]
Abstract
Brain diseases affect millions of people and have a huge social and economic impact. The use of neural probes for studies in animals has been the main approach to increasing knowledge about neural network functioning. Ultimately, neuroscientists are trying to develop new and more effective therapeutic approaches to treating neurological disorders. The implementation of neural probes with multifunctionalities (electrical, optical, and fluidic interactions) has been increasing in the last few years, leading to the creation of devices with high temporal and spatial resolution. Increasing the applicability of, and elements integrated into, neural probes has also led to the necessity to create flexible interfaces, reducing neural tissue damage during probe implantation and increasing the quality of neural acquisition data. In this paper, we review the fabrication, characterization, and validation of several types of flexible neural probes, exploring the main advantages and drawbacks of these devices. Finally, future developments and applications are covered. Overall, this review aims to present the currently available flexible devices and future appropriate avenues for development as possible guidance for future engineered devices.
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Affiliation(s)
- Sara Pimenta
- CMEMS-UMinho, University of Minho, Guimares 4800-058, Portugal.
- LABBELS-Associate Laboratory, Braga/Guimares, Portugal.
| | - Joo R Freitas
- CMEMS-UMinho, University of Minho, Guimares 4800-058, Portugal
| | - Jos H Correia
- CMEMS-UMinho, University of Minho, Guimares 4800-058, Portugal
- LABBELS-Associate Laboratory, Braga/Guimares, Portugal
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Sun Y, Shen A, Du C, Sun J, Chen X, Gao X. A Real-Time Non-Implantation Bi-Directional Brain-Computer Interface Solution Without Stimulation Artifacts. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3566-3575. [PMID: 37665696 DOI: 10.1109/tnsre.2023.3311750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
The non-implantation bi-directional brain-computer interface (BCI) is a neural interface technology that enables direct two-way communication between the brain and the external world by both "reading" neural signals and "writing" stimulation patterns to the brain. This technology has vast potential applications, such as improving the quality of life for individuals with neurological and mental illnesses and even expanding the boundaries of human capabilities. Nonetheless, non-implantation bi-directional BCIs face challenges in generating real-time feedback and achieving compatibility between stimulation and recording. These issues arise due to the considerable overlap between electrical stimulation frequencies and electrophysiological recording frequencies, as well as the impediment caused by the skull to the interaction of external and internal currents. To address those challenges, this work proposes a novel solution that combines the temporal interference stimulation paradigm and minimally invasive skull modification. A longitudinal animal experiment has preliminarily validated the feasibility of the proposed method. In signal recording experiments, the average impedance of our scheme decreased by 4.59 kΩ , about 67%, compared to the conventional technique at 18 points. The peak-to-peak value of the Somatosensory Evoked Potential increased by 8%. Meanwhile, the signal-to-noise ratio of Steady-State Visual Evoked Potential increased by 5.13 dB, and its classification accuracy increased by 44%. The maximum bandwidth of the resting state rose by 63%. In electrical stimulation experiments, the signal-to-noise ratio of the low-frequency response evoked by our scheme rose by 8.04 dB, and no stimulation artifacts were generated. The experimental results show that signal quality in acquisition has significantly improved, and frequency-band isolation eliminates stimulation artifacts at the source. The acquisition and stimulation pathways are real-time compatible in this non-implantation bi-directional BCI solution, which can provide technical support and theoretical guidance for creating closed-loop adaptive systems coupled with particular application scenarios in the future.
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Guo T, Chang YC, Li L, Dokos S, Li L. Editorial: Advances in bioelectronics and stimulation strategies for next generation neuroprosthetics. Front Neurosci 2023; 16:1116900. [PMID: 36704005 PMCID: PMC9872720 DOI: 10.3389/fnins.2022.1116900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 12/13/2022] [Indexed: 01/11/2023] Open
Affiliation(s)
- Tianruo Guo
- Graduate School of Biomedical Engineering, The University of New South Wales Sydney, Sydney, NSW, Australia
| | - Yao-chuan Chang
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, United States,Medtronic PLC, Minneapolis, MN, United States
| | - Luming Li
- National Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China,Precision Medicine and Healthcare Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China,IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
| | - Socrates Dokos
- Graduate School of Biomedical Engineering, The University of New South Wales Sydney, Sydney, NSW, Australia
| | - Liming Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China,*Correspondence: Liming Li ✉
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