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Zhao Z, Feng Y, Wang M, Wei J, Tan T, Li R, Hu H, Wang M, Chen P, Gao X, Wei Y, Wang C, Gao Z, Jiang W, Zhou X, Li M, Wang C, Pang T, Yu Y. Investigating cortical complexity and connectivity in rats with schizophrenia. Front Neuroinform 2024; 18:1392271. [PMID: 39211912 PMCID: PMC11358091 DOI: 10.3389/fninf.2024.1392271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 07/29/2024] [Indexed: 09/04/2024] Open
Abstract
Background The above studies indicate that the SCZ animal model has abnormal gamma oscillations and abnormal functional coupling ability of brain regions at the cortical level. However, few researchers have focused on the correlation between brain complexity and connectivity at the cortical level. In order to provide a more accurate representation of brain activity, we studied the complexity of electrocorticogram (ECoG) signals and the information interaction between brain regions in schizophrenic rats, and explored the correlation between brain complexity and connectivity. Methods We collected ECoG signal from SCZ rats. The frequency domain and time domain functional connectivity of SCZ rats were evaluated by magnitude square coherence and mutual information (MI). Permutation entropy (PE) and permutation Lempel-Ziv complexity (PLZC) were used to analyze the complexity of ECoG, and the relationship between them was evaluated. In addition, in order to further understand the causal structure of directional information flow among brain regions, we used phase transfer entropy (PTE) to analyze the effective connectivity of the brain. Results Firstly, in the high gamma band, the complexity of brain regions in SCZ rats is higher than that in normal rats, and the neuronal activity is irregularity. Secondly, the information integration ability of SCZ rats decreased and the communication of brain network information was hindered at the cortical level. Finally, compared with normal rats, the causal relationship between brain regions of SCZ rats was closer, but the information interaction center was not clear. Conclusion The above findings suggest that at the cortical level, complexity and connectivity are valid biomarkers for identifying SCZ. This bridges the gap between peak potentials and EEG. This may help to understand the pathophysiological mechanisms at the cortical level in schizophrenics.
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Affiliation(s)
- Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Yifan Feng
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Menghan Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Jiarong Wei
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Tao Tan
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Ruijiao Li
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Heshun Hu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Mengke Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Peiqi Chen
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Xudong Gao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Yinping Wei
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Chang Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Zhixian Gao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Wenshuai Jiang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Xuezhi Zhou
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Mingcai Li
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Chong Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Ting Pang
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Center of Image and Signal Processing, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Yi Yu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
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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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Chatterjee S, Joshi RK, Sakorikar T, Behera B, Bhaskar N, Kv SG, Jayachandra M, Pandya HJ. Design and fabrication of a microelectrode array for studying epileptiform discharges from rodents. Biomed Microdevices 2023; 25:31. [PMID: 37584876 DOI: 10.1007/s10544-023-00672-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/10/2023] [Indexed: 08/17/2023]
Abstract
Local field potentials, the extracellular electrical activities from brain regions, provide clinically relevant information about the status of neurophysiological conditions, including epilepsy. In this study, a 13-channel silicon-based single-shank microelectrode array (MEA) was designed and fabricated to record local field potentials (LFPs) from the different depths of a rat's brain. A titanium/gold layer was patterned as electrodes on an oxidized silicon substrate, and silicon dioxide was deposited as a passivation layer. The fabricated array was implanted in the somatosensory cortex of the right hemisphere of an anesthetized rat. The developed MEA was interfaced with an OpenBCI Cyton Daisy Biosensing Board to acquire the local field potentials. The LFPs were acquired at three different neurophysiological conditions, including baseline signals, chemically-induced epileptiform discharges, and recovered baseline signals after anti-epileptic drug (AED) administration. Further, time-frequency analyses were performed on the acquired biopotentials to study the difference in spatiotemporal features. The processed signals and time-frequency analyses clearly distinguish between pre-convulsant and post-AED baselines and evoked epileptiform discharges.
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Affiliation(s)
- Suman Chatterjee
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Rathin K Joshi
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Tushar Sakorikar
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Bhagaban Behera
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Nitu Bhaskar
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | - Shabari Girishan Kv
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
- MS Ramaiah Memorial Hospital, Bangalore, India
| | - Mahesh Jayachandra
- Center for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India
| | - Hardik J Pandya
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India.
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Yi D, Yao Y, Wang Y, Chen L. Manufacturing Processes of Implantable Microelectrode Array for In Vivo Neural Electrophysiological Recordings and Stimulation: A State-Of-the-Art Review. JOURNAL OF MICRO- AND NANO-MANUFACTURING 2022; 10:041001. [PMID: 37860671 PMCID: PMC10583290 DOI: 10.1115/1.4063179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 08/08/2023] [Indexed: 10/21/2023]
Abstract
Electrophysiological recording and stimulation of neuron activities are important for us to understand the function and dysfunction of the nervous system. To record/stimulate neuron activities as voltage fluctuation extracellularly, microelectrode array (MEA) implants are a promising tool to provide high temporal and spatial resolution for neuroscience studies and medical treatments. The design configuration and recording capabilities of the MEAs have evolved dramatically since their invention and manufacturing process development has been a key driving force for such advancement. Over the past decade, since the White House Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative launched in 2013, advanced manufacturing processes have enabled advanced MEAs with increased channel count and density, access to more brain areas, more reliable chronic performance, as well as minimal invasiveness and tissue reaction. In this state-of-the-art review paper, three major types of electrophysiological recording MEAs widely used nowadays, namely, microwire-based, silicon-based, and flexible MEAs are introduced and discussed. Conventional design and manufacturing processes and materials used for each type are elaborated, followed by a review of further development and recent advances in manufacturing technologies and the enabling new designs and capabilities. The review concludes with a discussion on potential future directions of manufacturing process development to enable the long-term goal of large-scale high-density brain-wide chronic recordings in freely moving animals.
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Affiliation(s)
- Dongyang Yi
- Department of Mechanical and Industrial Engineering, University of Massachusetts Lowell, 1 University Avenue, Lowell, MA 01854
| | - Yao Yao
- Department of Industrial and Systems Engineering, University of Missouri, 416 South 6th Street, Columbia, MO 65211
| | - Yi Wang
- Department of Industrial and Systems Engineering, University of Missouri, E3437C Thomas & Nell Lafferre Hall, 416 South 6th Street, Columbia, MO 65211
| | - Lei Chen
- Department of Mechanical and Industrial Engineering, University of Massachusetts Lowell, 1 University Avenue, Lowell, MA 01854
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