|
1
|
Johnsen KA, Cruzado NA, Menard ZC, Willats AA, Charles AS, Markowitz JE, Rozell CJ. Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.01.27.525963. [PMID: 39026717 PMCID: PMC11257437 DOI: 10.1101/2023.01.27.525963] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Systems neuroscience has experienced an explosion of new tools for reading and writing neural activity, enabling exciting new experiments such as all-optical or closed-loop control that effect powerful causal interventions. At the same time, improved computational models are capable of reproducing behavior and neural activity with increasing fidelity. Unfortunately, these advances have drastically increased the complexity of integrating different lines of research, resulting in the missed opportunities and untapped potential of suboptimal experiments. Experiment simulation can help bridge this gap, allowing model and experiment to better inform each other by providing a low-cost testbed for experiment design, model validation, and methods engineering. Specifically, this can be achieved by incorporating the simulation of the experimental interface into our models, but no existing tool integrates optogenetics, two-photon calcium imaging, electrode recording, and flexible closed-loop processing with neural population simulations. To address this need, we have developed Cleo: the Closed-Loop, Electrophysiology, and Optophysiology experiment simulation testbed. Cleo is a Python package enabling injection of recording and stimulation devices as well as closed-loop control with realistic latency into a Brian spiking neural network model. It is the only publicly available tool currently supporting two-photon and multi-opsin/wavelength optogenetics. To facilitate adoption and extension by the community, Cleo is open-source, modular, tested, and documented, and can export results to various data formats. Here we describe the design and features of Cleo, validate output of individual components and integrated experiments, and demonstrate its utility for advancing optogenetic techniques in prospective experiments using previously published systems neuroscience models.
Collapse
Affiliation(s)
- Kyle A. Johnsen
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | | | - Zachary C. Menard
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Adam A. Willats
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Adam S. Charles
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey E. Markowitz
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | | |
Collapse
|
|
2
|
Zhang X, Ding J, Zou L, Tian H, Fang Y, Wang J. Electrodeposited NaYF 4:Yb 3+, Er 3+ up-conversion films for flexible neural device construction and near-infrared optogenetics. J Mater Chem B 2023. [PMID: 36939747 DOI: 10.1039/d2tb02665a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2023]
Abstract
Near-infrared optogenetics based on up-conversion materials provides a promising tool for the dissection of neural circuit functions in deep brain regions. However, it remains a challenge to combine near-infrared up-conversion optogenetic stimulation with high-density electrophysiological recording in a minimally invasive manner. Here, we develop a flexible device for simultaneous electrophysiological recording and near-infrared optogenetics. The flexible device is constructed by integrating polymer-based flexible recording microelectrodes with electrodeposited NaYF4:Yb3+, Er3+ up-conversion films that can convert deep-tissue-penetrating near-infrared light into visible light for optogenetic activation of C1V1-expressing neurons. The emission properties of the up-conversion films are optimized for green light emission to stimulate C1V1 opsins. Owing to their minimized surgical footprint and high mechanical compliance, chronically implanted devices enable simultaneous electrophysiological recording and near-infrared optogenetic modulation of neuronal activities in the brain.
Collapse
Affiliation(s)
- Xuran Zhang
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, China. .,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jianfei Ding
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, China.
| | - Liang Zou
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, China. .,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Huihui Tian
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, China.
| | - Ying Fang
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, China. .,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Chinese Institute for Brain Research, Beijing, 102206, China
| | - Jinfen Wang
- CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, 100190, China.
| |
Collapse
|
|
3
|
Ryu D, Lee Y, Lee Y, Lee Y, Hwang S, Kim YK, Jun SB, Lee HW, Ji CH. Silicon optrode array with monolithically integrated SU-8 waveguide and single LED light source. J Neural Eng 2022; 19. [PMID: 35797969 DOI: 10.1088/1741-2552/ac7f5f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 07/07/2022] [Indexed: 11/11/2022]
Abstract
This paper presents a conventional LED (light emitting diode) and polymer waveguide coupled silicon optrode array. Unique lens design at the waveguide inlet enables a high light coupling efficiency with a single LED light source, and provides small power consumption compatible with a wireless optogenetic neuromodulation system. To increase the light intensity at the waveguide tip, a lensed waveguide is fabricated with epoxy-based photoresist SU-8, which has a plano-convex lens shape at the waveguide inlet to focus the light in the horizontal direction. In addition, a cylindrical lens is assembled in front of the waveguide inlet to focus the source light in the vertical direction. The glass cylindrical lens and SU-8 plano-convex lens increased the light coupling efficiency by 6.7 dB and 6.6 dB, respectively. The fabricated 1×4 array of optrodes is assembled with a single LED with 465 nm wavelength, which produces a light intensity of approximately 2.7 mW/mm2 at the SU-8 waveguide outlet when 50 mA input current is applied to the LED. Each optrode has four recording electrodes at the SU-8 waveguide outlet. The average impedance of the iridium oxide (IrOx) electroplated recording electrodes is 43.6 kΩ. In-vivo experiment at the hippocampus region CA1 and CA2 demonstrated the capability of optical stimulation and neural signal recording through the LED and SU-8 waveguide coupled silicon optrode array.
Collapse
Affiliation(s)
- Daeho Ryu
- Electrical and computer engineering, Seoul National University, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, Korea (the Republic of)
| | - Youjin Lee
- Department of Electronic and Electrical Engineering, Graduate Program in Smart Factory, Ewha Womans University, Asan Engineering Building, Seoul, 03760, Korea (the Republic of)
| | - Yongseung Lee
- Department of Electrical and Computer Engineering, , Seoul National University, 301 Dong 1116 Ho, Gwanak-gu, Seoul, 08826, Korea (the Republic of)
| | - Yena Lee
- Department of Electronic and Electrical Engineering, Ewha Womans University, Asan Engineering Building, Seoul, 03760, Korea (the Republic of)
| | - Seoyoung Hwang
- Department of Electronic and Electrical Engineering, Ewha Womans University, Asan Engineering Building, Seoul, 03760, Korea (the Republic of)
| | - Yong-Kweon Kim
- Department of Electrical and Computer Engineering, Graduate School of Engineering Practice, Seoul National University, Seoul National University, PO Box 34, Kwanak, Seoul 151-600, Korea, Gwanak-gu, Seoul, 08826, Korea (the Republic of)
| | - Sang Beom Jun
- Department of Electronic and Electrical Engineering, Graduate Program in Smart Factory, Ewha Womans University, Department of Brain and Cognitive Sciences, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemungu, Seoul, 03760, Korea (the Republic of)
| | - Hyang Woon Lee
- Departments of Neurology, Medical Science, and Computational Medicine, Graduate Program in System Health Science and Engineering, Ewha Womans University, Ewha Womans University Medical Center, Seoul, 03760, Korea (the Republic of)
| | - Chang-Hyeon Ji
- Department of Electronics and Electrical Engineering, Graduate Program in Smart Factory, Ewha Womans University, Asan Engineering Building #432, Seoul, Republic of Korea, Seoul, 03760, Korea (the Republic of)
| |
Collapse
|
|
4
|
Shen J, Xu Y, Xiao Z, Liu Y, Liu H, Wang F, Yao W, Yan Z, Zhang M, Wu Z, Liu Y, Pun SH, Lei TC, Vai MI, Mak PU, Chen C, Zhang B. Influence of the Surface Material and Illumination upon the Performance of a Microelectrode/Electrolyte Interface in Optogenetics. MICROMACHINES 2021; 12:1061. [PMID: 34577704 PMCID: PMC8471589 DOI: 10.3390/mi12091061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 08/23/2021] [Accepted: 08/27/2021] [Indexed: 11/17/2022]
Abstract
Integrated optrodes for optogenetics have been becoming a significant tool in neuroscience through the combination of offering accurate stimulation to target cells and recording biological signals simultaneously. This makes it not just be widely used in neuroscience researches, but also have a great potential to be employed in future treatments in clinical neurological diseases. To optimize the integrated optrodes, this paper aimed to investigate the influence of surface material and illumination upon the performance of the microelectrode/electrolyte interface and build a corresponding evaluation system. In this work, an integrated planar optrode with a blue LED and microelectrodes was designed and fabricated. The charge transfer mechanism on the interface was theoretically modeled and experimentally verified. An evaluation system for assessing microelectrodes was also built up. Using this system, the proposed model of various biocompatible surface materials on microelectrodes was further investigated under different illumination conditions. The influence of illumination on the microelectrode/electrolyte interface was the cause of optical artifacts, which interfere the biological signal recording. It was found that surface materials had a great effect on the charge transfer capacity, electrical stability and recoverability, photostability, and especially optical artifacts. The metal with better charge transfer capacity and electrical stability is highly possible to have a better performance on the optical artifacts, regardless of its electrical recoverability and photostability under the illumination conditions of optogenetics. Among the five metals used in our investigation, iridium served as the best surface material for the proposed integrated optrodes. Thus, optimizing the surface material for optrodes could reduce optical interference, enhance the quality of the neural signal recording for optogenetics, and thus help to advance the research in neuroscience.
Collapse
Grants
- 62061160368 & 0022/2020/AFJ This research was funded by the joint funding of the Nature Science Foundation of China (NSFC) & the Macao Science and Technology Development Fund (FDCT) of China
- 2019B010132003, 2019B010132001 Science & Technology Plan of Guangdong Province, China
- 2016YFB0400105, 2017YFB0403001 the National Key Research and Development Program
- 20167612042080001 the Zhuhai Key Technology Laboratory of Wide Bandgap Semiconductor Power Electronics, Sun Yat-sen University, China
- 088/2016/A2, 0144/2019/A3, 0022/2020/AFJ, SKL-AMSV (FDCT-funded), SKL-AMSV-ADDITIONAL FUND, SKL-AMSV(UM)-2020-2022 the Science and Technology Development Fund, Macau SAR
- MYRG2018-00146-AMSV, MYRG2019-00056-AMSV the University of Macau
- 2020YFB1313502 the National Key R&D Program of China
Collapse
Affiliation(s)
- Junyu Shen
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
| | - Yanyan Xu
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
| | - Zhengwen Xiao
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
| | - Yuebo Liu
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
| | - Honghui Liu
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
| | - Fengge Wang
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
| | - Wanqing Yao
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
| | - Zhaokun Yan
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
| | - Minjie Zhang
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
| | - Zhisheng Wu
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
- State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
| | - Yang Liu
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
- State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
| | - Sio Hang Pun
- State Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau 999078, China; (S.H.P.); (M.I.V.); (C.C.)
| | - Tim C. Lei
- Department of Electrical Engineering, University of Colorado Denver, Denver, CO 80204, USA;
| | - Mang I Vai
- State Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau 999078, China; (S.H.P.); (M.I.V.); (C.C.)
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China;
| | - Peng Un Mak
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China;
| | - Changhao Chen
- State Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau 999078, China; (S.H.P.); (M.I.V.); (C.C.)
| | - Baijun Zhang
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (J.S.); (Y.X.); (Z.X.); (Y.L.); (H.L.); (F.W.); (W.Y.); (Z.Y.); (M.Z.); (Z.W.); (Y.L.)
- State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
| |
Collapse
|