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Wu Y, Temple BA, Sevilla N, Zhang J, Zhu H, Zolotavin P, Jin Y, Duarte D, Sanders E, Azim E, Nimmerjahn A, Pfaff SL, Luan L, Xie C. Ultraflexible electrodes for recording neural activity in the mouse spinal cord during motor behavior. Cell Rep 2024; 43:114199. [PMID: 38728138 PMCID: PMC11233142 DOI: 10.1016/j.celrep.2024.114199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 03/10/2024] [Accepted: 04/22/2024] [Indexed: 05/12/2024] Open
Abstract
Implantable electrode arrays are powerful tools for directly interrogating neural circuitry in the brain, but implementing this technology in the spinal cord in behaving animals has been challenging due to the spinal cord's significant motion with respect to the vertebral column during behavior. Consequently, the individual and ensemble activity of spinal neurons processing motor commands remains poorly understood. Here, we demonstrate that custom ultraflexible 1-μm-thick polyimide nanoelectronic threads can conduct laminar recordings of many neuronal units within the lumbar spinal cord of unrestrained, freely moving mice. The extracellular action potentials have high signal-to-noise ratio, exhibit well-isolated feature clusters, and reveal diverse patterns of activity during locomotion. Furthermore, chronic recordings demonstrate the stable tracking of single units and their functional tuning over multiple days. This technology provides a path for elucidating how spinal circuits compute motor actions.
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Affiliation(s)
- Yu Wu
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77030, USA
| | - Benjamin A Temple
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA; Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Nicole Sevilla
- Rice Neuroengineering Initiative, Rice University, Houston, TX 77030, USA; Department of Bioengineering, Rice University, Houston, TX 77030, USA
| | - Jiaao Zhang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77030, USA
| | - Hanlin Zhu
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77030, USA
| | - Pavlo Zolotavin
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77030, USA
| | - Yifu Jin
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77030, USA
| | - Daniela Duarte
- Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Elischa Sanders
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Eiman Azim
- Molecular Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Axel Nimmerjahn
- Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Samuel L Pfaff
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
| | - Lan Luan
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77030, USA; Department of Bioengineering, Rice University, Houston, TX 77030, USA.
| | - Chong Xie
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77030, USA; Department of Bioengineering, Rice University, Houston, TX 77030, USA.
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2
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Xiang Y, Zhao Y, Cheng T, Sun S, Wang J, Pei R. Implantable Neural Microelectrodes: How to Reduce Immune Response. ACS Biomater Sci Eng 2024; 10:2762-2783. [PMID: 38591141 DOI: 10.1021/acsbiomaterials.4c00238] [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] [Indexed: 04/10/2024]
Abstract
Implantable neural microelectrodes exhibit the great ability to accurately capture the electrophysiological signals from individual neurons with exceptional submillisecond precision, holding tremendous potential for advancing brain science research, as well as offering promising avenues for neurological disease therapy. Although significant advancements have been made in the channel and density of implantable neural microelectrodes, challenges persist in extending the stable recording duration of these microelectrodes. The enduring stability of implanted electrode signals is primarily influenced by the chronic immune response triggered by the slight movement of the electrode within the neural tissue. The intensity of this immune response increases with a higher bending stiffness of the electrode. This Review thoroughly analyzes the sequential reactions evoked by implanted electrodes in the brain and highlights strategies aimed at mitigating chronic immune responses. Minimizing immune response mainly includes designing the microelectrode structure, selecting flexible materials, surface modification, and controlling drug release. The purpose of this paper is to provide valuable references and ideas for reducing the immune response of implantable neural microelectrodes and stimulate their further exploration in the field of brain science.
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Affiliation(s)
- Ying Xiang
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China (USTC), Hefei 230026, PR China
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
| | - Yuewu Zhao
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
| | - Tingting Cheng
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
| | - Shengkai Sun
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
| | - Jine Wang
- Jiangxi Institute of Nanotechnology, Nanchang 330200, China
- College of Medicine and Nursing, Shandong Provincial Engineering Laboratory of Novel Pharmaceutical Excipients, Sustained and Controlled Release Preparations, Dezhou University, Dezhou 253023, China
| | - Renjun Pei
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China (USTC), Hefei 230026, PR China
- CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
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3
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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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4
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Vareberg AD, Bok I, Eizadi J, Ren X, Hai A. Inference of network connectivity from temporally binned spike trains. J Neurosci Methods 2024; 404:110073. [PMID: 38309313 PMCID: PMC10949361 DOI: 10.1016/j.jneumeth.2024.110073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 01/19/2024] [Accepted: 01/30/2024] [Indexed: 02/05/2024]
Abstract
BACKGROUND Processing neural activity to reconstruct network connectivity is a central focus of neuroscience, yet the spatiotemporal requisites of biological nervous systems are challenging for current neuronal sensing modalities. Consequently, methods that leverage limited data to successfully infer synaptic connections, predict activity at single unit resolution, and decipher their effect on whole systems, can uncover critical information about neural processing. Despite the emergence of powerful methods for inferring connectivity, network reconstruction based on temporally subsampled data remains insufficiently unexplored. NEW METHOD We infer synaptic weights by processing firing rates within variable time bins for a heterogeneous feed-forward network of excitatory, inhibitory, and unconnected units. We assess classification and optimize model parameters for postsynaptic spike train reconstruction. We test our method on a physiological network of leaky integrate-and-fire neurons displaying bursting patterns and assess prediction of postsynaptic activity from microelectrode array data. RESULTS Results reveal parameters for improved prediction and performance and suggest that lower resolution data and limited access to neurons can be preferred. COMPARISON WITH EXISTING METHOD(S) Recent computational methods demonstrate highly improved reconstruction of connectivity from networks of parallel spike trains by considering spike lag, time-varying firing rates, and other underlying dynamics. However, these methods insufficiently explore temporal subsampling representative of novel data types. CONCLUSIONS We provide a framework for reverse engineering neural networks from data with limited temporal quality, describing optimal parameters for each bin size, which can be further improved using non-linear methods and applied to more complicated readouts and connectivity distributions in multiple brain circuits.
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Affiliation(s)
- Adam D Vareberg
- Department of Biomedical Engineering, University of Wisconsin-Madison, United States; Wisconsin Institute for Translational Neuroengineering (WITNe), University of Wisconsin-Madison, United States
| | - Ilhan Bok
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, United States; Wisconsin Institute for Translational Neuroengineering (WITNe), University of Wisconsin-Madison, United States
| | - Jenna Eizadi
- Department of Biomedical Engineering, University of Wisconsin-Madison, United States; Wisconsin Institute for Translational Neuroengineering (WITNe), University of Wisconsin-Madison, United States
| | - Xiaoxuan Ren
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, United States
| | - Aviad Hai
- Department of Biomedical Engineering, University of Wisconsin-Madison, United States; Department of Electrical and Computer Engineering, University of Wisconsin-Madison, United States; Wisconsin Institute for Translational Neuroengineering (WITNe), University of Wisconsin-Madison, United States.
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5
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Meneghetti N, Vannini E, Mazzoni A. Rodents' visual gamma as a biomarker of pathological neural conditions. J Physiol 2024; 602:1017-1048. [PMID: 38372352 DOI: 10.1113/jp283858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 01/23/2024] [Indexed: 02/20/2024] Open
Abstract
Neural gamma oscillations (indicatively 30-100 Hz) are ubiquitous: they are associated with a broad range of functions in multiple cortical areas and across many animal species. Experimental and computational works established gamma rhythms as a global emergent property of neuronal networks generated by the balanced and coordinated interaction of excitation and inhibition. Coherently, gamma activity is strongly influenced by the alterations of synaptic dynamics which are often associated with pathological neural dysfunctions. We argue therefore that these oscillations are an optimal biomarker for probing the mechanism of cortical dysfunctions. Gamma oscillations are also highly sensitive to external stimuli in sensory cortices, especially the primary visual cortex (V1), where the stimulus dependence of gamma oscillations has been thoroughly investigated. Gamma manipulation by visual stimuli tuning is particularly easy in rodents, which have become a standard animal model for investigating the effects of network alterations on gamma oscillations. Overall, gamma in the rodents' visual cortex offers an accessible probe on dysfunctional information processing in pathological conditions. Beyond vision-related dysfunctions, alterations of gamma oscillations in rodents were indeed also reported in neural deficits such as migraine, epilepsy and neurodegenerative or neuropsychiatric conditions such as Alzheimer's, schizophrenia and autism spectrum disorders. Altogether, the connections between visual cortical gamma activity and physio-pathological conditions in rodent models underscore the potential of gamma oscillations as markers of neuronal (dys)functioning.
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Affiliation(s)
- Nicolò Meneghetti
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence for Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Eleonora Vannini
- Neuroscience Institute, National Research Council (CNR), Pisa, Italy
| | - Alberto Mazzoni
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence for Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
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Hoffmann M, Henninger J, Veith J, Richter L, Judkewitz B. Blazed oblique plane microscopy reveals scale-invariant inference of brain-wide population activity. Nat Commun 2023; 14:8019. [PMID: 38049412 PMCID: PMC10695970 DOI: 10.1038/s41467-023-43741-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 11/17/2023] [Indexed: 12/06/2023] Open
Abstract
Due to the size and opacity of vertebrate brains, it has until now been impossible to simultaneously record neuronal activity at cellular resolution across the entire adult brain. As a result, scientists are forced to choose between cellular-resolution microscopy over limited fields-of-view or whole-brain imaging at coarse-grained resolution. Bridging the gap between these spatial scales of understanding remains a major challenge in neuroscience. Here, we introduce blazed oblique plane microscopy to perform brain-wide recording of neuronal activity at cellular resolution in an adult vertebrate. Contrary to common belief, we find that inferences of neuronal population activity are near-independent of spatial scale: a set of randomly sampled neurons has a comparable predictive power as the same number of coarse-grained macrovoxels. Our work thus links cellular resolution with brain-wide scope, challenges the prevailing view that macroscale methods are generally inferior to microscale techniques and underscores the value of multiscale approaches to studying brain-wide activity.
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Affiliation(s)
- Maximilian Hoffmann
- Einstein Center for Neurosciences, NeuroCure Cluster of Excellence, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Rockefeller University, New York, USA
| | - Jörg Henninger
- Einstein Center for Neurosciences, NeuroCure Cluster of Excellence, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Veith
- Einstein Center for Neurosciences, NeuroCure Cluster of Excellence, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Department of Biology, Humboldt University Berlin, Berlin, Germany
| | - Lars Richter
- Department of Chemistry and Center for NanoScience, Ludwig Maximilians University, Munich, Germany
| | - Benjamin Judkewitz
- Einstein Center for Neurosciences, NeuroCure Cluster of Excellence, Charité - Universitätsmedizin Berlin, Berlin, Germany.
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7
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Yao P, Liu R, Broggini T, Thunemann M, Kleinfeld D. Construction and use of an adaptive optics two-photon microscope with direct wavefront sensing. Nat Protoc 2023; 18:3732-3766. [PMID: 37914781 PMCID: PMC11033548 DOI: 10.1038/s41596-023-00893-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 07/24/2023] [Indexed: 11/03/2023]
Abstract
Two-photon microscopy, combined with the appropriate optical labelling, enables the measurement and tracking of submicrometer structures within brain cells, as well as the spatiotemporal mapping of spikes in individual neurons and of neurotransmitter release in individual synapses. Yet, the spatial resolution of two-photon microscopy rapidly degrades as imaging is attempted at depths of more than a few scattering lengths into tissue, i.e., below the superficial layers that constitute the top 300-400 µm of the neocortex. To obviate this limitation, we shape the focal volume, generated by the excitation beam, by modulating the incident wavefront via guidestar-assisted adaptive optics. Here, we describe the construction, calibration and operation of a two-photon microscope that incorporates adaptive optics to restore diffraction-limited resolution at depths close to 900 µm in the mouse cortex. Our setup detects a guidestar formed by the excitation of a red-shifted dye in blood serum, used to directly measure the wavefront. We incorporate predominantly commercially available optical, optomechanical, mechanical and electronic components, and supply computer-aided design models of other customized components. The resulting adaptive optics two-photon microscope is modular and allows for expanded imaging and optical excitation capabilities. We demonstrate our methodology in the mouse neocortex by imaging the morphology of somatostatin-expressing neurons that lie 700 µm beneath the pia, calcium dynamics of layer 5b projection neurons and thalamocortical glutamate transmission to L4 neurons. The protocol requires ~30 d to complete and is suitable for users with graduate-level expertise in optics.
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Affiliation(s)
- Pantong Yao
- Neurosciences Graduate Program, University of California San Diego, La Jolla, CA, USA
| | - Rui Liu
- Department of Physics, University of California San Diego, La Jolla, CA, USA
| | - Thomas Broggini
- Department of Physics, University of California San Diego, La Jolla, CA, USA
| | - Martin Thunemann
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - David Kleinfeld
- Neurosciences Graduate Program, University of California San Diego, La Jolla, CA, USA.
- Department of Physics, University of California San Diego, La Jolla, CA, USA.
- Department of Neurobiology, University of California San Diego, La Jolla, CA, USA.
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8
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Luan L, Yin R, Zhu H, Xie C. Emerging Penetrating Neural Electrodes: In Pursuit of Large Scale and Longevity. Annu Rev Biomed Eng 2023; 25:185-205. [PMID: 37289556 PMCID: PMC11078330 DOI: 10.1146/annurev-bioeng-090622-050507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Penetrating neural electrodes provide a powerful approach to decipher brain circuitry by allowing for time-resolved electrical detections of individual action potentials. This unique capability has contributed tremendously to basic and translational neuroscience, enabling both fundamental understandings of brain functions and applications of human prosthetic devices that restore crucial sensations and movements. However, conventional approaches are limited by the scarce number of available sensing channels and compromised efficacy over long-term implantations. Recording longevity and scalability have become the most sought-after improvements in emerging technologies. In this review, we discuss the technological advances in the past 5-10 years that have enabled larger-scale, more detailed, and longer-lasting recordings of neural circuits at work than ever before. We present snapshots of the latest advances in penetration electrode technology, showcase their applications in animal models and humans, and outline the underlying design principles and considerations to fuel future technological development.
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Affiliation(s)
- Lan Luan
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas, USA;
- Rice Neuroengineering Initiative, Rice University, Houston, Texas, USA
- Department of Bioengineering, Rice University, Houston, Texas, USA
| | - Rongkang Yin
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas, USA;
- Rice Neuroengineering Initiative, Rice University, Houston, Texas, USA
| | - Hanlin Zhu
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas, USA;
- Rice Neuroengineering Initiative, Rice University, Houston, Texas, USA
| | - Chong Xie
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas, USA;
- Rice Neuroengineering Initiative, Rice University, Houston, Texas, USA
- Department of Bioengineering, Rice University, Houston, Texas, USA
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9
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Böhler C, Vomero M, Soula M, Vöröslakos M, Porto Cruz M, Liljemalm R, Buzsaki G, Stieglitz T, Asplund M. Multilayer Arrays for Neurotechnology Applications (MANTA): Chronically Stable Thin-Film Intracortical Implants. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2207576. [PMID: 36935361 DOI: 10.1002/advs.202207576] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 02/07/2023] [Indexed: 05/18/2023]
Abstract
Flexible implantable neurointerfaces show great promise in addressing one of the major challenges of implantable neurotechnology, namely the loss of signal connected to unfavorable probe tissue interaction. The authors here show how multilayer polyimide probes allow high-density intracortical recordings to be combined with a reliable long-term stable tissue interface, thereby progressing toward chronic stability of implantable neurotechnology. The probes could record 10-60 single units over 5 months with a consistent peak-to-peak voltage at dimensions that ensure robust handling and insulation longevity. Probes that remain in intimate contact with the signaling tissue over months to years are a game changer for neuroscience and, importantly, open up for broader clinical translation of systems relying on neurotechnology to interface the human brain.
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Affiliation(s)
- Christian Böhler
- Department of Microsystems Engineering (IMTEK), University of Freiburg, 79110, Freiburg, Germany
- BrainLinks-BrainTools Center, University of Freiburg, 79110, Freiburg, Germany
| | - Maria Vomero
- Department of Microsystems Engineering (IMTEK), University of Freiburg, 79110, Freiburg, Germany
- BrainLinks-BrainTools Center, University of Freiburg, 79110, Freiburg, Germany
| | - Marisol Soula
- Neuroscience Institute, Langone Medical Center, New York University, New York, 10016, USA
| | - Mihály Vöröslakos
- Neuroscience Institute, Langone Medical Center, New York University, New York, 10016, USA
| | - Maria Porto Cruz
- Department of Microsystems Engineering (IMTEK), University of Freiburg, 79110, Freiburg, Germany
- BrainLinks-BrainTools Center, University of Freiburg, 79110, Freiburg, Germany
| | - Rickard Liljemalm
- Department of Microsystems Engineering (IMTEK), University of Freiburg, 79110, Freiburg, Germany
| | - György Buzsaki
- Neuroscience Institute, Langone Medical Center, New York University, New York, 10016, USA
- Department of Physiology and Neuroscience, Langone Medical Center, New York University, New York, 10016, USA
- Department of Neurology, Langone Medical Center, New York University, New York, 10016, USA
| | - Thomas Stieglitz
- Department of Microsystems Engineering (IMTEK), University of Freiburg, 79110, Freiburg, Germany
- BrainLinks-BrainTools Center, University of Freiburg, 79110, Freiburg, Germany
- Bernstein Center Freiburg, University of Freiburg, 79110, Freiburg, Germany
| | - Maria Asplund
- Department of Microsystems Engineering (IMTEK), University of Freiburg, 79110, Freiburg, Germany
- BrainLinks-BrainTools Center, University of Freiburg, 79110, Freiburg, Germany
- Department of Microtechnology and Nanoscience, Chalmers University of Technology, Gothenburg, SE-41296, Sweden
- Division of Nursing and Medical Technology, Luleå University of Technology, Luleå, 97187, Sweden
- Freiburg Institute for Advanced Studies (FRIAS), University of Freiburg, 79110, Freiburg, Germany
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10
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Shen K, Chen O, Edmunds JL, Piech DK, Maharbiz MM. Translational opportunities and challenges of invasive electrodes for neural interfaces. Nat Biomed Eng 2023; 7:424-442. [PMID: 37081142 DOI: 10.1038/s41551-023-01021-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 02/15/2023] [Indexed: 04/22/2023]
Abstract
Invasive brain-machine interfaces can restore motor, sensory and cognitive functions. However, their clinical adoption has been hindered by the surgical risk of implantation and by suboptimal long-term reliability. In this Review, we highlight the opportunities and challenges of invasive technology for clinically relevant electrophysiology. Specifically, we discuss the characteristics of neural probes that are most likely to facilitate the clinical translation of invasive neural interfaces, describe the neural signals that can be acquired or produced by intracranial electrodes, the abiotic and biotic factors that contribute to their failure, and emerging neural-interface architectures.
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Affiliation(s)
- Konlin Shen
- University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA.
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.
| | - Oliver Chen
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA
| | - Jordan L Edmunds
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA
| | - David K Piech
- University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA
| | - Michel M Maharbiz
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA
- Department of Bioengineering, University of California, Berkeley, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA, USA
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11
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Letner JG, Patel PR, Hsieh JC, Smith Flores IM, della Valle E, Walker LA, Weiland JD, Chestek CA, Cai D. Post-explant profiling of subcellular-scale carbon fiber intracortical electrodes and surrounding neurons enables modeling of recorded electrophysiology. J Neural Eng 2023; 20:026019. [PMID: 36848679 PMCID: PMC10022369 DOI: 10.1088/1741-2552/acbf78] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 01/12/2023] [Accepted: 02/27/2023] [Indexed: 03/01/2023]
Abstract
Objective.Characterizing the relationship between neuron spiking and the signals that electrodes record is vital to defining the neural circuits driving brain function and informing clinical brain-machine interface design. However, high electrode biocompatibility and precisely localizing neurons around the electrodes are critical to defining this relationship.Approach.Here, we demonstrate consistent localization of the recording site tips of subcellular-scale (6.8µm diameter) carbon fiber electrodes and the positions of surrounding neurons. We implanted male rats with carbon fiber electrode arrays for 6 or 12+ weeks targeting layer V motor cortex. After explanting the arrays, we immunostained the implant site and localized putative recording site tips with subcellular-cellular resolution. We then 3D segmented neuron somata within a 50µm radius from implanted tips to measure neuron positions and health and compare to healthy cortex with symmetric stereotaxic coordinates.Main results.Immunostaining of astrocyte, microglia, and neuron markers confirmed that overall tissue health was indicative of high biocompatibility near the tips. While neurons near implanted carbon fibers were stretched, their number and distribution were similar to hypothetical fibers placed in healthy contralateral brain. Such similar neuron distributions suggest that these minimally invasive electrodes demonstrate the potential to sample naturalistic neural populations. This motivated the prediction of spikes produced by nearby neurons using a simple point source model fit using recorded electrophysiology and the mean positions of the nearest neurons observed in histology. Comparing spike amplitudes suggests that the radius at which single units can be distinguished from others is near the fourth closest neuron (30.7 ± 4.6µm,X-± S) in layer V motor cortex.Significance.Collectively, these data and simulations provide the first direct evidence that neuron placement in the immediate vicinity of the recording site influences how many spike clusters can be reliably identified by spike sorting.
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Affiliation(s)
- Joseph G Letner
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Paras R Patel
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Jung-Chien Hsieh
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Israel M Smith Flores
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Elena della Valle
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Logan A Walker
- Biophysics Program, University of Michigan, Ann Arbor, MI 48109, United States of America
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, Ann Arbor, MI 48109, United States of America
| | - James D Weiland
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, United States of America
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, MI 48105, United States of America
| | - Cynthia A Chestek
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, United States of America
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, United States of America
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, United States of America
- Robotics Department, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Dawen Cai
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, United States of America
- Biophysics Program, University of Michigan, Ann Arbor, MI 48109, United States of America
- Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI 48109, United States of America
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12
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McAlpine D, de Hoz L. Listening loops and the adapting auditory brain. Front Neurosci 2023; 17:1081295. [PMID: 37008228 PMCID: PMC10060829 DOI: 10.3389/fnins.2023.1081295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 02/17/2023] [Indexed: 03/18/2023] Open
Abstract
Analysing complex auditory scenes depends in part on learning the long-term statistical structure of sounds comprising those scenes. One way in which the listening brain achieves this is by analysing the statistical structure of acoustic environments over multiple time courses and separating background from foreground sounds. A critical component of this statistical learning in the auditory brain is the interplay between feedforward and feedback pathways—“listening loops”—connecting the inner ear to higher cortical regions and back. These loops are likely important in setting and adjusting the different cadences over which learned listening occurs through adaptive processes that tailor neural responses to sound environments that unfold over seconds, days, development, and the life-course. Here, we posit that exploring listening loops at different scales of investigation—from in vivo recording to human assessment—their role in detecting different timescales of regularity, and the consequences this has for background detection, will reveal the fundamental processes that transform hearing into the essential task of listening.
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Affiliation(s)
- David McAlpine
- Department of Linguistics, Macquarie University, Sydney, NSW, Australia
- *Correspondence: David McAlpine,
| | - Livia de Hoz
- Neuroscience Research Center, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
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13
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Pollmann EH, Yin H, Uguz I, Dubey A, Wingel KE, Choi JS, Moazeni S, Gilhotra Y, Pavlovsky VA, Banees A, Boominathan V, Robinson J, Veeraraghavan A, Pieribone VA, Pesaran B, Shepard KL. Subdural CMOS optical probe (SCOPe) for bidirectional neural interfacing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.07.527500. [PMID: 36798295 PMCID: PMC9934536 DOI: 10.1101/2023.02.07.527500] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Optical neurotechnologies use light to interface with neurons and can monitor and manipulate neural activity with high spatial-temporal precision over large cortical extents. While there has been significant progress in miniaturizing microscope for head-mounted configurations, these existing devices are still very bulky and could never be fully implanted. Any viable translation of these technologies to human use will require a much more noninvasive, fully implantable form factor. Here, we leverage advances in microelectronics and heterogeneous optoelectronic packaging to develop a transformative, ultrathin, miniaturized device for bidirectional optical stimulation and recording: the subdural CMOS Optical Probe (SCOPe). By being thin enough to lie entirely within the subdural space of the primate brain, SCOPe defines a path for the eventual human translation of a new generation of brain-machine interfaces based on light.
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14
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Mittal D, Mease R, Kuner T, Flor H, Kuner R, Andoh J. Data management strategy for a collaborative research center. Gigascience 2022; 12:giad049. [PMID: 37401720 PMCID: PMC10318494 DOI: 10.1093/gigascience/giad049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 02/20/2023] [Accepted: 06/11/2023] [Indexed: 07/05/2023] Open
Abstract
The importance of effective research data management (RDM) strategies to support the generation of Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience data grows with each advance in data acquisition techniques and research methods. To maximize the impact of diverse research strategies, multidisciplinary, large-scale neuroscience research consortia face a number of unsolved challenges in RDM. While open science principles are largely accepted, it is practically difficult for researchers to prioritize RDM over other pressing demands. The implementation of a coherent, executable RDM plan for consortia spanning animal, human, and clinical studies is becoming increasingly challenging. Here, we present an RDM strategy implemented for the Heidelberg Collaborative Research Consortium. Our consortium combines basic and clinical research in diverse populations (animals and humans) and produces highly heterogeneous and multimodal research data (e.g., neurophysiology, neuroimaging, genetics, behavior). We present a concrete strategy for initiating early-stage RDM and FAIR data generation for large-scale collaborative research consortia, with a focus on sustainable solutions that incentivize incremental RDM while respecting research-specific requirements.
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Affiliation(s)
- Deepti Mittal
- Institute of Pharmacology, Heidelberg University, 69120 Heidelberg, Germany
| | - Rebecca Mease
- Institute of Physiology and Pathophysiology, Heidelberg University, 69120 Heidelberg, Germany
| | - Thomas Kuner
- Institute for Anatomy and Cell Biology, Heidelberg University, 69120 Mannheim, Germany
| | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
| | - Rohini Kuner
- Institute of Pharmacology, Heidelberg University, 69120 Heidelberg, Germany
| | - Jamila Andoh
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
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15
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Machado TA, Kauvar IV, Deisseroth K. Multiregion neuronal activity: the forest and the trees. Nat Rev Neurosci 2022; 23:683-704. [PMID: 36192596 PMCID: PMC10327445 DOI: 10.1038/s41583-022-00634-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/25/2022] [Indexed: 12/12/2022]
Abstract
The past decade has witnessed remarkable advances in the simultaneous measurement of neuronal activity across many brain regions, enabling fundamentally new explorations of the brain-spanning cellular dynamics that underlie sensation, cognition and action. These recently developed multiregion recording techniques have provided many experimental opportunities, but thoughtful consideration of methodological trade-offs is necessary, especially regarding field of view, temporal acquisition rate and ability to guarantee cellular resolution. When applied in concert with modern optogenetic and computational tools, multiregion recording has already made possible fundamental biological discoveries - in part via the unprecedented ability to perform unbiased neural activity screens for principles of brain function, spanning dozens of brain areas and from local to global scales.
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Affiliation(s)
- Timothy A Machado
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Isaac V Kauvar
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
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16
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Zhang YJ, Yu ZF, Liu JK, Huang TJ. Neural Decoding of Visual Information Across Different Neural Recording Modalities and Approaches. MACHINE INTELLIGENCE RESEARCH 2022. [PMCID: PMC9283560 DOI: 10.1007/s11633-022-1335-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Vision plays a peculiar role in intelligence. Visual information, forming a large part of the sensory information, is fed into the human brain to formulate various types of cognition and behaviours that make humans become intelligent agents. Recent advances have led to the development of brain-inspired algorithms and models for machine vision. One of the key components of these methods is the utilization of the computational principles underlying biological neurons. Additionally, advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual information. Thus, there is a high demand for mapping out functional models for reading out visual information from neural signals. Here, we briefly review recent progress on this issue with a focus on how machine learning techniques can help in the development of models for contending various types of neural signals, from fine-scale neural spikes and single-cell calcium imaging to coarse-scale electroencephalography (EEG) and functional magnetic resonance imaging recordings of brain signals.
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17
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Gidon A, Aru J, Larkum ME. Does brain activity cause consciousness? A thought experiment. PLoS Biol 2022; 20:e3001651. [PMID: 35687582 PMCID: PMC9187086 DOI: 10.1371/journal.pbio.3001651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Rapid advances in neuroscience have provided remarkable breakthroughs in understanding the brain on many fronts. Although promising, the role of these advancements in solving the problem of consciousness is still unclear. Based on technologies conceivably within the grasp of modern neuroscience, we discuss a thought experiment in which neural activity, in the form of action potentials, is initially recorded from all the neurons in a participant’s brain during a conscious experience and then played back into the same neurons. We consider whether this artificial replay can reconstitute a conscious experience. The possible outcomes of this experiment unravel hidden costs and pitfalls in understanding consciousness from the neurosciences’ perspective and challenge the conventional wisdom that causally links action potentials and consciousness. The authors of this Essay examine whether action potentials cause consciousness in a three-step thought experiment that assumes technology is advanced enough to fully manipulate our brains.
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Affiliation(s)
- Albert Gidon
- Institute of Biology, Humboldt University Berlin, Berlin, Germany
| | - Jaan Aru
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Matthew Evan Larkum
- Institute of Biology, Humboldt University Berlin, Berlin, Germany
- Neurocure Center for Excellence, Charité Universitätsmedizin Berlin, Berlin, Germany
- * E-mail:
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18
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Multichannel optogenetics combined with laminar recordings for ultra-controlled neuronal interrogation. Nat Commun 2022; 13:985. [PMID: 35190556 PMCID: PMC8861070 DOI: 10.1038/s41467-022-28629-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 02/02/2022] [Indexed: 11/18/2022] Open
Abstract
Simultaneous large-scale recordings and optogenetic interventions may hold the key to deciphering the fast-paced and multifaceted dialogue between neurons that sustains brain function. Here we have taken advantage of thin, cell-sized, optical fibers for minimally invasive optogenetics and flexible implantations. We describe a simple procedure for making those fibers side-emitting with a Lambertian emission distribution. Here we combined those fibers with silicon probes to achieve high-quality recordings and ultrafast multichannel optogenetic inhibition. Furthermore, we developed a multi-channel optical commutator and general-purpose patch-cord for flexible experiments. We demonstrate that our framework allows to conduct simultaneous laminar recordings and multifiber stimulations, 3D optogenetic stimulation, connectivity inference, and behavioral quantification in freely moving animals. Our framework paves the way for large-scale photo tagging and controlled interrogation of rapid neuronal communication in any combination of brain areas. Researchers from Freiburg University developed an ultraflexible fiber-based 3D light delivery system for electrophysiology and optogenetic manipulation in freely moving animals. The system allows multiside modulation of neuronal activity combined with neuronal measurements.
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19
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Urai AE, Doiron B, Leifer AM, Churchland AK. Large-scale neural recordings call for new insights to link brain and behavior. Nat Neurosci 2022; 25:11-19. [PMID: 34980926 DOI: 10.1038/s41593-021-00980-9] [Citation(s) in RCA: 84] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 11/08/2021] [Indexed: 12/17/2022]
Abstract
Neuroscientists today can measure activity from more neurons than ever before, and are facing the challenge of connecting these brain-wide neural recordings to computation and behavior. In the present review, we first describe emerging tools and technologies being used to probe large-scale brain activity and new approaches to characterize behavior in the context of such measurements. We next highlight insights obtained from large-scale neural recordings in diverse model systems, and argue that some of these pose a challenge to traditional theoretical frameworks. Finally, we elaborate on existing modeling frameworks to interpret these data, and argue that the interpretation of brain-wide neural recordings calls for new theoretical approaches that may depend on the desired level of understanding. These advances in both neural recordings and theory development will pave the way for critical advances in our understanding of the brain.
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Affiliation(s)
- Anne E Urai
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.,Cognitive Psychology Unit, Leiden University, Leiden, The Netherlands
| | | | | | - Anne K Churchland
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA. .,University of California Los Angeles, Los Angeles, CA, USA.
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20
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Luhmann HJ. Neurophysiology of the Developing Cerebral Cortex: What We Have Learned and What We Need to Know. Front Cell Neurosci 2022; 15:814012. [PMID: 35046777 PMCID: PMC8761895 DOI: 10.3389/fncel.2021.814012] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 12/09/2021] [Indexed: 11/15/2022] Open
Abstract
This review article aims to give a brief summary on the novel technologies, the challenges, our current understanding, and the open questions in the field of the neurophysiology of the developing cerebral cortex in rodents. In the past, in vitro electrophysiological and calcium imaging studies on single neurons provided important insights into the function of cellular and subcellular mechanism during early postnatal development. In the past decade, neuronal activity in large cortical networks was recorded in pre- and neonatal rodents in vivo by the use of novel high-density multi-electrode arrays and genetically encoded calcium indicators. These studies demonstrated a surprisingly rich repertoire of spontaneous cortical and subcortical activity patterns, which are currently not completely understood in their functional roles in early development and their impact on cortical maturation. Technological progress in targeted genetic manipulations, optogenetics, and chemogenetics now allow the experimental manipulation of specific neuronal cell types to elucidate the function of early (transient) cortical circuits and their role in the generation of spontaneous and sensory evoked cortical activity patterns. Large-scale interactions between different cortical areas and subcortical regions, characterization of developmental shifts from synchronized to desynchronized activity patterns, identification of transient circuits and hub neurons, role of electrical activity in the control of glial cell differentiation and function are future key tasks to gain further insights into the neurophysiology of the developing cerebral cortex.
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Affiliation(s)
- Heiko J. Luhmann
- Institute of Physiology, University Medical Center Mainz, Mainz, Germany
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21
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Contribution of animal models toward understanding resting state functional connectivity. Neuroimage 2021; 245:118630. [PMID: 34644593 DOI: 10.1016/j.neuroimage.2021.118630] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/06/2021] [Accepted: 09/29/2021] [Indexed: 12/27/2022] Open
Abstract
Functional connectivity, which reflects the spatial and temporal organization of intrinsic activity throughout the brain, is one of the most studied measures in human neuroimaging research. The noninvasive acquisition of resting state functional magnetic resonance imaging (rs-fMRI) allows the characterization of features designated as functional networks, functional connectivity gradients, and time-varying activity patterns that provide insight into the intrinsic functional organization of the brain and potential alterations related to brain dysfunction. Functional connectivity, hence, captures dimensions of the brain's activity that have enormous potential for both clinical and preclinical research. However, the mechanisms underlying functional connectivity have yet to be fully characterized, hindering interpretation of rs-fMRI studies. As in other branches of neuroscience, the identification of the neurophysiological processes that contribute to functional connectivity largely depends on research conducted on laboratory animals, which provide a platform where specific, multi-dimensional investigations that involve invasive measurements can be carried out. These highly controlled experiments facilitate the interpretation of the temporal correlations observed across the brain. Indeed, information obtained from animal experimentation to date is the basis for our current understanding of the underlying basis for functional brain connectivity. This review presents a compendium of some of the most critical advances in the field based on the efforts made by the animal neuroimaging community.
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22
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Lee KH, Ni YL, Colonell J, Karsh B, Putzeys J, Pachitariu M, Harris TD, Meister M. Electrode pooling can boost the yield of extracellular recordings with switchable silicon probes. Nat Commun 2021; 12:5245. [PMID: 34475396 PMCID: PMC8413349 DOI: 10.1038/s41467-021-25443-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 07/28/2021] [Indexed: 11/09/2022] Open
Abstract
State-of-the-art silicon probes for electrical recording from neurons have thousands of recording sites. However, due to volume limitations there are typically many fewer wires carrying signals off the probe, which restricts the number of channels that can be recorded simultaneously. To overcome this fundamental constraint, we propose a method called electrode pooling that uses a single wire to serve many recording sites through a set of controllable switches. Here we present the framework behind this method and an experimental strategy to support it. We then demonstrate its feasibility by implementing electrode pooling on the Neuropixels 1.0 electrode array and characterizing its effect on signal and noise. Finally we use simulations to explore the conditions under which electrode pooling saves wires without compromising the content of the recordings. We make recommendations on the design of future devices to take advantage of this strategy.
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Affiliation(s)
- Kyu Hyun Lee
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA, USA
| | - Yu-Li Ni
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA, USA
| | | | - Bill Karsh
- HHMI Janelia Research Campus, Ashburn, VA, USA
| | | | | | | | - Markus Meister
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA, USA.
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23
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Lu HY, Lorenc ES, Zhu H, Kilmarx J, Sulzer J, Xie C, Tobler PN, Watrous AJ, Orsborn AL, Lewis-Peacock J, Santacruz SR. Multi-scale neural decoding and analysis. J Neural Eng 2021; 18. [PMID: 34284369 PMCID: PMC8840800 DOI: 10.1088/1741-2552/ac160f] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 07/20/2021] [Indexed: 12/15/2022]
Abstract
Objective. Complex spatiotemporal neural activity encodes rich information related to behavior and cognition. Conventional research has focused on neural activity acquired using one of many different measurement modalities, each of which provides useful but incomplete assessment of the neural code. Multi-modal techniques can overcome tradeoffs in the spatial and temporal resolution of a single modality to reveal deeper and more comprehensive understanding of system-level neural mechanisms. Uncovering multi-scale dynamics is essential for a mechanistic understanding of brain function and for harnessing neuroscientific insights to develop more effective clinical treatment. Approach. We discuss conventional methodologies used for characterizing neural activity at different scales and review contemporary examples of how these approaches have been combined. Then we present our case for integrating activity across multiple scales to benefit from the combined strengths of each approach and elucidate a more holistic understanding of neural processes. Main results. We examine various combinations of neural activity at different scales and analytical techniques that can be used to integrate or illuminate information across scales, as well the technologies that enable such exciting studies. We conclude with challenges facing future multi-scale studies, and a discussion of the power and potential of these approaches. Significance. This roadmap will lead the readers toward a broad range of multi-scale neural decoding techniques and their benefits over single-modality analyses. This Review article highlights the importance of multi-scale analyses for systematically interrogating complex spatiotemporal mechanisms underlying cognition and behavior.
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Affiliation(s)
- Hung-Yun Lu
- The University of Texas at Austin, Biomedical Engineering, Austin, TX, United States of America
| | - Elizabeth S Lorenc
- The University of Texas at Austin, Psychology, Austin, TX, United States of America.,The University of Texas at Austin, Institute for Neuroscience, Austin, TX, United States of America
| | - Hanlin Zhu
- Rice University, Electrical and Computer Engineering, Houston, TX, United States of America
| | - Justin Kilmarx
- The University of Texas at Austin, Mechanical Engineering, Austin, TX, United States of America
| | - James Sulzer
- The University of Texas at Austin, Mechanical Engineering, Austin, TX, United States of America.,The University of Texas at Austin, Institute for Neuroscience, Austin, TX, United States of America
| | - Chong Xie
- Rice University, Electrical and Computer Engineering, Houston, TX, United States of America
| | - Philippe N Tobler
- University of Zurich, Neuroeconomics and Social Neuroscience, Zurich, Switzerland
| | - Andrew J Watrous
- The University of Texas at Austin, Neurology, Austin, TX, United States of America
| | - Amy L Orsborn
- University of Washington, Electrical and Computer Engineering, Seattle, WA, United States of America.,University of Washington, Bioengineering, Seattle, WA, United States of America.,Washington National Primate Research Center, Seattle, WA, United States of America
| | - Jarrod Lewis-Peacock
- The University of Texas at Austin, Psychology, Austin, TX, United States of America.,The University of Texas at Austin, Institute for Neuroscience, Austin, TX, United States of America
| | - Samantha R Santacruz
- The University of Texas at Austin, Biomedical Engineering, Austin, TX, United States of America.,The University of Texas at Austin, Institute for Neuroscience, Austin, TX, United States of America
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24
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Lee Y, Shin H, Lee D, Choi S, Cho I, Seo J. A Lubricated Nonimmunogenic Neural Probe for Acute Insertion Trauma Minimization and Long-Term Signal Recording. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:e2100231. [PMID: 34085402 PMCID: PMC8336494 DOI: 10.1002/advs.202100231] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 03/29/2021] [Indexed: 05/06/2023]
Abstract
Brain-machine interfaces (BMIs) that link the brain to a machine are promising for the treatment of neurological disorders through the bi-directional translation of neural information over extended periods. However, the longevity of such implanted devices remains limited by the deterioration of their signal sensitivity over time due to acute inflammation from insertion trauma and chronic inflammation caused by the foreign body reaction. To address this challenge, a lubricated surface is fabricated to minimize friction during insertion and avoid immunogenicity during neural signal recording. Reduced friction force leads to 86% less impulse on the brain tissue, and thus immediately increases the number of measured signal electrodes by 102% upon insertion. Furthermore, the signal measurable period increases from 8 to 16 weeks due to the prevention of gliosis. By significantly reducing insertion damage and the foreign body reaction, the lubricated immune-stealthy probe surface (LIPS) can maximize the longevity of implantable BMIs.
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Affiliation(s)
- Yeontaek Lee
- School of Electrical and Electronic EngineeringYonsei UniversitySeoul03722Republic of Korea
| | - Hyogeun Shin
- Center for BioMicrosystemsBrain Science InstituteKorea Institute of Science and Technology (KIST)Seoul02792Republic of Korea
- Division of Bio‐Medical Science & Technology, KIST SchoolKorea University of Science and Technology (UST)Seoul02792Republic of Korea
| | - Dongwon Lee
- School of Electrical and Electronic EngineeringYonsei UniversitySeoul03722Republic of Korea
| | - Sungah Choi
- School of Electrical and Electronic EngineeringYonsei UniversitySeoul03722Republic of Korea
| | - Il‐Joo Cho
- School of Electrical and Electronic EngineeringYonsei UniversitySeoul03722Republic of Korea
- Center for BioMicrosystemsBrain Science InstituteKorea Institute of Science and Technology (KIST)Seoul02792Republic of Korea
- Division of Bio‐Medical Science & Technology, KIST SchoolKorea University of Science and Technology (UST)Seoul02792Republic of Korea
- Yonsei‐KIST Convergence Research InstituteYonsei UniversitySeoul03722Republic of Korea
| | - Jungmok Seo
- School of Electrical and Electronic EngineeringYonsei UniversitySeoul03722Republic of Korea
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25
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Tukker JJ, Beed P, Brecht M, Kempter R, Moser EI, Schmitz D. Microcircuits for spatial coding in the medial entorhinal cortex. Physiol Rev 2021; 102:653-688. [PMID: 34254836 PMCID: PMC8759973 DOI: 10.1152/physrev.00042.2020] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
The hippocampal formation is critically involved in learning and memory and contains a large proportion of neurons encoding aspects of the organism’s spatial surroundings. In the medial entorhinal cortex (MEC), this includes grid cells with their distinctive hexagonal firing fields as well as a host of other functionally defined cell types including head direction cells, speed cells, border cells, and object-vector cells. Such spatial coding emerges from the processing of external inputs by local microcircuits. However, it remains unclear exactly how local microcircuits and their dynamics within the MEC contribute to spatial discharge patterns. In this review we focus on recent investigations of intrinsic MEC connectivity, which have started to describe and quantify both excitatory and inhibitory wiring in the superficial layers of the MEC. Although the picture is far from complete, it appears that these layers contain robust recurrent connectivity that could sustain the attractor dynamics posited to underlie grid pattern formation. These findings pave the way to a deeper understanding of the mechanisms underlying spatial navigation and memory.
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Affiliation(s)
- John J Tukker
- Network Dysfunction, German Center for Neurodegenerative Diseases, Berlin, Germany
| | - Prateep Beed
- NeuroScientific Research Center, Charite Berlin, Germany
| | - Michael Brecht
- Systems Neuroscience, Humboldt University of Berlin, Berlin, Germany
| | - Richard Kempter
- Department of Biology, Institute for Theoretical Biology, Humbolt-Universität zu Berlin, Berlin, Germany
| | - Edvard I Moser
- Kavli Institute for Systems Neuroscience and Centre for the Biology of Memory, Norwegian University of Science and Technology
| | - Dietmar Schmitz
- Neuroscience Research Center, Charité Universitätsmedizin Berlin, Berlin, Germany
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26
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Chen ZS, Pesaran B. Improving scalability in systems neuroscience. Neuron 2021; 109:1776-1790. [PMID: 33831347 PMCID: PMC8178195 DOI: 10.1016/j.neuron.2021.03.025] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 03/11/2021] [Accepted: 03/16/2021] [Indexed: 12/30/2022]
Abstract
Emerging technologies to acquire data at increasingly greater scales promise to transform discovery in systems neuroscience. However, current exponential growth in the scale of data acquisition is a double-edged sword. Scaling up data acquisition can speed up the cycle of discovery but can also misinterpret the results or possibly slow down the cycle because of challenges presented by the curse of high-dimensional data. Active, adaptive, closed-loop experimental paradigms use hardware and algorithms optimized to enable time-critical computation to provide feedback that interprets the observations and tests hypotheses to actively update the stimulus or stimulation parameters. In this perspective, we review important concepts of active and adaptive experiments and discuss how selectively constraining the dimensionality and optimizing strategies at different stages of discovery loop can help mitigate the curse of high-dimensional data. Active and adaptive closed-loop experimental paradigms can speed up discovery despite an exponentially increasing data scale, offering a road map to timely and iterative hypothesis revision and discovery in an era of exponential growth in neuroscience.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY 10016, USA; Neuroscience Institute, NYU School of Medicine, New York, NY 10016, USA.
| | - Bijan Pesaran
- Neuroscience Institute, NYU School of Medicine, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA; Department of Neurology, New York University School of Medicine, New York, NY 10016, USA.
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27
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Steinmetz NA, Aydin C, Lebedeva A, Okun M, Pachitariu M, Bauza M, Beau M, Bhagat J, Böhm C, Broux M, Chen S, Colonell J, Gardner RJ, Karsh B, Kloosterman F, Kostadinov D, Mora-Lopez C, O'Callaghan J, Park J, Putzeys J, Sauerbrei B, van Daal RJJ, Vollan AZ, Wang S, Welkenhuysen M, Ye Z, Dudman JT, Dutta B, Hantman AW, Harris KD, Lee AK, Moser EI, O'Keefe J, Renart A, Svoboda K, Häusser M, Haesler S, Carandini M, Harris TD. Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings. Science 2021. [PMID: 33859006 DOI: 10.1101/2020.10.27.358291] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Measuring the dynamics of neural processing across time scales requires following the spiking of thousands of individual neurons over milliseconds and months. To address this need, we introduce the Neuropixels 2.0 probe together with newly designed analysis algorithms. The probe has more than 5000 sites and is miniaturized to facilitate chronic implants in small mammals and recording during unrestrained behavior. High-quality recordings over long time scales were reliably obtained in mice and rats in six laboratories. Improved site density and arrangement combined with newly created data processing methods enable automatic post hoc correction for brain movements, allowing recording from the same neurons for more than 2 months. These probes and algorithms enable stable recordings from thousands of sites during free behavior, even in small animals such as mice.
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Affiliation(s)
- Nicholas A Steinmetz
- UCL Institute of Ophthalmology, University College London, London, UK.
- Department of Biological Structure, University of Washington, Seattle, WA, USA
| | | | - Anna Lebedeva
- Sainsbury Wellcome Centre, University College London, London, UK
| | - Michael Okun
- Centre for Systems Neuroscience and Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Marius Pachitariu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Marius Bauza
- Sainsbury Wellcome Centre, University College London, London, UK
| | - Maxime Beau
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Jai Bhagat
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Claudia Böhm
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Susu Chen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Jennifer Colonell
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Richard J Gardner
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
| | - Bill Karsh
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Fabian Kloosterman
- Neuroelectronics Research Flanders, Leuven, Belgium
- IMEC, Leuven, Belgium
- Vlaams Instituut voor Biotechnologie (VIB), Leuven, Belgium
- Brain and Cognition, KU Leuven, Leuven, Belgium
| | - Dimitar Kostadinov
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | | | | | - Junchol Park
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Britton Sauerbrei
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Rik J J van Daal
- ATLAS Neuroengineering, Leuven, Belgium
- Neuroelectronics Research Flanders, Leuven, Belgium
- Micro- and Nanosystems, KU Leuven, Leuven, Belgium
| | - Abraham Z Vollan
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
| | | | | | - Zhiwen Ye
- Department of Biological Structure, University of Washington, Seattle, WA, USA
| | - Joshua T Dudman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Adam W Hantman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Kenneth D Harris
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Albert K Lee
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Edvard I Moser
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
| | - John O'Keefe
- Sainsbury Wellcome Centre, University College London, London, UK
| | | | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Michael Häusser
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Sebastian Haesler
- Neuroelectronics Research Flanders, Leuven, Belgium
- Vlaams Instituut voor Biotechnologie (VIB), Leuven, Belgium
| | - Matteo Carandini
- UCL Institute of Ophthalmology, University College London, London, UK.
| | - Timothy D Harris
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
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28
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Steinmetz NA, Aydin C, Lebedeva A, Okun M, Pachitariu M, Bauza M, Beau M, Bhagat J, Böhm C, Broux M, Chen S, Colonell J, Gardner RJ, Karsh B, Kloosterman F, Kostadinov D, Mora-Lopez C, O'Callaghan J, Park J, Putzeys J, Sauerbrei B, van Daal RJJ, Vollan AZ, Wang S, Welkenhuysen M, Ye Z, Dudman JT, Dutta B, Hantman AW, Harris KD, Lee AK, Moser EI, O'Keefe J, Renart A, Svoboda K, Häusser M, Haesler S, Carandini M, Harris TD. Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings. Science 2021; 372:eabf4588. [PMID: 33859006 PMCID: PMC8244810 DOI: 10.1126/science.abf4588] [Citation(s) in RCA: 356] [Impact Index Per Article: 118.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 03/01/2021] [Indexed: 12/22/2022]
Abstract
Measuring the dynamics of neural processing across time scales requires following the spiking of thousands of individual neurons over milliseconds and months. To address this need, we introduce the Neuropixels 2.0 probe together with newly designed analysis algorithms. The probe has more than 5000 sites and is miniaturized to facilitate chronic implants in small mammals and recording during unrestrained behavior. High-quality recordings over long time scales were reliably obtained in mice and rats in six laboratories. Improved site density and arrangement combined with newly created data processing methods enable automatic post hoc correction for brain movements, allowing recording from the same neurons for more than 2 months. These probes and algorithms enable stable recordings from thousands of sites during free behavior, even in small animals such as mice.
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Affiliation(s)
- Nicholas A Steinmetz
- UCL Institute of Ophthalmology, University College London, London, UK.
- Department of Biological Structure, University of Washington, Seattle, WA, USA
| | | | - Anna Lebedeva
- Sainsbury Wellcome Centre, University College London, London, UK
| | - Michael Okun
- Centre for Systems Neuroscience and Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Marius Pachitariu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Marius Bauza
- Sainsbury Wellcome Centre, University College London, London, UK
| | - Maxime Beau
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Jai Bhagat
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Claudia Böhm
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Susu Chen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Jennifer Colonell
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Richard J Gardner
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
| | - Bill Karsh
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Fabian Kloosterman
- Neuroelectronics Research Flanders, Leuven, Belgium
- IMEC, Leuven, Belgium
- Vlaams Instituut voor Biotechnologie (VIB), Leuven, Belgium
- Brain and Cognition, KU Leuven, Leuven, Belgium
| | - Dimitar Kostadinov
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | | | | | - Junchol Park
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Britton Sauerbrei
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Rik J J van Daal
- ATLAS Neuroengineering, Leuven, Belgium
- Neuroelectronics Research Flanders, Leuven, Belgium
- Micro- and Nanosystems, KU Leuven, Leuven, Belgium
| | - Abraham Z Vollan
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
| | | | | | - Zhiwen Ye
- Department of Biological Structure, University of Washington, Seattle, WA, USA
| | - Joshua T Dudman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Adam W Hantman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Kenneth D Harris
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Albert K Lee
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Edvard I Moser
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
| | - John O'Keefe
- Sainsbury Wellcome Centre, University College London, London, UK
| | | | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Michael Häusser
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Sebastian Haesler
- Neuroelectronics Research Flanders, Leuven, Belgium
- Vlaams Instituut voor Biotechnologie (VIB), Leuven, Belgium
| | - Matteo Carandini
- UCL Institute of Ophthalmology, University College London, London, UK.
| | - Timothy D Harris
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
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29
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Cai L, Gutruf P. Soft, Wireless and subdermally implantable recording and neuromodulation tools. J Neural Eng 2021; 18. [PMID: 33607646 DOI: 10.1088/1741-2552/abe805] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Accepted: 02/19/2021] [Indexed: 12/14/2022]
Abstract
Progress in understanding neuronal interaction and circuit behavior of the central and peripheral nervous system strongly relies on the advancement of tools that record and stimulate with high fidelity and specificity. Currently, devices used in exploratory research predominantly utilize cables or tethers to provide pathways for power supply, data communication, stimulus delivery and recording, which constrains the scope and use of such devices. In particular, the tethered connection, mechanical mismatch to surrounding soft tissues and bones frustrate the interface leading to irritation and limitation of motion of the subject, which in the case of fundamental and preclinical studies, impacts naturalistic behaviors of animals and precludes the use in experiments involving social interaction and ethologically relevant three-dimensional environments, limiting the use of current tools to mostly rodents and exclude species such as birds and fish. This review explores the current state-of-the-art in wireless, subdermally implantable tools that quantitively expand capabilities in analysis and perturbation of the central and peripheral nervous system by removing tethers and externalized features of implantable neuromodulation and recording tools. Specifically, the review explores power harvesting strategies, wireless communication schemes, and soft materials and mechanics that enable the creation of such devices and discuss their capabilities in the context of freely-behaving subjects. Highlights of this class of devices includes wireless battery-free and fully implantable operation with capabilities in cell specific recording, multimodal neural stimulation and electrical, optogenetic and pharmacological neuromodulation capabilities. We conclude with discussion on translation of such technologies which promises routes towards broad dissemination.
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Affiliation(s)
- Le Cai
- Biomedical Engineering, University of Arizona, 1230 N Cherry Ave., Tucson, Arizona, 85719, UNITED STATES
| | - Philipp Gutruf
- Biomedical Engineering, University of Arizona, 1230 N Cherry Ave., Tucson, Arizona, 85719, UNITED STATES
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30
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Luan L, Robinson JT, Aazhang B, Chi T, Yang K, Li X, Rathore H, Singer A, Yellapantula S, Fan Y, Yu Z, Xie C. Recent Advances in Electrical Neural Interface Engineering: Minimal Invasiveness, Longevity, and Scalability. Neuron 2020; 108:302-321. [PMID: 33120025 PMCID: PMC7646678 DOI: 10.1016/j.neuron.2020.10.011] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 10/03/2020] [Accepted: 10/08/2020] [Indexed: 12/16/2022]
Abstract
Electrical neural interfaces serve as direct communication pathways that connect the nervous system with the external world. Technological advances in this domain are providing increasingly more powerful tools to study, restore, and augment neural functions. Yet, the complexities of the nervous system give rise to substantial challenges in the design, fabrication, and system-level integration of these functional devices. In this review, we present snapshots of the latest progresses in electrical neural interfaces, with an emphasis on advances that expand the spatiotemporal resolution and extent of mapping and manipulating brain circuits. We include discussions of large-scale, long-lasting neural recording; wireless, miniaturized implants; signal transmission, amplification, and processing; as well as the integration of interfaces with optical modalities. We outline the background and rationale of these developments and share insights into the future directions and new opportunities they enable.
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Affiliation(s)
- Lan Luan
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA; Department of Bioengineering, Rice University, Houston, TX, USA; NeuroEngineering Initiative, Rice University, Houston, TX, USA
| | - Jacob T Robinson
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA; Department of Bioengineering, Rice University, Houston, TX, USA; NeuroEngineering Initiative, Rice University, Houston, TX, USA
| | - Behnaam Aazhang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA; NeuroEngineering Initiative, Rice University, Houston, TX, USA
| | - Taiyun Chi
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Kaiyuan Yang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Xue Li
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA; NeuroEngineering Initiative, Rice University, Houston, TX, USA
| | - Haad Rathore
- NeuroEngineering Initiative, Rice University, Houston, TX, USA; Applied Physics Graduate Program, Rice University, Houston, TX, USA
| | - Amanda Singer
- NeuroEngineering Initiative, Rice University, Houston, TX, USA; Applied Physics Graduate Program, Rice University, Houston, TX, USA
| | - Sudha Yellapantula
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA; NeuroEngineering Initiative, Rice University, Houston, TX, USA
| | - Yingying Fan
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Zhanghao Yu
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Chong Xie
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA; Department of Bioengineering, Rice University, Houston, TX, USA; NeuroEngineering Initiative, Rice University, Houston, TX, USA.
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31
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Moreaux LC, Yatsenko D, Sacher WD, Choi J, Lee C, Kubat NJ, Cotton RJ, Boyden ES, Lin MZ, Tian L, Tolias AS, Poon JKS, Shepard KL, Roukes ML. Integrated Neurophotonics: Toward Dense Volumetric Interrogation of Brain Circuit Activity-at Depth and in Real Time. Neuron 2020; 108:66-92. [PMID: 33058767 PMCID: PMC8061790 DOI: 10.1016/j.neuron.2020.09.043] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 08/18/2020] [Accepted: 09/28/2020] [Indexed: 12/17/2022]
Abstract
We propose a new paradigm for dense functional imaging of brain activity to surmount the limitations of present methodologies. We term this approach "integrated neurophotonics"; it combines recent advances in microchip-based integrated photonic and electronic circuitry with those from optogenetics. This approach has the potential to enable lens-less functional imaging from within the brain itself to achieve dense, large-scale stimulation and recording of brain activity with cellular resolution at arbitrary depths. We perform a computational study of several prototype 3D architectures for implantable probe-array modules that are designed to provide fast and dense single-cell resolution (e.g., within a 1-mm3 volume of mouse cortex comprising ∼100,000 neurons). We describe progress toward realizing integrated neurophotonic imaging modules, which can be produced en masse with current semiconductor foundry protocols for chip manufacturing. Implantation of multiple modules can cover extended brain regions.
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Affiliation(s)
- Laurent C Moreaux
- Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA.
| | - Dimitri Yatsenko
- Vathes LLC, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Wesley D Sacher
- Kavli Nanoscience Institute, California Institute of Technology, Pasadena, CA 91125, USA; Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA; Max Planck Institute for Microstructure Physics, Halle, Germany
| | - Jaebin Choi
- Departments of Electrical Engineering and Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Changhyuk Lee
- Departments of Electrical Engineering and Biomedical Engineering, Columbia University, New York, NY 10027, USA; Center for BioMicrosystems, Brain Science Institute, Korea Institute of Science and Technology, Korea
| | - Nicole J Kubat
- Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA
| | - R James Cotton
- Shirley Ryan AbilityLab, Northwestern University, Chicago, IL 60611, USA; Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Edward S Boyden
- Howard Hughes Medical Institute, Cambridge, MA, USA; McGovern Institute, MIT, Cambridge, USA; Koch Institute, MIT, Cambridge, USA; Departments of Brain and Cognitive Sciences, Media Arts and Sciences, and Biological Engineering, MIT, Cambridge, USA
| | - Michael Z Lin
- Departments of Neurobiology and Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Lin Tian
- Department of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis, CA 95616, USA
| | - Andreas S Tolias
- Vathes LLC, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Joyce K S Poon
- Max Planck Institute for Microstructure Physics, Halle, Germany; Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Rd., Toronto, ON M5S 3G4, Canada
| | - Kenneth L Shepard
- Departments of Electrical Engineering and Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Michael L Roukes
- Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA; Kavli Nanoscience Institute, California Institute of Technology, Pasadena, CA 91125, USA; Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
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32
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Guo L. Principles of functional neural mapping using an intracortical ultra-density microelectrode array (ultra-density MEA). J Neural Eng 2020; 17:036018. [PMID: 32365334 DOI: 10.1088/1741-2552/ab8fc5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
OBJECTIVE Intracortical electrical neural recording using solid-state electrodes is a prevalent approach in addressing neurophysiological queries and implementing brain-computer interfacing systems. As a variety of ultra-density microelectrode arrays (ultra-density MEAs) are being created more recently, this paper answers to the rising demand for a more rigorous theory concerning this new type of neural electrode technology, both to guide the proper design and to inform the proper usage. APPROACH This design and use problem of ultra-density MEAs for functional intracortical neuronal circuit mapping is approached from a signal analysis perspective. Starting with quantitative derivations of key basic concepts, the concept of ultra-density MEA is defined in the context for fully resolving the voltage sources within its view volume. Then, the principle of using such an ultra-density MEA for functional neural mapping is elaborated, and a recursive approach to completely resolve all voltage sources from the ultra-density MEA's recordings is proposed. This approach is further validated using a simulated experiment. Last, the limitations and implications of this work are discussed. MAIN RESULTS MEAs can only be used to map the extracellular somatic action potential (esAP) sources in a neural microcircuit, and AP propagation along individual axons cannot be detected. The key for the ultra-density MEA design is to make sure that each spatial unit of analysis (SUA) contains no more than one active esAP source. The unique neural resolving capability of ultra-density MEAs comparing to conventional MEAs is to be able to spatiotemporally resolve each esAP source within its view volume. SIGNIFICANCE The ultimate capability and limitation of neural electrode array technology at such an unprecedented fabrication resolution is unraveled. This work strives to further the discussions on this topic into a more quantitative and rational direction, while providing a theoretical guideline for the rational development and neuroscientific application of an ultra-density MEA for intracortical functional mapping.
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Affiliation(s)
- Liang Guo
- Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, United States of America
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33
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Lycke R, Sun L, Luan L, Xie C. Spikes to Pixels: Camera Chips for Large-scale Electrophysiology. Trends Neurosci 2020; 43:269-271. [PMID: 32353330 DOI: 10.1016/j.tins.2020.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 03/02/2020] [Indexed: 11/28/2022]
Abstract
Implanted neural probes are among the most important techniques in both fundamental and clinical neuroscience. Despite great successes and promise, neural electrodes are technically limited by their scalability. A recent study by Obaid et al. demonstrated an innovative way to greatly scale up the channel count and density of neural electrode arrays.
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Affiliation(s)
- Roy Lycke
- Department of Biomedical Engineering, University of Texas, Austin, TX, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Liuyang Sun
- Department of Biomedical Engineering, University of Texas, Austin, TX, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Lan Luan
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA; Department of Bioengineering, Rice University, Houston, TX, USA; Neuorengineering Initiative, Rice University, Houston, TX, USA
| | - Chong Xie
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA; Department of Bioengineering, Rice University, Houston, TX, USA; Neuorengineering Initiative, Rice University, Houston, TX, USA.
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Wu Y, Chen H, Guo L. Opportunities and dilemmas of in vitro nano neural electrodes. RSC Adv 2020; 10:187-200. [PMID: 35492533 PMCID: PMC9047985 DOI: 10.1039/c9ra08917a] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 12/04/2019] [Indexed: 01/05/2023] Open
Abstract
Developing electrophysiological platforms to capture electrical activities of neurons and exert modulatory stimuli lays the foundation for many neuroscience-related disciplines, including the neuron–machine interface, neuroprosthesis, and mapping of brain circuitry.
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Affiliation(s)
- Yu Wu
- Department of Electrical and Computer Engineering
- The Ohio State University
- Columbus
- USA
| | - Haowen Chen
- Department of Electrical and Computer Engineering
- The Ohio State University
- Columbus
- USA
| | - Liang Guo
- Department of Electrical and Computer Engineering
- The Ohio State University
- Columbus
- USA
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