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Yi D, Yao Y, Wang Y, Chen L. Design, Fabrication, and Implantation of Invasive Microelectrode Arrays as in vivo Brain Machine Interfaces: A Comprehensive Review. JOURNAL OF MANUFACTURING PROCESSES 2024; 126:185-207. [PMID: 39185373 PMCID: PMC11340637 DOI: 10.1016/j.jmapro.2024.07.100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Invasive Microelectrode Arrays (MEAs) have been a significant and useful tool for us to gain a fundamental understanding of how the brain works through high spatiotemporal resolution neuron-level recordings and/or stimulations. Through decades of research, various types of microwire, silicon, and flexible substrate-based MEAs have been developed using the evolving new materials, novel design concepts, and cutting-edge advanced manufacturing capabilities. Surgical implantation of the latest minimal damaging flexible MEAs through the hard-to-penetrate brain membranes introduces new challenges and thus the development of implantation strategies and instruments for the latest MEAs. In this paper, studies on the design considerations and enabling manufacturing processes of various invasive MEAs as in vivo brain-machine interfaces have been reviewed to facilitate the development as well as the state-of-art of such brain-machine interfaces from an engineering perspective. The challenges and solution strategies developed for surgically implanting such interfaces into the brain have also been evaluated and summarized. Finally, the research gaps have been identified in the design, manufacturing, and implantation perspectives, and future research prospects in invasive MEA development have been proposed.
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Affiliation(s)
- Dongyang Yi
- Department of Mechanical and Industrial Engineering, University of Massachusetts Lowell, Lowell, MA 01854
| | - Yao Yao
- Department of Industrial and Systems Engineering, University of Missouri, Columbia, MO 65211
| | - Yi Wang
- Department of Industrial and Systems Engineering, University of Missouri, Columbia, MO 65211
| | - Lei Chen
- Department of Mechanical and Industrial Engineering, University of Massachusetts Lowell, Lowell, MA 01854
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2
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Bennett C, Ouellette B, Ramirez TK, Cahoon A, Cabasco H, Browning Y, Lakunina A, Lynch GF, McBride EG, Belski H, Gillis R, Grasso C, Howard R, Johnson T, Loeffler H, Smith H, Sullivan D, Williford A, Caldejon S, Durand S, Gale S, Guthrie A, Ha V, Han W, Hardcastle B, Mochizuki C, Sridhar A, Suarez L, Swapp J, Wilkes J, Siegle JH, Farrell C, Groblewski PA, Olsen SR. SHIELD: Skull-shaped hemispheric implants enabling large-scale electrophysiology datasets in the mouse brain. Neuron 2024:S0896-6273(24)00450-1. [PMID: 38996587 DOI: 10.1016/j.neuron.2024.06.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/02/2024] [Accepted: 06/18/2024] [Indexed: 07/14/2024]
Abstract
To understand the neural basis of behavior, it is essential to measure spiking dynamics across many interacting brain regions. Although new technologies, such as Neuropixels probes, facilitate multi-regional recordings, significant surgical and procedural hurdles remain for these experiments to achieve their full potential. Here, we describe skull-shaped hemispheric implants enabling large-scale electrophysiology datasets (SHIELD). These 3D-printed skull-replacement implants feature customizable insertion holes, allowing dozens of cortical and subcortical structures to be recorded in a single mouse using repeated multi-probe insertions over many days. We demonstrate the procedure's high success rate, biocompatibility, lack of adverse effects on behavior, and compatibility with imaging and optogenetics. To showcase SHIELD's scientific utility, we use multi-probe recordings to reveal novel insights into how alpha rhythms organize spiking activity across visual and sensorimotor networks. Overall, this method enables powerful, large-scale electrophysiological experiments for the study of distributed neural computation.
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Affiliation(s)
- Corbett Bennett
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA.
| | - Ben Ouellette
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | | | | | - Hannah Cabasco
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Yoni Browning
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Anna Lakunina
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Galen F Lynch
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | | | - Hannah Belski
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Ryan Gillis
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Conor Grasso
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Robert Howard
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Tye Johnson
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Henry Loeffler
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Heston Smith
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | | | | | | | | | - Samuel Gale
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Alan Guthrie
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Vivian Ha
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Warren Han
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Ben Hardcastle
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | | | - Arjun Sridhar
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Lucas Suarez
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Jackie Swapp
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Joshua Wilkes
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | | | | | | | - Shawn R Olsen
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA.
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3
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Melin MD, Khanal A, Vasquez M, Ryan MB, Churchland AK, Couto J. Large scale, simultaneous, chronic neural recordings from multiple brain areas. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.22.572441. [PMID: 38187681 PMCID: PMC10769364 DOI: 10.1101/2023.12.22.572441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Understanding how brain activity is related to animal behavior requires measuring multi-area interactions on multiple timescales. However, methods to perform chronic, simultaneous recordings of neural activity from many brain areas are lacking. Here, we introduce a novel approach for independent chronic probe implantation that enables flexible, simultaneous interrogation of neural activity from many brain regions during head restrained or freely moving behavior. The approach, that we called indie (independent dovetail implants for electrophysiology), enables repeated retrieval and reimplantation of probes. The chronic implantation approach can be combined with other modalities such as skull clearing for cortex wide access and optogenetics with optic fibers. Using this approach, we implanted 6 probes chronically in one hemisphere of the mouse brain. The implant is lightweight, allows flexible targeting with different angles, and offers enhanced stability. Our approach broadens the applications of chronic recording while retaining its main advantages over acute recordings (superior stability, longitudinal monitoring of activity and freely moving interrogations) and provides an appealing venue to study processes not accessible by acute methods, such as the neural substrate of learning across multiple areas.
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Affiliation(s)
- Maxwell D Melin
- Department of Neurobiology, University of California Los Angeles
- UCLA-Caltech Medical Scientist Training Program
| | - Anup Khanal
- Department of Neurobiology, University of California Los Angeles
| | - Marvin Vasquez
- Department of Neurobiology, University of California Los Angeles
| | - Michael B Ryan
- Department of Neurobiology, University of California Los Angeles
| | | | - Joao Couto
- Department of Neurobiology, University of California Los Angeles
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4
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Stroh A, Schweiger S, Ramirez JM, Tüscher O. The selfish network: how the brain preserves behavioral function through shifts in neuronal network state. Trends Neurosci 2024; 47:246-258. [PMID: 38485625 DOI: 10.1016/j.tins.2024.02.005] [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: 08/19/2023] [Revised: 01/31/2024] [Accepted: 02/19/2024] [Indexed: 04/12/2024]
Abstract
Neuronal networks possess the ability to regulate their activity states in response to disruptions. How and when neuronal networks turn from physiological into pathological states, leading to the manifestation of neuropsychiatric disorders, remains largely unknown. Here, we propose that neuronal networks intrinsically maintain network stability even at the cost of neuronal loss. Despite the new stable state being potentially maladaptive, neural networks may not reverse back to states associated with better long-term outcomes. These maladaptive states are often associated with hyperactive neurons, marking the starting point for activity-dependent neurodegeneration. Transitions between network states may occur rapidly, and in discrete steps rather than continuously, particularly in neurodegenerative disorders. The self-stabilizing, metastable, and noncontinuous characteristics of these network states can be mathematically described as attractors. Maladaptive attractors may represent a distinct pathophysiological entity that could serve as a target for new therapies and for fostering resilience.
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Affiliation(s)
- Albrecht Stroh
- Leibniz Institute for Resilience Research, Mainz, Germany; Institute of Pathophysiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
| | - Susann Schweiger
- Leibniz Institute for Resilience Research, Mainz, Germany; Institute of Human Genetics, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany; Institute of Molecular Biology (IMB), Mainz, Germany
| | - Jan-Marino Ramirez
- Center for Integrative Brain Research at the Seattle Children's Research Institute, University of Washington, Seattle, USA
| | - Oliver Tüscher
- Leibniz Institute for Resilience Research, Mainz, Germany; Institute of Molecular Biology (IMB), Mainz, Germany; Department of Psychiatry and Psychotherapy, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
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Garcia S, Windolf C, Boussard J, Dichter B, Buccino AP, Yger P. A Modular Implementation to Handle and Benchmark Drift Correction for High-Density Extracellular Recordings. eNeuro 2024; 11:ENEURO.0229-23.2023. [PMID: 38238082 PMCID: PMC10897502 DOI: 10.1523/eneuro.0229-23.2023] [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] [Received: 06/29/2023] [Revised: 12/20/2023] [Accepted: 12/21/2023] [Indexed: 02/28/2024] Open
Abstract
High-density neural devices are now offering the possibility to record from neuronal populations in vivo at unprecedented scale. However, the mechanical drifts often observed in these recordings are currently a major issue for "spike sorting," an essential analysis step to identify the activity of single neurons from extracellular signals. Although several strategies have been proposed to compensate for such drifts, the lack of proper benchmarks makes it hard to assess the quality and effectiveness of motion correction. In this paper, we present a benchmark study to precisely and quantitatively evaluate the performance of several state-of-the-art motion correction algorithms introduced in the literature. Using simulated recordings with induced drifts, we dissect the origins of the errors performed while applying a motion correction algorithm as a preprocessing step in the spike sorting pipeline. We show how important it is to properly estimate the positions of the neurons from extracellular traces in order to correctly estimate the probe motion, compare several interpolation procedures, and highlight what are the current limits for motion correction approaches.
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Affiliation(s)
- Samuel Garcia
- Centre de Recherche en Neuroscience de Lyon, CNRS, Lyon 69675, France
| | | | | | | | - Alessio P Buccino
- CatalystNeuro, Benicia, California 94510
- Allen Institute for Neural Dynamics, Seattle, Washington 98109
| | - Pierre Yger
- Institut de la Vision, Sorbonne Université, INSERM, Paris 75012, France
- Lille Neurosciences & Cognition (lilNCog)-U1172 (INSERM, Lille), Univ Lille, Centre Hospitalier Universitaire de Lille, Lille 59800, France
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6
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Mandino F, Vujic S, Grandjean J, Lake EMR. Where do we stand on fMRI in awake mice? Cereb Cortex 2024; 34:bhad478. [PMID: 38100331 PMCID: PMC10793583 DOI: 10.1093/cercor/bhad478] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 11/17/2023] [Accepted: 11/18/2023] [Indexed: 12/17/2023] Open
Abstract
Imaging awake animals is quickly gaining traction in neuroscience as it offers a means to eliminate the confounding effects of anesthesia, difficulties of inter-species translation (when humans are typically imaged while awake), and the inability to investigate the full range of brain and behavioral states in unconscious animals. In this systematic review, we focus on the development of awake mouse blood oxygen level dependent functional magnetic resonance imaging (fMRI). Mice are widely used in research due to their fast-breeding cycle, genetic malleability, and low cost. Functional MRI yields whole-brain coverage and can be performed on both humans and animal models making it an ideal modality for comparing study findings across species. We provide an analysis of 30 articles (years 2011-2022) identified through a systematic literature search. Our conclusions include that head-posts are favorable, acclimation training for 10-14 d is likely ample under certain conditions, stress has been poorly characterized, and more standardization is needed to accelerate progress. For context, an overview of awake rat fMRI studies is also included. We make recommendations that will benefit a wide range of neuroscience applications.
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Affiliation(s)
- Francesca Mandino
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
| | - Stella Vujic
- Department of Computer Science, Yale University, New Haven, CT 06520, United States
| | - Joanes Grandjean
- Donders Institute for Brain, Behaviour, and Cognition, Radboud University, Nijmegen, The Netherlands
- Department for Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Evelyn M R Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, United States
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7
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Timme NM, Ardinger CE, Weir SDC, Zelaya-Escobar R, Kruger R, Lapish CC. Non-consummatory behavior signals predict aversion-resistant alcohol drinking in head-fixed mice. Neuropharmacology 2024; 242:109762. [PMID: 37871677 PMCID: PMC10872650 DOI: 10.1016/j.neuropharm.2023.109762] [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: 07/17/2023] [Revised: 10/05/2023] [Accepted: 10/12/2023] [Indexed: 10/25/2023]
Abstract
A key facet of alcohol use disorder is continuing to drink alcohol despite negative consequences (so called "aversion-resistant drinking"). In this study, we sought to assess the degree to which head-fixed mice exhibit aversion-resistant drinking and to leverage behavioral analysis techniques available in head-fixture to relate non-consummatory behaviors to aversion-resistant drinking. We assessed aversion-resistant drinking in head-fixed female and male C57BL/6 J mice. We adulterated 20% (v/v) alcohol with varying concentrations of the bitter tastant quinine to measure the degree to which mice would continue to drink despite this aversive stimulus. We recorded high-resolution video of the mice during head-fixed drinking, tracked body parts with machine vision tools, and analyzed body movements in relation to consumption. Female and male head-fixed mice exhibited heterogenous levels of aversion-resistant drinking. Additionally, non-consummatory behaviors, such as paw movement and snout movement, were related to the intensity of aversion-resistant drinking. These studies demonstrate that head-fixed mice exhibit aversion-resistant drinking and that non-consummatory behaviors can be used to assess perceived aversiveness in this paradigm. Furthermore, these studies lay the groundwork for future experiments that will utilize advanced electrophysiological techniques to record from large populations of neurons during aversion-resistant drinking to understand the neurocomputational processes that drive this clinically relevant behavior. This article is part of the Special Issue on "PFC circuit function in psychiatric disease and relevant models".
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Affiliation(s)
- Nicholas M Timme
- Department of Psychology, Indiana University - Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA.
| | - Cherish E Ardinger
- Department of Psychology, Indiana University - Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA
| | - Seth D C Weir
- Department of Psychology, Indiana University - Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA
| | - Rachel Zelaya-Escobar
- Department of Psychology, Indiana University - Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA
| | - Rachel Kruger
- Department of Psychology, Indiana University - Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA
| | - Christopher C Lapish
- Department of Anatomy, Cell Biology, and Physiology, Indiana University School of Medicine, 635 Barnhill Drive, MSB 5035, Indianapolis, IN, 46202, USA; Stark Neuroscience Institute, Indiana University School of Medicine, 320 W. 15th St, NB 414, Indianapolis, IN, 46202, USA
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8
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Lee CH, Park YK, Lee K. Recent strategies for neural dynamics observation at a larger scale and wider scope. Biosens Bioelectron 2023; 240:115638. [PMID: 37647685 DOI: 10.1016/j.bios.2023.115638] [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] [Received: 04/14/2023] [Revised: 08/15/2023] [Accepted: 08/24/2023] [Indexed: 09/01/2023]
Abstract
The tremendous technical progress in neuroscience offers opportunities to observe a more minor or/and broader dynamic picture of the brain. Moreover, the large-scale neural activity of individual neurons enables the dissection of detailed mechanistic links between neural populations and behaviors. To measure neural activity in-vivo, multi-neuron recording, and neuroimaging techniques are employed and developed to acquire more neurons. The tools introduced concurrently recorded dozens to hundreds of neurons in the coordinated brain regions and elucidated the neuronal ensembles from a massive population perspective of diverse neurons at cellular resolution. In particular, the increasing spatiotemporal resolution of neuronal monitoring across the whole brain dramatically facilitates our understanding of additional nervous system functions in health and disease. Here, we will introduce state-of-the-art neuroscience tools involving large-scale neural population recording and the long-range connections spanning multiple brain regions. Their synergic effects provide to clarify the controversial circuitry underlying neuroscience. These challenging neural tools present a promising outlook for the fundamental dynamic interplay across levels of synaptic cellular, circuit organization, and brain-wide. Hence, more observations of neural dynamics will provide more clues to elucidate brain functions and push forward innovative technology at the intersection of neural engineering disciplines. We hope this review will provide insight into the use or development of recent neural techniques considering spatiotemporal scales of brain observation.
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Affiliation(s)
- Chang Hak Lee
- Department of Brain Sciences, Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu, South Korea
| | - Young Kwon Park
- Department of Brain Sciences, Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu, South Korea
| | - Kwang Lee
- Department of Brain Sciences, Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu, South Korea.
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9
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Timme NM, Ardinger CE, Weir SDC, Zelaya-Escobar R, Kruger R, Lapish CC. Non-Consummatory Behavior Signals Predict Aversion-Resistant Alcohol Drinking in Head-Fixed Mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.20.545767. [PMID: 37873153 PMCID: PMC10592797 DOI: 10.1101/2023.06.20.545767] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
A key facet of alcohol use disorder is continuing to drink alcohol despite negative consequences (so called "aversion-resistant drinking"). In this study, we sought to assess the degree to which head-fixed mice exhibit aversion-resistant drinking and to leverage behavioral analysis techniques available in head-fixture to relate non-consummatory behaviors to aversion-resistant drinking. We assessed aversion-resistant drinking in head-fixed female and male C57BL/6J mice. We adulterated 20% (v/v) alcohol with varying concentrations of the bitter tastant quinine to measure the degree to which mice would continue to drink despite this aversive stimulus. We recorded high-resolution video of the mice during head-fixed drinking, tracked body parts with machine vision tools, and analyzed body movements in relation to consumption. Female and male head-fixed mice exhibited heterogenous levels of aversion-resistant drinking. Additionally, non-consummatory behaviors, such as paw movement and snout movement, were related to the intensity of aversion-resistant drinking. These studies demonstrate that head-fixed mice exhibit aversion-resistant drinking and that non-consummatory behaviors can be used to assess perceived aversiveness in this paradigm. Furthermore, these studies lay the groundwork for future experiments that will utilize advanced electrophysiological techniques to record from large populations of neurons during aversion-resistant drinking to understand the neurocomputational processes that drive this clinically relevant behavior.
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Affiliation(s)
- Nicholas M. Timme
- Department of Psychology, Indiana University – Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA
| | - Cherish E. Ardinger
- Department of Psychology, Indiana University – Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA
| | - Seth D. C. Weir
- Department of Psychology, Indiana University – Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA
| | - Rachel Zelaya-Escobar
- Department of Psychology, Indiana University – Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA
| | - Rachel Kruger
- Department of Psychology, Indiana University – Purdue University Indianapolis, 402 N. Blackford St, LD 124, Indianapolis, IN, 46202, USA
| | - Christopher C. Lapish
- Department of Anatomy, Cell Biology, and Physiology, Indiana University School of Medicine, 635 Barnhill Drive, MSB 5035, Indianapolis, IN, 46202, USA
- Stark Neuroscience Institute, Indiana University School of Medicine, 320 W. 15 St, NB 414, Indianapolis, IN 46202, USA
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10
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Coughlin B, Muñoz W, Kfir Y, Young MJ, Meszéna D, Jamali M, Caprara I, Hardstone R, Khanna A, Mustroph ML, Trautmann EM, Windolf C, Varol E, Soper DJ, Stavisky SD, Welkenhuysen M, Dutta B, Shenoy KV, Hochberg LR, Mark Richardson R, Williams ZM, Cash SS, Paulk AC. Modified Neuropixels probes for recording human neurophysiology in the operating room. Nat Protoc 2023; 18:2927-2953. [PMID: 37697108 DOI: 10.1038/s41596-023-00871-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 06/08/2023] [Indexed: 09/13/2023]
Abstract
Neuropixels are silicon-based electrophysiology-recording probes with high channel count and recording-site density. These probes offer a turnkey platform for measuring neural activity with single-cell resolution and at a scale that is beyond the capabilities of current clinically approved devices. Our team demonstrated the first-in-human use of these probes during resection surgery for epilepsy or tumors and deep brain stimulation electrode placement in patients with Parkinson's disease. Here, we provide a better understanding of the capabilities and challenges of using Neuropixels as a research tool to study human neurophysiology, with the hope that this information may inform future efforts toward regulatory approval of Neuropixels probes as research devices. In perioperative procedures, the major concerns are the initial sterility of the device, maintaining a sterile field during surgery, having multiple referencing and grounding schemes available to de-noise recordings (if necessary), protecting the silicon probe from accidental contact before insertion and obtaining high-quality action potential and local field potential recordings. The research team ensures that the device is fully operational while coordinating with the surgical team to remove sources of electrical noise that could otherwise substantially affect the signals recorded by the sensitive hardware. Prior preparation using the equipment and training in human clinical research and working in operating rooms maximize effective communication within and between the teams, ensuring high recording quality and minimizing the time added to the surgery. The perioperative procedure requires ~4 h, and the entire protocol requires multiple weeks.
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Affiliation(s)
- Brian Coughlin
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - William Muñoz
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Yoav Kfir
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Michael J Young
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Domokos Meszéna
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Mohsen Jamali
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Irene Caprara
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Richard Hardstone
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Arjun Khanna
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Martina L Mustroph
- Department of Neurosurgery, Harvard Medical School and Brigham & Women's Hospital, Boston, MA, USA
| | - Eric M Trautmann
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University Medical Center, New York, NY, USA
| | - Charlie Windolf
- Department of Statistics, Zuckerman Institute, Columbia University, New York, NY, USA
| | - Erdem Varol
- Department of Statistics, Zuckerman Institute, Columbia University, New York, NY, USA
- Department of Computer Science and Engineering, Zuckerman Institute, Columbia University, New York, NY, USA
| | - Dan J Soper
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Sergey D Stavisky
- Department of Neurological Surgery, University of California Davis, Davis, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
| | | | | | - Krishna V Shenoy
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Leigh R Hochberg
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA
- School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - R Mark Richardson
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Ziv M Williams
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA.
| | - Sydney S Cash
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
| | - Angelique C Paulk
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
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11
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Birman D, Yang KJ, West SJ, Karsh B, Browning Y, Siegle JH, Steinmetz NA. Pinpoint: trajectory planning for multi-probe electrophysiology and injections in an interactive web-based 3D environment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.14.548952. [PMID: 37503284 PMCID: PMC10369935 DOI: 10.1101/2023.07.14.548952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Targeting deep brain structures during electrophysiology and injections requires intensive training and expertise. Even with experience, researchers often can't be certain that a probe is placed precisely in a target location and this complexity scales with the number of simultaneous probes used in an experiment. Here, we present Pinpoint, open-source software that allows for interactive exploration of stereotaxic insertion plans. Once an insertion plan is created, Pinpoint allows users to save these online and share them with collaborators. 3D modeling tools allow users to explore their insertions alongside rig and implant hardware and ensure plans are physically possible. Probes in Pinpoint can be linked to electronic micro-manipulators allowing real-time visualization of current brain region targets alongside neural data. In addition, Pinpoint can control manipulators to automate and parallelize the insertion process. Compared to previously available software, Pinpoint's easy access through web browsers, extensive features, and real-time experiment integration enable more efficient and reproducible recordings.
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Affiliation(s)
- Daniel Birman
- Department of Biological Structure, University of Washington, Seattle, WA 98195, USA
| | - Kenneth J. Yang
- Department of Biological Structure, University of Washington, Seattle, WA 98195, USA
| | - Steven J. West
- Sainsbury Wellcome Centre, London W1T 4JG, United Kingdom
| | - Bill Karsh
- HHMI Janelia Research Campus, Ashburn, Virginia 20147, USA
| | - Yoni Browning
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | | | | | - Nicholas A. Steinmetz
- Department of Biological Structure, University of Washington, Seattle, WA 98195, USA
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12
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de Vries SEJ, Siegle JH, Koch C. Sharing neurophysiology data from the Allen Brain Observatory. eLife 2023; 12:e85550. [PMID: 37432073 PMCID: PMC10335829 DOI: 10.7554/elife.85550] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 06/27/2023] [Indexed: 07/12/2023] Open
Abstract
Nullius in verba ('trust no one'), chosen as the motto of the Royal Society in 1660, implies that independently verifiable observations-rather than authoritative claims-are a defining feature of empirical science. As the complexity of modern scientific instrumentation has made exact replications prohibitive, sharing data is now essential for ensuring the trustworthiness of one's findings. While embraced in spirit by many, in practice open data sharing remains the exception in contemporary systems neuroscience. Here, we take stock of the Allen Brain Observatory, an effort to share data and metadata associated with surveys of neuronal activity in the visual system of laboratory mice. Data from these surveys have been used to produce new discoveries, to validate computational algorithms, and as a benchmark for comparison with other data, resulting in over 100 publications and preprints to date. We distill some of the lessons learned about open surveys and data reuse, including remaining barriers to data sharing and what might be done to address these.
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13
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Shin H, Ogando MB, Abdeladim L, Durand S, Belski H, Cabasco H, Loefler H, Bawany A, Hardcastle B, Wilkes J, Nguyen K, Suarez L, Johnson T, Han W, Ouellette B, Grasso C, Swapp J, Ha V, Young A, Caldejon S, Williford A, Groblewski P, Olsen S, Kiselycznyk C, Lecoq J, Adesnik H. Recurrent pattern completion drives the neocortical representation of sensory inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.05.543698. [PMID: 37333175 PMCID: PMC10274729 DOI: 10.1101/2023.06.05.543698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
When sensory information is incomplete or ambiguous, the brain relies on prior expectations to infer perceptual objects. Despite the centrality of this process to perception, the neural mechanism of sensory inference is not known. Illusory contours (ICs) are key tools to study sensory inference because they contain edges or objects that are implied only by their spatial context. Using cellular resolution, mesoscale two-photon calcium imaging and multi-Neuropixels recordings in the mouse visual cortex, we identified a sparse subset of neurons in the primary visual cortex (V1) and higher visual areas that respond emergently to ICs. We found that these highly selective 'IC-encoders' mediate the neural representation of IC inference. Strikingly, selective activation of these neurons using two-photon holographic optogenetics was sufficient to recreate IC representation in the rest of the V1 network, in the absence of any visual stimulus. This outlines a model in which primary sensory cortex facilitates sensory inference by selectively strengthening input patterns that match prior expectations through local, recurrent circuitry. Our data thus suggest a clear computational purpose for recurrence in the generation of holistic percepts under sensory ambiguity. More generally, selective reinforcement of top-down predictions by pattern-completing recurrent circuits in lower sensory cortices may constitute a key step in sensory inference.
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Affiliation(s)
- Hyeyoung Shin
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
- Present Address: School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Mora B Ogando
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Lamiae Abdeladim
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | | | - Hannah Belski
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | | | - Henry Loefler
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Ahad Bawany
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | | | - Josh Wilkes
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | | | - Lucas Suarez
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Tye Johnson
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Warren Han
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Ben Ouellette
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Conor Grasso
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Jackie Swapp
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Vivian Ha
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Ahrial Young
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | | | - Ali Williford
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | | | - Shawn Olsen
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | | | - Jerome Lecoq
- Allen Institute, Mindscope Program, Seattle, WA, USA
| | - Hillel Adesnik
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
- The Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
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14
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Zhu L, Wang M, Liu Y, Fu P, Zhang W, Zhang H, Roe AW, Xi W. Single-microvessel occlusion produces lamina-specific microvascular flow vasodynamics and signs of neurodegenerative change. Cell Rep 2023; 42:112469. [PMID: 37141094 DOI: 10.1016/j.celrep.2023.112469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 01/12/2023] [Accepted: 04/18/2023] [Indexed: 05/05/2023] Open
Abstract
Recent studies have highlighted the importance of understanding the architecture and function of microvasculature, and dysfunction of these microvessels may underlie neurodegenerative disease. Here, we utilize a high-precision ultrafast laser-induced photothrombosis (PLP) method to occlude single capillaries and then quantitatively study the effects on vasodynamics and surrounding neurons. Analysis of the microvascular architecture and hemodynamics after single-capillary occlusion reveals distinct changes upstream vs. downstream branches, which shows rapid regional flow redistribution and local downstream blood-brain barrier (BBB) leakage. Focal ischemia via capillary occlusions surrounding labeled target neurons induces dramatic and rapid lamina-specific changes in neuronal dendritic architecture. Further, we find that micro-occlusion at two different depths within the same vascular arbor results in distinct effects on flow profiles in layers 2/3 vs layer 4. The current results reveal laminar-scale regulation distinctions in microinfarct response and raise the possibility that relatively greater impacts on microvascular function contribute to cognitive decline in neurodegenerative disease.
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Affiliation(s)
- Liang Zhu
- Interdisciplinary Institute of Neuroscience and Technology (ZIINT), The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310020, China; Interdisciplinary Institute of Neuroscience and Technology (ZIINT), College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Mengqi Wang
- Interdisciplinary Institute of Neuroscience and Technology (ZIINT), The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310020, China
| | - Yin Liu
- Interdisciplinary Institute of Neuroscience and Technology (ZIINT), The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310020, China
| | - Peng Fu
- Interdisciplinary Institute of Neuroscience and Technology (ZIINT), The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310020, China
| | - Weijie Zhang
- Interdisciplinary Institute of Neuroscience and Technology (ZIINT), College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Hequn Zhang
- Interdisciplinary Institute of Neuroscience and Technology (ZIINT), The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310020, China
| | - Anna Wang Roe
- Interdisciplinary Institute of Neuroscience and Technology (ZIINT), The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310020, China; MOE Frontier Science Center for Brain Research and Brain Machine Integration, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Biomedical Engineering of the Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China.
| | - Wang Xi
- Interdisciplinary Institute of Neuroscience and Technology (ZIINT), The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310020, China; MOE Frontier Science Center for Brain Research and Brain Machine Integration, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Biomedical Engineering of the Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China.
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