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Jia Q, Jing L, Zhu Y, Han M, Jiao P, Wang Y, Xu Z, Duan Y, Wang M, Cai X. Real-Time Precise Targeting of the Subthalamic Nucleus via Transfer Learning in a Rat Model of Parkinson's Disease Based on Microelectrode Arrays. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1787-1795. [PMID: 38656860 DOI: 10.1109/tnsre.2024.3393116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
In neurodegenerative disorders, neuronal firing patterns and oscillatory activity are remarkably altered in specific brain regions, which can serve as valuable biomarkers for the identification of deep brain regions. The subthalamic nucleus (STN) has been the primary target for DBS in patients with Parkinson's disease (PD). In this study, changes in the spike firing patterns and spectral power of local field potentials (LFPs) in the pre-STN (zona incerta, ZI) and post-STN (cerebral peduncle, cp) regions were investigated in PD rats, providing crucial evidence for the functional localization of the STN. Sixteen-channel microelectrode arrays (MEAs) with sites distributed at different depths and widths were utilized to record neuronal activities. The spikes in the STN exhibited higher firing rates than those in the ZI and cp. Furthermore, the LFP power in the delta band in the STN was the greatest, followed by that in the ZI, and was greater than that in the cp. Additionally, increased LFP power was observed in the beta bands in the STN. To identify the best performing classification model, we applied various convolutional neural networks (CNNs) based on transfer learning to analyze the recorded raw data, which were processed using the Gram matrix of the spikes and the fast Fourier transform of the LFPs. The best transfer learning model achieved an accuracy of 95.16%. After fusing the spike and LFP classification results, the time precision for processing the raw data reached 500 ms. The pretrained model, utilizing raw data, demonstrated the feasibility of employing transfer learning for training models on neural activity. This approach highlights the potential for functional localization within deep brain regions.
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2
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Chen FD, Sharma A, Roszko DA, Xue T, Mu X, Luo X, Chua H, Lo PGQ, Sacher WD, Poon JKS. Development of wafer-scale multifunctional nanophotonic neural probes for brain activity mapping. LAB ON A CHIP 2024; 24:2397-2417. [PMID: 38623840 DOI: 10.1039/d3lc00931a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
Optical techniques, such as optogenetic stimulation and functional fluorescence imaging, have been revolutionary for neuroscience by enabling neural circuit analysis with cell-type specificity. To probe deep brain regions, implantable light sources are crucial. Silicon photonics, commonly used for data communications, shows great promise in creating implantable devices with complex optical systems in a compact form factor compatible with high volume manufacturing practices. This article reviews recent developments of wafer-scale multifunctional nanophotonic neural probes. The probes can be realized on 200 or 300 mm wafers in commercial foundries and integrate light emitters for photostimulation, microelectrodes for electrophysiological recording, and microfluidic channels for chemical delivery and sampling. By integrating active optical devices to the probes, denser emitter arrays, enhanced on-chip biosensing, and increased ease of use may be realized. Silicon photonics technology makes possible highly versatile implantable neural probes that can transform neuroscience experiments.
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Affiliation(s)
- Fu Der Chen
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
- Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
| | - Ankita Sharma
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
- Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
| | - David A Roszko
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
- Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
| | - Tianyuan Xue
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
- Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
| | - Xin Mu
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
- Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
| | - Xianshu Luo
- Advanced Micro Foundry Pte Ltd, 11 Science Park Road, Singapore Science Park II, 117685, Singapore
| | - Hongyao Chua
- Advanced Micro Foundry Pte Ltd, 11 Science Park Road, Singapore Science Park II, 117685, Singapore
| | - Patrick Guo-Qiang Lo
- Advanced Micro Foundry Pte Ltd, 11 Science Park Road, Singapore Science Park II, 117685, Singapore
| | - Wesley D Sacher
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
| | - Joyce K S Poon
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
- Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
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3
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Rankin G, Chirila AM, Emanuel AJ, Zhang Z, Woolf CJ, Drugowitsch J, Ginty DD. Nerve injury disrupts temporal processing in the spinal cord dorsal horn through alterations in PV + interneurons. Cell Rep 2024; 43:113718. [PMID: 38294904 PMCID: PMC11101906 DOI: 10.1016/j.celrep.2024.113718] [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: 03/15/2023] [Revised: 11/13/2023] [Accepted: 01/11/2024] [Indexed: 02/02/2024] Open
Abstract
How mechanical allodynia following nerve injury is encoded in patterns of neural activity in the spinal cord dorsal horn (DH) remains incompletely understood. We address this in mice using the spared nerve injury model of neuropathic pain and in vivo electrophysiological recordings. Surprisingly, despite dramatic behavioral over-reactivity to mechanical stimuli following nerve injury, an overall increase in sensitivity or reactivity of DH neurons is not observed. We do, however, observe a marked decrease in correlated neural firing patterns, including the synchrony of mechanical stimulus-evoked firing, across the DH. Alterations in DH temporal firing patterns are recapitulated by silencing DH parvalbumin+ (PV+) interneurons, previously implicated in mechanical allodynia, as are allodynic pain-like behaviors. These findings reveal decorrelated DH network activity, driven by alterations in PV+ interneurons, as a prominent feature of neuropathic pain and suggest restoration of proper temporal activity as a potential therapeutic strategy to treat chronic neuropathic pain.
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Affiliation(s)
- Genelle Rankin
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Anda M Chirila
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Alan J Emanuel
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Zihe Zhang
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA; F.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA 02115, USA
| | - Clifford J Woolf
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA; F.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA 02115, USA
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - David D Ginty
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA.
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4
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Haynes VR, Zhou Y, Crook SM. Discovering optimal features for neuron-type identification from extracellular recordings. Front Neuroinform 2024; 18:1303993. [PMID: 38371496 PMCID: PMC10869512 DOI: 10.3389/fninf.2024.1303993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 01/15/2024] [Indexed: 02/20/2024] Open
Abstract
Advancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel features of spatially-varying extracellularly-recorded action potentials (EAPs) that are useful for identifying neuron-types. Traditional approaches to classifying neuron-types often rely on computing EAP waveform features based on conventions of single-channel recordings and thus inherit their limitations. However, spatiotemporal EAP waveforms are the product of signals from underlying current sources being mixed within the extracellular space. We introduce a machine learning approach to demix the underlying sources of spatiotemporal EAP waveforms. Using biophysically realistic computational models, we simulate EAP waveforms and characterize them by the relative prevalence of these sources, which we use as features for identifying the neuron-types corresponding to recorded single units. These EAP sources have distinct spatial and multi-resolution temporal patterns that are robust to various sampling biases. EAP sources also are shared across many neuron-types, are predictive of gross morphological features, and expose underlying morphological domains. We then organize known neuron-types into a hierarchy of latent morpho-electrophysiological types based on differences in the source prevalences, which provides a multi-level classification scheme. We validate the robustness, accuracy, and interpretations of our demixing approach by analyzing simulated EAPs from morphologically detailed models with classification and clustering methods. This simulation-based approach provides a machine learning strategy for neuron-type identification.
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Affiliation(s)
- Vergil R. Haynes
- Laboratory for Auditory Computation and Neurophysiology, College of Health Solutions, Arizona State University, Tempe, AZ, United States
- Laboratory for Informatics and Computation in Open Neuroscience, School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United States
| | - Yi Zhou
- Laboratory for Auditory Computation and Neurophysiology, College of Health Solutions, Arizona State University, Tempe, AZ, United States
| | - Sharon M. Crook
- Laboratory for Informatics and Computation in Open Neuroscience, School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United States
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5
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Zhang LA, Li P, Callaway EM. High-Resolution Laminar Identification in Macaque Primary Visual Cortex Using Neuropixels Probes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576944. [PMID: 38328229 PMCID: PMC10849622 DOI: 10.1101/2024.01.23.576944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Laminar electrode arrays allow simultaneous recording of activity of many cortical neurons and assignment to correct layers using current source density (CSD) analyses. Electrode arrays with 100-micron contact spacing can estimate borders between layer 4 versus superficial or deep layers, but in macaque primary visual cortex (V1) there are far more layers, such as 4A which is only 50-100 microns thick. Neuropixels electrode arrays have 20-micron spacing, and thus could potentially discern thinner layers and more precisely identify laminar borders. Here we show that CSD signals lack the spatial resolution required to take advantage of high density Neuropixels arrays and describe the development of approaches based on higher resolution electrical signals and analyses, including spike waveforms and spatial spread, unit density, high-frequency action potential (AP) power spectrum, temporal power change, and coherence spectrum, that afford far higher resolution of laminar distinctions, including the ability to precisely detect the borders of even the thinnest layers of V1.
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Affiliation(s)
- Li A. Zhang
- The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Peichao Li
- Department of Neurology of the Second Affiliated Hospital and Interdisciplinary Institute of Neuroscience and Technology, School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science & Brain-Machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
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6
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Paulk AC, Salami P, Zelmann R, Cash SS. Electrode Development for Epilepsy Diagnosis and Treatment. Neurosurg Clin N Am 2024; 35:135-149. [PMID: 38000837 DOI: 10.1016/j.nec.2023.09.003] [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: 11/26/2023]
Abstract
Recording neural activity has been a critical aspect in the diagnosis and treatment of patients with epilepsy. For those with intractable epilepsy, intracranial neural monitoring has been of substantial importance. Clinically, however, methods for recording neural information have remained essentially unchanged for decades. Over the last decade or so, rapid advances in electrode technology have begun to change this landscape. New systems allow for the observation of neural activity with high spatial resolution and, in some cases, at the level of the activity of individual neurons. These new tools have contributed greatly to our understanding of brain function and dysfunction. Here, the authors review the primary technologies currently in use in humans. The authors discuss other possible systems, some of the challenges which come along with these devices, and how they will become incorporated into the clinical workflow. Ultimately, the expectation is that these new, high-density, high-spatial-resolution recording systems will become a valuable part of the clinical arsenal used in the diagnosis and surgical management of epilepsy.
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Affiliation(s)
- Angelique C Paulk
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA.
| | - Pariya Salami
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA
| | - Rina Zelmann
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA
| | - Sydney S Cash
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA
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7
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Jeong H, Namboodiri VMK, Jung MW, Andermann ML. Sensory cortical ensembles exhibit differential coupling to ripples in distinct hippocampal subregions. Curr Biol 2023; 33:5185-5198.e4. [PMID: 37995696 PMCID: PMC10842729 DOI: 10.1016/j.cub.2023.10.073] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 08/29/2023] [Accepted: 10/31/2023] [Indexed: 11/25/2023]
Abstract
Cortical neurons activated during recent experiences often reactivate with dorsal hippocampal CA1 ripples during subsequent rest. Less is known about cortical interactions with intermediate hippocampal CA1, whose connectivity, functions, and ripple events differ from dorsal CA1. We identified three clusters of putative excitatory neurons in mouse visual cortex that are preferentially excited together with either dorsal or intermediate CA1 ripples or suppressed before both ripples. Neurons in each cluster were evenly distributed across primary and higher visual cortices and co-active even in the absence of ripples. These ensembles exhibited similar visual responses but different coupling to thalamus and pupil-indexed arousal. We observed a consistent activity sequence preceding and predicting ripples: (1) suppression of ripple-suppressed cortical neurons, (2) thalamic silence, and (3) activation of intermediate CA1-ripple-activated cortical neurons. We propose that coordinated dynamics of these ensembles relay visual experiences to distinct hippocampal subregions for incorporation into different cognitive maps.
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Affiliation(s)
- Huijeong Jeong
- Department of Neurology, University of California, San Francisco, 1651 4th Street, San Francisco, CA 94158, USA; Center for Synaptic Brain Dysfunctions, Institute for Basic Science, 291 Daehak-ro, Daejeon 34141, Republic of Korea; Department of Biological Sciences, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, Republic of Korea
| | - Vijay Mohan K Namboodiri
- Department of Neurology, University of California, San Francisco, 1651 4th Street, San Francisco, CA 94158, USA; Neuroscience Graduate Program, University of California, San Francisco, 1651 4th Street, San Francisco, CA 94158, USA; Weill Institute for Neuroscience, Kavli Institute for Fundamental Neuroscience, Center for Integrative Neuroscience, University of California, San Francisco, 1651 4th Street, San Francisco, CA 94158, USA.
| | - Min Whan Jung
- Center for Synaptic Brain Dysfunctions, Institute for Basic Science, 291 Daehak-ro, Daejeon 34141, Republic of Korea; Department of Biological Sciences, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon 34141, Republic of Korea.
| | - Mark L Andermann
- Division of Endocrinology, Metabolism, and Diabetes, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA; Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, MA 02115, USA.
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8
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Windolf C, Yu H, Paulk AC, Meszéna D, Muñoz W, Boussard J, Hardstone R, Caprara I, Jamali M, Kfir Y, Xu D, Chung JE, Sellers KK, Ye Z, Shaker J, Lebedeva A, Raghavan M, Trautmann E, Melin M, Couto J, Garcia S, Coughlin B, Horváth C, Fiáth R, Ulbert I, Movshon JA, Shadlen MN, Churchland MM, Churchland AK, Steinmetz NA, Chang EF, Schweitzer JS, Williams ZM, Cash SS, Paninski L, Varol E. DREDge: robust motion correction for high-density extracellular recordings across species. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.24.563768. [PMID: 37961359 PMCID: PMC10634799 DOI: 10.1101/2023.10.24.563768] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
High-density microelectrode arrays (MEAs) have opened new possibilities for systems neuroscience in human and non-human animals, but brain tissue motion relative to the array poses a challenge for downstream analyses, particularly in human recordings. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm which is well suited for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from spikes in the action potential (AP) frequency band, DREDge enables automated tracking of motion at high temporal resolution in the local field potential (LFP) frequency band. In human intraoperative recordings, which often feature fast (period <1s) motion, DREDge correction in the LFP band enabled reliable recovery of evoked potentials, and significantly reduced single-unit spike shape variability and spike sorting error. Applying DREDge to recordings made during deep probe insertions in nonhuman primates demonstrated the possibility of tracking probe motion of centimeters across several brain regions while simultaneously mapping single unit electrophysiological features. DREDge reliably delivered improved motion correction in acute mouse recordings, especially in those made with an recent ultra-high density probe. We also implemented a procedure for applying DREDge to recordings made across tens of days in chronic implantations in mice, reliably yielding stable motion tracking despite changes in neural activity across experimental sessions. Together, these advances enable automated, scalable registration of electrophysiological data across multiple species, probe types, and drift cases, providing a stable foundation for downstream scientific analyses of these rich datasets.
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Affiliation(s)
- Charlie Windolf
- Department of Statistics, Columbia University
- Zuckerman Institute, Columbia University
| | - Han Yu
- Zuckerman Institute, Columbia University
- Department of Electrical Engineering, Columbia University
| | - Angelique C Paulk
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School
| | - Domokos Meszéna
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - William Muñoz
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School
| | - Julien Boussard
- Department of Statistics, Columbia University
- Zuckerman Institute, Columbia University
| | - Richard Hardstone
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School
| | - Irene Caprara
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School
| | - Mohsen Jamali
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School
| | - Yoav Kfir
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School
| | - Duo Xu
- Weill Institute for Neurosciences, University of California San Francisco
- Department of Neurological Surgery, University of California San Francisco
| | - Jason E Chung
- Department of Neurological Surgery, University of California San Francisco
| | - Kristin K Sellers
- Weill Institute for Neurosciences, University of California San Francisco
- Department of Neurological Surgery, University of California San Francisco
| | - Zhiwen Ye
- Department of Biological Structure, University of Washington
| | - Jordan Shaker
- Department of Biological Structure, University of Washington
| | | | | | - Eric Trautmann
- Department of Neuroscience, Columbia University Medical Center
- Zuckerman Institute, Columbia University
- Grossman Center for the Statistics of Mind, Columbia University
| | - Max Melin
- David Geffen School of Medicine, University of California Los Angeles
| | - João Couto
- David Geffen School of Medicine, University of California Los Angeles
| | - Samuel Garcia
- Centre National de la Recherche Scientifique, Centre de Recherche en Neurosciences de Lyon
| | - Brian Coughlin
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School
| | - Csaba Horváth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - Richárd Fiáth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - István Ulbert
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | | | - Michael N Shadlen
- Zuckerman Institute, Columbia University
- Howard Hughes Medical Institute
| | | | - Anne K Churchland
- David Geffen School of Medicine, University of California Los Angeles
| | | | - Edward F Chang
- Weill Institute for Neurosciences, University of California San Francisco
- Department of Neurological Surgery, University of California San Francisco
| | - Jeffrey S Schweitzer
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School
| | - Ziv M Williams
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School
| | - Sydney S Cash
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School
| | - Liam Paninski
- Department of Statistics, Columbia University
- Zuckerman Institute, Columbia University
- Department of Neuroscience, Columbia University Medical Center
- Grossman Center for the Statistics of Mind, Columbia University
| | - Erdem Varol
- Department of Statistics, Columbia University
- Zuckerman Institute, Columbia University
- Department of Computer Science & Engineering, New York University
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9
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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: 0] [Impact Index Per Article: 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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10
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Someck S, Levi A, Sloin HE, Spivak L, Gattegno R, Stark E. Positive and biphasic extracellular waveforms correspond to return currents and axonal spikes. Commun Biol 2023; 6:950. [PMID: 37723241 PMCID: PMC10507124 DOI: 10.1038/s42003-023-05328-6] [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: 04/18/2023] [Accepted: 09/06/2023] [Indexed: 09/20/2023] Open
Abstract
Multiple biophysical mechanisms may generate non-negative extracellular waveforms during action potentials, but the origin and prevalence of positive spikes and biphasic spikes in the intact brain are unknown. Using extracellular recordings from densely-connected cortical networks in freely-moving mice, we find that a tenth of the waveforms are non-negative. Positive phases of non-negative spikes occur in synchrony or just before wider same-unit negative spikes. Narrow positive spikes occur in isolation in the white matter. Isolated biphasic spikes are narrower than negative spikes, occurring right after spikes of verified inhibitory units. In CA1, units with dominant non-negative spikes exhibit place fields, phase precession, and phase-locking to ripples. Thus, near-somatic narrow positive extracellular potentials correspond to return currents, and isolated non-negative spikes correspond to axonal potentials. Identifying non-negative extracellular waveforms that correspond to non-somatic compartments during spikes can enhance the understanding of physiological and pathological neural mechanisms in intact animals.
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Affiliation(s)
- Shirly Someck
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, 6997801, Israel
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Amir Levi
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, 6997801, Israel
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Hadas E Sloin
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, 6997801, Israel
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Lidor Spivak
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, 6997801, Israel
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Roni Gattegno
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Eran Stark
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, 6997801, Israel.
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel.
- Sagol Department of Neurobiology, Haifa University, Haifa, 3103301, Israel.
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11
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Buccino AP, Winter O, Bryant D, Feng D, Svoboda K, Siegle JH. Compression strategies for large-scale electrophysiology data. J Neural Eng 2023; 20:056009. [PMID: 37651998 DOI: 10.1088/1741-2552/acf5a4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/31/2023] [Indexed: 09/02/2023]
Abstract
Objective.With the rapid adoption of high-density electrode arrays for recording neural activity, electrophysiology data volumes within labs and across the field are growing at unprecedented rates. For example, a one-hour recording with a 384-channel Neuropixels probe generates over 80 GB of raw data. These large data volumes carry a high cost, especially if researchers plan to store and analyze their data in the cloud. Thus, there is a pressing need for strategies that can reduce the data footprint of each experiment.Approach.Here, we establish a set of benchmarks for comparing the performance of various compression algorithms on experimental and simulated recordings from Neuropixels 1.0 (NP1) and 2.0 (NP2) probes.Main results.For lossless compression, audio codecs (FLACandWavPack) achieve compression ratios (CRs) 6% higher for NP1 and 10% higher for NP2 than the best general-purpose codecs, at the expense of decompression speed. For lossy compression, theWavPackalgorithm in 'hybrid mode' increases the CR from 3.59 to 7.08 for NP1 and from 2.27 to 7.04 for NP2 (compressed file size of ∼14% for both types of probes), without adverse effects on spike sorting accuracy or spike waveforms.Significance.Along with the tools we have developed to make compression easier to deploy, these results should encourage all electrophysiologists to apply compression as part of their standard analysis workflows.
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Affiliation(s)
- Alessio P Buccino
- Allen Institute for Neural Dynamics, Seattle, WA, United States of America
| | | | - David Bryant
- Independent Researcher, San Francisco, CA, United States of America
| | - David Feng
- Allen Institute for Neural Dynamics, Seattle, WA, United States of America
| | - Karel Svoboda
- Allen Institute for Neural Dynamics, Seattle, WA, United States of America
| | - Joshua H Siegle
- Allen Institute for Neural Dynamics, Seattle, WA, United States of America
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12
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Jung YJ, Sun SH, Almasi A, Yunzab M, Meffin H, Ibbotson MR. Characterization of extracellular spike waveforms recorded in wallaby primary visual cortex. Front Neurosci 2023; 17:1244952. [PMID: 37746137 PMCID: PMC10517629 DOI: 10.3389/fnins.2023.1244952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 08/25/2023] [Indexed: 09/26/2023] Open
Abstract
Extracellular recordings were made from 642 units in the primary visual cortex (V1) of a highly visual marsupial, the Tammar wallaby. The receptive field (RF) characteristics of the cells were objectively estimated using the non-linear input model (NIM), and these were correlated with spike shapes. We found that wallaby cortical units had 68% regular spiking (RS), 12% fast spiking (FS), 4% triphasic spiking (TS), 5% compound spiking (CS) and 11% positive spiking (PS). RS waveforms are most often associated with recordings from pyramidal or spiny stellate cell bodies, suggesting that recordings from these cell types dominate in the wallaby cortex. In wallaby, 70-80% of FS and RS cells had orientation selective RFs and had evenly distributed linear and nonlinear RFs. We found that 47% of wallaby PS units were non-orientation selective and they were dominated by linear RFs. Previous studies suggest that the PS units represent recordings from the axon terminals of non-orientation selective cells originating in the lateral geniculate nucleus (LGN). If this is also true in wallaby, as strongly suggested by their low response latencies and bursty spiking properties, the results suggest that significantly more neurons in wallaby LGN are already orientation selective. In wallaby, less than 10% of recorded spikes had triphasic (TS) or sluggish compound spiking (CS) waveforms. These units had a mixture of orientation selective and non-oriented properties, and their cellular origins remain difficult to classify.
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Affiliation(s)
- Young Jun Jung
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
- National Vision Research Institute, Australian College of Optometry Carlton, Carlton, VIC, Australia
- Department of Optometry and Vision Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Shi H. Sun
- National Vision Research Institute, Australian College of Optometry Carlton, Carlton, VIC, Australia
| | - Ali Almasi
- National Vision Research Institute, Australian College of Optometry Carlton, Carlton, VIC, Australia
| | - Molis Yunzab
- National Vision Research Institute, Australian College of Optometry Carlton, Carlton, VIC, Australia
| | - Hamish Meffin
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Michael R. Ibbotson
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
- National Vision Research Institute, Australian College of Optometry Carlton, Carlton, VIC, Australia
- Department of Optometry and Vision Sciences, The University of Melbourne, Melbourne, VIC, Australia
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13
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Gehr C, Sibille J, Kremkow J. Retinal input integration in excitatory and inhibitory neurons in the mouse superior colliculus in vivo. eLife 2023; 12:RP88289. [PMID: 37682267 PMCID: PMC10491433 DOI: 10.7554/elife.88289] [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: 09/09/2023] Open
Abstract
The superior colliculus (SC) is a midbrain structure that receives inputs from retinal ganglion cells (RGCs). The SC contains one of the highest densities of inhibitory neurons in the brain but whether excitatory and inhibitory SC neurons differentially integrate retinal activity in vivo is still largely unknown. We recently established a recording approach to measure the activity of RGCs simultaneously with their postsynaptic SC targets in vivo, to study how SC neurons integrate RGC activity. Here, we employ this method to investigate the functional properties that govern retinocollicular signaling in a cell type-specific manner by identifying GABAergic SC neurons using optotagging in VGAT-ChR2 mice. Our results demonstrate that both excitatory and inhibitory SC neurons receive comparably strong RGC inputs and similar wiring rules apply for RGCs innervation of both SC cell types, unlike the cell type-specific connectivity in the thalamocortical system. Moreover, retinal activity contributed more to the spiking activity of postsynaptic excitatory compared to inhibitory SC neurons. This study deepens our understanding of cell type-specific retinocollicular functional connectivity and emphasizes that the two major brain areas for visual processing, the visual cortex and the SC, differently integrate sensory afferent inputs.
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Affiliation(s)
- Carolin Gehr
- Neuroscience Research Center, Charité-Universitätsmedizin BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
- Institute for Theoretical Biology, Humboldt-Universität zu BerlinBerlinGermany
- Einstein Center for Neurosciences BerlinBerlinGermany
| | - Jeremie Sibille
- Neuroscience Research Center, Charité-Universitätsmedizin BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
- Institute for Theoretical Biology, Humboldt-Universität zu BerlinBerlinGermany
- Einstein Center for Neurosciences BerlinBerlinGermany
| | - Jens Kremkow
- Neuroscience Research Center, Charité-Universitätsmedizin BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
- Institute for Theoretical Biology, Humboldt-Universität zu BerlinBerlinGermany
- Einstein Center for Neurosciences BerlinBerlinGermany
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14
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Ding L, Balsamo G, Diamantaki M, Preston-Ferrer P, Burgalossi A. Opto-juxtacellular interrogation of neural circuits in freely moving mice. Nat Protoc 2023; 18:2415-2440. [PMID: 37420087 DOI: 10.1038/s41596-023-00842-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 04/11/2023] [Indexed: 07/09/2023]
Abstract
Neural circuits are assembled from an enormous variety of neuronal cell types. Although significant advances have been made in classifying neurons on the basis of morphological, molecular and electrophysiological properties, understanding how this diversity contributes to brain function during behavior has remained a major experimental challenge. Here, we present an extension to our previous protocol, in which we describe the technical procedures for performing juxtacellular opto-tagging of single neurons in freely moving mice by using Channelrhodopsin-2-expressing viral vectors. This method allows one to selectively target molecularly defined cell classes for in vivo single-cell recordings. The targeted cells can be labeled via juxtacellular procedures and further characterized via post-hoc morphological and molecular analysis. In its current form, the protocol allows multiple recording and labeling attempts to be performed within individual animals, by means of a mechanical pipette micropositioning system. We provide proof-of-principle validation of this technique by recording from Calbindin-positive pyramidal neurons in the mouse hippocampus during spatial exploration; however, this approach can easily be extended to other behaviors and cortical or subcortical areas. The procedures described here, from the viral injection to the histological processing of brain sections, can be completed in ~4-5 weeks.This protocol is an extension to: Nat. Protoc. 9, 2369-2381 (2014): https://doi.org/10.1038/nprot.2014.161.
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Affiliation(s)
- Lingjun Ding
- Institute of Neurobiology, Eberhard Karls University of Tübingen, Tübingen, Germany
- Werner-Reichardt Centre for Integrative Neuroscience, Tübingen, Germany
- Graduate Training Centre of Neuroscience-International Max-Planck Research School (IMPRS), Tübingen, Germany
| | - Giuseppe Balsamo
- Institute of Neurobiology, Eberhard Karls University of Tübingen, Tübingen, Germany
- Werner-Reichardt Centre for Integrative Neuroscience, Tübingen, Germany
- Graduate Training Centre of Neuroscience-International Max-Planck Research School (IMPRS), Tübingen, Germany
| | - Maria Diamantaki
- Institute of Neurobiology, Eberhard Karls University of Tübingen, Tübingen, Germany
- Werner-Reichardt Centre for Integrative Neuroscience, Tübingen, Germany
- Graduate Training Centre of Neuroscience-International Max-Planck Research School (IMPRS), Tübingen, Germany
- Institute of Molecular Biology and Biotechnology, Foundation of Research and Technology-Hellas, Heraklion, Greece
| | - Patricia Preston-Ferrer
- Institute of Neurobiology, Eberhard Karls University of Tübingen, Tübingen, Germany.
- Werner-Reichardt Centre for Integrative Neuroscience, Tübingen, Germany.
| | - Andrea Burgalossi
- Institute of Neurobiology, Eberhard Karls University of Tübingen, Tübingen, Germany.
- Werner-Reichardt Centre for Integrative Neuroscience, Tübingen, Germany.
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15
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Claar LD, Rembado I, Kuyat JR, Russo S, Marks LC, Olsen SR, Koch C. Cortico-thalamo-cortical interactions modulate electrically evoked EEG responses in mice. eLife 2023; 12:RP84630. [PMID: 37358562 DOI: 10.7554/elife.84630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2023] Open
Abstract
Perturbational complexity analysis predicts the presence of consciousness in volunteers and patients by stimulating the brain with brief pulses, recording EEG responses, and computing their spatiotemporal complexity. We examined the underlying neural circuits in mice by directly stimulating cortex while recording with EEG and Neuropixels probes during wakefulness and isoflurane anesthesia. When mice are awake, stimulation of deep cortical layers reliably evokes locally a brief pulse of excitation, followed by a biphasic sequence of 120 ms profound off period and a rebound excitation. A similar pattern, partially attributed to burst spiking, is seen in thalamic nuclei and is associated with a pronounced late component in the evoked EEG. We infer that cortico-thalamo-cortical interactions drive the long-lasting evoked EEG signals elicited by deep cortical stimulation during the awake state. The cortical and thalamic off period and rebound excitation, and the late component in the EEG, are reduced during running and absent during anesthesia.
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Affiliation(s)
- Leslie D Claar
- MindScope Program, Allen Institute, Seattle, United States
| | - Irene Rembado
- MindScope Program, Allen Institute, Seattle, United States
| | | | - Simone Russo
- MindScope Program, Allen Institute, Seattle, United States
- Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy
| | - Lydia C Marks
- MindScope Program, Allen Institute, Seattle, United States
| | - Shawn R Olsen
- MindScope Program, Allen Institute, Seattle, United States
| | - Christof Koch
- MindScope Program, Allen Institute, Seattle, United States
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16
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Lee K, Carr N, Perliss A, Chandrasekaran C. WaveMAP for identifying putative cell types from in vivo electrophysiology. STAR Protoc 2023; 4:102320. [PMID: 37220000 DOI: 10.1016/j.xpro.2023.102320] [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: 01/17/2023] [Revised: 03/28/2023] [Accepted: 04/27/2023] [Indexed: 05/25/2023] Open
Abstract
Action potential spike widths are used to classify cell types as either excitatory or inhibitory; however, this approach obscures other differences in waveform shape useful for identifying more fine-grained cell types. Here, we present a protocol for using WaveMAP to generate nuanced average waveform clusters more closely linked to underlying cell types. We describe steps for installing WaveMAP, preprocessing data, and clustering waveform into putative cell types. We also detail cluster evaluation for functional differences and interpretation of WaveMAP output. For complete details on the use and execution of this protocol, please refer to Lee et al. (2021).1.
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Affiliation(s)
- Kenji Lee
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA.
| | - Nicole Carr
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Alec Perliss
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Chandramouli Chandrasekaran
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA; Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA; Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian, School of Medicine, Boston, MA 02118, USA; Center for Systems Neuroscience, Boston University, Boston, MA 02215, USA.
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17
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Trautmann EM, Hesse JK, Stine GM, Xia R, Zhu S, O'Shea DJ, Karsh B, Colonell J, Lanfranchi FF, Vyas S, Zimnik A, Steinmann NA, Wagenaar DA, Andrei A, Lopez CM, O'Callaghan J, Putzeys J, Raducanu BC, Welkenhuysen M, Churchland M, Moore T, Shadlen M, Shenoy K, Tsao D, Dutta B, Harris T. Large-scale high-density brain-wide neural recording in nonhuman primates. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.01.526664. [PMID: 37205406 PMCID: PMC10187172 DOI: 10.1101/2023.02.01.526664] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
High-density, integrated silicon electrodes have begun to transform systems neuroscience, by enabling large-scale neural population recordings with single cell resolution. Existing technologies, however, have provided limited functionality in nonhuman primate species such as macaques, which offer close models of human cognition and behavior. Here, we report the design, fabrication, and performance of Neuropixels 1.0-NHP, a high channel count linear electrode array designed to enable large-scale simultaneous recording in superficial and deep structures within the macaque or other large animal brain. These devices were fabricated in two versions: 4416 electrodes along a 45 mm shank, and 2496 along a 25 mm shank. For both versions, users can programmatically select 384 channels, enabling simultaneous multi-area recording with a single probe. We demonstrate recording from over 3000 single neurons within a session, and simultaneous recordings from over 1000 neurons using multiple probes. This technology represents a significant increase in recording access and scalability relative to existing technologies, and enables new classes of experiments involving fine-grained electrophysiological characterization of brain areas, functional connectivity between cells, and simultaneous brain-wide recording at scale.
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18
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Li Q, Lin Z, Liu R, Tang X, Huang J, He Y, Sui X, Tian W, Shen H, Zhou H, Sheng H, Shi H, Xiao L, Wang X, Liu J. Multimodal charting of molecular and functional cell states via in situ electro-sequencing. Cell 2023; 186:2002-2017.e21. [PMID: 37080201 DOI: 10.1016/j.cell.2023.03.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/21/2022] [Accepted: 03/16/2023] [Indexed: 04/22/2023]
Abstract
Paired mapping of single-cell gene expression and electrophysiology is essential to understand gene-to-function relationships in electrogenic tissues. Here, we developed in situ electro-sequencing (electro-seq) that combines flexible bioelectronics with in situ RNA sequencing to stably map millisecond-timescale electrical activity and profile single-cell gene expression from the same cells across intact biological networks, including cardiac and neural patches. When applied to human-induced pluripotent stem-cell-derived cardiomyocyte patches, in situ electro-seq enabled multimodal in situ analysis of cardiomyocyte electrophysiology and gene expression at the cellular level, jointly defining cell states and developmental trajectories. Using machine-learning-based cross-modal analysis, in situ electro-seq identified gene-to-electrophysiology relationships throughout cardiomyocyte development and accurately reconstructed the evolution of gene expression profiles based on long-term stable electrical measurements. In situ electro-seq could be applicable to create spatiotemporal multimodal maps in electrogenic tissues, potentiating the discovery of cell types and gene programs responsible for electrophysiological function and dysfunction.
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Affiliation(s)
- Qiang Li
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | - Zuwan Lin
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | - Ren Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | - Xin Tang
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jiahao Huang
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Yichun He
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Xin Sui
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Weiwen Tian
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | - Hao Shen
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | - Haowen Zhou
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Hao Sheng
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | - Hailing Shi
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ling Xiao
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Cardiovascular Disease Initiative and Precision Cardiology Laboratory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Xiao Wang
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Jia Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA.
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19
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Schneider A, Azabou M, McDougall-Vigier L, Parks DF, Ensley S, Bhaskaran-Nair K, Nowakowski T, Dyer EL, Hengen KB. Transcriptomic cell type structures in vivo neuronal activity across multiple timescales. Cell Rep 2023; 42:112318. [PMID: 36995938 PMCID: PMC10539488 DOI: 10.1016/j.celrep.2023.112318] [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: 07/07/2022] [Revised: 02/04/2023] [Accepted: 03/15/2023] [Indexed: 03/30/2023] Open
Abstract
Cell type is hypothesized to be a key determinant of a neuron's role within a circuit. Here, we examine whether a neuron's transcriptomic type influences the timing of its activity. We develop a deep-learning architecture that learns features of interevent intervals across timescales (ms to >30 min). We show that transcriptomic cell-class information is embedded in the timing of single neuron activity in the intact brain of behaving animals (calcium imaging and extracellular electrophysiology) as well as in a bio-realistic model of the visual cortex. Further, a subset of excitatory cell types are distinguishable but can be classified with higher accuracy when considering cortical layer and projection class. Finally, we show that computational fingerprints of cell types may be universalizable across structured stimuli and naturalistic movies. Our results indicate that transcriptomic class and type may be imprinted in the timing of single neuron activity across diverse stimuli.
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Affiliation(s)
- Aidan Schneider
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Mehdi Azabou
- School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | | | - David F Parks
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Sahara Ensley
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Kiran Bhaskaran-Nair
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Tomasz Nowakowski
- Department of Anatomy, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Eva L Dyer
- School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Keith B Hengen
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA.
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20
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Wei Y, Nandi A, Jia X, Siegle JH, Denman D, Lee SY, Buchin A, Van Geit W, Mosher CP, Olsen S, Anastassiou CA. Associations between in vitro, in vivo and in silico cell classes in mouse primary visual cortex. Nat Commun 2023; 14:2344. [PMID: 37095130 PMCID: PMC10126114 DOI: 10.1038/s41467-023-37844-8] [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: 04/07/2021] [Accepted: 03/31/2023] [Indexed: 04/26/2023] Open
Abstract
The brain consists of many cell classes yet in vivo electrophysiology recordings are typically unable to identify and monitor their activity in the behaving animal. Here, we employed a systematic approach to link cellular, multi-modal in vitro properties from experiments with in vivo recorded units via computational modeling and optotagging experiments. We found two one-channel and six multi-channel clusters in mouse visual cortex with distinct in vivo properties in terms of activity, cortical depth, and behavior. We used biophysical models to map the two one- and the six multi-channel clusters to specific in vitro classes with unique morphology, excitability and conductance properties that explain their distinct extracellular signatures and functional characteristics. These concepts were tested in ground-truth optotagging experiments with two inhibitory classes unveiling distinct in vivo properties. This multi-modal approach presents a powerful way to separate in vivo clusters and infer their cellular properties from first principles.
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Affiliation(s)
- Yina Wei
- Zhejiang Lab, Hangzhou, 311100, China.
- Allen Institute for Brain Science, Seattle, WA, 98109, USA.
| | - Anirban Nandi
- Allen Institute for Brain Science, Seattle, WA, 98109, USA
| | - Xiaoxuan Jia
- Allen Institute for Brain Science, Seattle, WA, 98109, USA
- School of Life Sciences/McGovern Institute for Brain Research, Tsinghua University, 100084, Beijing, China
| | | | | | - Soo Yeun Lee
- Allen Institute for Brain Science, Seattle, WA, 98109, USA
| | - Anatoly Buchin
- Allen Institute for Brain Science, Seattle, WA, 98109, USA
- Cajal Neuroscience Inc, Seattle, WA, 98102, USA
| | - Werner Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL) Campus Biotech, Geneva, 1202, Switzerland
| | - Clayton P Mosher
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Shawn Olsen
- Allen Institute for Brain Science, Seattle, WA, 98109, USA
| | - Costas A Anastassiou
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.
- Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.
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21
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Wei Y, Nandi A, Jia X, Siegle JH, Denman D, Lee SY, Buchin A, Geit WV, Mosher CP, Olsen S, Anastassiou CA. Associations between in vitro , in vivo and in silico cell classes in mouse primary visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.17.532851. [PMID: 37131710 PMCID: PMC10153154 DOI: 10.1101/2023.04.17.532851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The brain consists of many cell classes yet in vivo electrophysiology recordings are typically unable to identify and monitor their activity in the behaving animal. Here, we employed a systematic approach to link cellular, multi-modal in vitro properties from experiments with in vivo recorded units via computational modeling and optotagging experiments. We found two one-channel and six multi-channel clusters in mouse visual cortex with distinct in vivo properties in terms of activity, cortical depth, and behavior. We used biophysical models to map the two one- and the six multi-channel clusters to specific in vitro classes with unique morphology, excitability and conductance properties that explain their distinct extracellular signatures and functional characteristics. These concepts were tested in ground-truth optotagging experiments with two inhibitory classes unveiling distinct in vivo properties. This multi-modal approach presents a powerful way to separate in vivo clusters and infer their cellular properties from first principles.
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Affiliation(s)
- Yina Wei
- Zhejiang Lab, Hangzhou 311100, China
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Anirban Nandi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Xiaoxuan Jia
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- School of Life Sciences, Tsinghua University, Beijing, 100084, China, IDG/McGovern Institute for Brain Research at Tsinghua University, Beijing, 100084, China
| | | | | | - Soo Yeun Lee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Anatoly Buchin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Cajal Neuroscience Inc, Seattle, WA 98102, USA
| | - Werner Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL) Campus Biotech, Geneva 1202, Switzerland
| | - Clayton P. Mosher
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Shawn Olsen
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Costas A. Anastassiou
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Lead contact
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22
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Rankin G, Chirila AM, Emanuel AJ, Zhang Z, Woolf CJ, Drugowitsch J, Ginty DD. Nerve injury disrupts temporal processing in the spinal cord dorsal horn through alterations in PV + interneurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.20.533541. [PMID: 36993199 PMCID: PMC10055222 DOI: 10.1101/2023.03.20.533541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
How mechanical allodynia following nerve injury is encoded in patterns of neural activity in the spinal cord dorsal horn (DH) is not known. We addressed this using the spared nerve injury model of neuropathic pain and in vivo electrophysiological recordings. Surprisingly, despite dramatic behavioral over-reactivity to mechanical stimuli following nerve injury, an overall increase in sensitivity or reactivity of DH neurons was not observed. We did, however, observe a marked decrease in correlated neural firing patterns, including the synchrony of mechanical stimulus-evoked firing, across the DH. Alterations in DH temporal firing patterns were recapitulated by silencing DH parvalbumin + (PV + ) inhibitory interneurons, previously implicated in mechanical allodynia, as were allodynic pain-like behaviors in mice. These findings reveal decorrelated DH network activity, driven by alterations in PV + interneurons, as a prominent feature of neuropathic pain, and suggest that restoration of proper temporal activity is a potential treatment for chronic neuropathic pain.
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23
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Tracking neural activity from the same cells during the entire adult life of mice. Nat Neurosci 2023; 26:696-710. [PMID: 36804648 DOI: 10.1038/s41593-023-01267-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 01/19/2023] [Indexed: 02/22/2023]
Abstract
Stably recording the electrical activity of the same neurons over the adult life of an animal is important to neuroscience research and biomedical applications. Current implantable devices cannot provide stable recording on this timescale. Here, we introduce a method to precisely implant electronics with an open, unfolded mesh structure across multiple brain regions in the mouse. The open mesh structure forms a stable interwoven structure with the neural network, preventing probe drifting and showing no immune response and neuron loss during the year-long implantation. Rigorous statistical analysis, visual stimulus-dependent measurement and unbiased, machine-learning-based analysis demonstrated that single-unit action potentials have been recorded from the same neurons of behaving mice in a very long-term stable manner. Leveraging this stable structure, we demonstrated that the same neurons can be recorded over the entire adult life of the mouse, revealing the aging-associated evolution of single-neuron activities.
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24
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Liu Y, Xu S, Yang Y, Zhang K, He E, Liang W, Luo J, Wu Y, Cai X. Nanomaterial-based microelectrode arrays for in vitro bidirectional brain-computer interfaces: a review. MICROSYSTEMS & NANOENGINEERING 2023; 9:13. [PMID: 36726940 PMCID: PMC9884667 DOI: 10.1038/s41378-022-00479-8] [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: 05/17/2022] [Revised: 10/04/2022] [Accepted: 10/21/2022] [Indexed: 06/18/2023]
Abstract
A bidirectional in vitro brain-computer interface (BCI) directly connects isolated brain cells with the surrounding environment, reads neural signals and inputs modulatory instructions. As a noninvasive BCI, it has clear advantages in understanding and exploiting advanced brain function due to the simplified structure and high controllability of ex vivo neural networks. However, the core of ex vivo BCIs, microelectrode arrays (MEAs), urgently need improvements in the strength of signal detection, precision of neural modulation and biocompatibility. Notably, nanomaterial-based MEAs cater to all the requirements by converging the multilevel neural signals and simultaneously applying stimuli at an excellent spatiotemporal resolution, as well as supporting long-term cultivation of neurons. This is enabled by the advantageous electrochemical characteristics of nanomaterials, such as their active atomic reactivity and outstanding charge conduction efficiency, improving the performance of MEAs. Here, we review the fabrication of nanomaterial-based MEAs applied to bidirectional in vitro BCIs from an interdisciplinary perspective. We also consider the decoding and coding of neural activity through the interface and highlight the various usages of MEAs coupled with the dissociated neural cultures to benefit future developments of BCIs.
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Affiliation(s)
- Yaoyao Liu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049 PR China
| | - Shihong Xu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049 PR China
| | - Yan Yang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049 PR China
| | - Kui Zhang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049 PR China
| | - Enhui He
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049 PR China
| | - Wei Liang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049 PR China
| | - Jinping Luo
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049 PR China
| | - Yirong Wu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049 PR China
| | - Xinxia Cai
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049 PR China
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25
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Tanabe S, Fu J, Cang J. Strong tuning for stereoscopic depth indicates orientation-specific recurrent circuitry in tree shrew V1. Curr Biol 2022; 32:5274-5284.e6. [PMID: 36417902 PMCID: PMC9772061 DOI: 10.1016/j.cub.2022.10.063] [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: 08/10/2022] [Revised: 09/23/2022] [Accepted: 10/27/2022] [Indexed: 11/23/2022]
Abstract
Neurons in the primary visual cortex (V1) are tuned to specific disparities between the two retinal images, which form the neural substrate for stereoscopic vision. We show that V1 neurons in tree shrews, but not in mice, display highly selective responses to narrow ranges of disparity in random-dot stereograms. Surprisingly, V1 neurons in both species show similarly strong tuning to gratings of varying interocular phase differences. This stimulus-dependent dissociation of disparity tuning can be explained by a network model that combines both feedforward and recurrent connections. The features of the model connections are supported by cortical organizations specific to each species. We validate this model by identifying putative inhibitory neurons and confirming their predicted disparity tuning in both species. Together, our studies establish a foundation for using tree shrews in studying binocular vision and raise an exciting possibility of how cortical columns could be uniquely important in computing stereoscopic depth.
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Affiliation(s)
- Seiji Tanabe
- Department of Psychology, University of Virginia, Charlottesville, VA 22904, USA.
| | - Jieming Fu
- Department of Biology, University of Virginia, Charlottesville, VA 22904, USA; Neuroscience Graduate Program, University of Virginia, Charlottesville, VA 22904, USA
| | - Jianhua Cang
- Department of Psychology, University of Virginia, Charlottesville, VA 22904, USA; Department of Biology, University of Virginia, Charlottesville, VA 22904, USA.
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26
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Schröter M, Wang C, Terrigno M, Hornauer P, Huang Z, Jagasia R, Hierlemann A. Functional imaging of brain organoids using high-density microelectrode arrays. MRS BULLETIN 2022; 47:530-544. [PMID: 36120104 PMCID: PMC9474390 DOI: 10.1557/s43577-022-00282-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/02/2022] [Indexed: 05/31/2023]
Abstract
ABSTRACT Studies have provided evidence that human cerebral organoids (hCOs) recapitulate fundamental milestones of early brain development, but many important questions regarding their functionality and electrophysiological properties persist. High-density microelectrode arrays (HD-MEAs) represent an attractive analysis platform to perform functional studies of neuronal networks at the cellular and network scale. Here, we use HD-MEAs to derive large-scale electrophysiological recordings from sliced hCOs. We record the activity of hCO slices over several weeks and probe observed neuronal dynamics pharmacologically. Moreover, we present results on how the obtained recordings can be spike-sorted and subsequently studied across scales. For example, we show how to track single neurons across several days on the HD-MEA and how to infer axonal action potential velocities. We also infer putative functional connectivity from hCO recordings. The introduced methodology will contribute to a better understanding of developing neuronal networks in brain organoids and provide new means for their functional characterization. IMPACT STATEMENT Human cerebral organoids (hCOs) represent an attractive in vitro model system to study key physiological mechanisms underlying early neuronal network formation in tissue with healthy or disease-related genetic backgrounds. Despite remarkable advances in the generation of brain organoids, knowledge on the functionality of their neuronal circuits is still scarce. Here, we used complementary metal-oxide-semiconductor (CMOS)-based high-density microelectrode arrays (HD-MEAs) to perform large-scale recordings from sliced hCOs over several weeks and quantified their activity across scales. Using single-cell and network metrics, we were able to probe aspects of hCO neurophysiology that are more difficult to obtain with other techniques, such as patch clamping (lower yield) and calcium imaging (lower temporal resolution). These metrics included, for example, extracellular action potential (AP) waveform features and axonal AP velocity at the cellular level, as well as functional connectivity at the network level. Analysis was enabled by the large sensing area and the high spatiotemporal resolution provided by HD-MEAs, which allowed recordings from hundreds of neurons and spike sorting of their activity. Our results demonstrate that HD-MEAs provide a multi-purpose platform for the functional characterization of hCOs, which will be key in improving our understanding of this model system and assessing its relevance for translational research. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1557/s43577-022-00282-w.
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Affiliation(s)
- Manuel Schröter
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Congwei Wang
- NRD, F. Hoffmann-La Roche Ltd., Roche Innovation Center Basel, Basel, Switzerland
| | - Marco Terrigno
- NRD, F. Hoffmann-La Roche Ltd., Roche Innovation Center Basel, Basel, Switzerland
| | - Philipp Hornauer
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Ziqiang Huang
- EMBL Imaging Centre, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Ravi Jagasia
- NRD, F. Hoffmann-La Roche Ltd., Roche Innovation Center Basel, Basel, Switzerland
| | - Andreas Hierlemann
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
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27
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Jia X, Siegle JH, Durand S, Heller G, Ramirez TK, Koch C, Olsen SR. Multi-regional module-based signal transmission in mouse visual cortex. Neuron 2022; 110:1585-1598.e9. [PMID: 35143752 DOI: 10.1016/j.neuron.2022.01.027] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 11/20/2021] [Accepted: 01/22/2022] [Indexed: 11/28/2022]
Abstract
The visual cortex is hierarchically organized, yet the presence of extensive recurrent and parallel pathways make it challenging to decipher how signals flow between neuronal populations. Here, we tracked the flow of spiking activity recorded from six interconnected levels of the mouse visual hierarchy. By analyzing leading and lagging spike-timing relationships among pairs of simultaneously recorded neurons, we created a cellular-scale directed network graph. Using a module-detection algorithm to cluster neurons based on shared connectivity patterns, we uncovered two multi-regional communication modules distributed across the hierarchy. The direction of signal flow both between and within these modules, differences in layer and area distributions, and distinct temporal dynamics suggest that one module transmits feedforward sensory signals, whereas the other integrates inputs for recurrent processing. These results suggest that multi-regional functional modules may be a fundamental feature of organization beyond cortical areas that supports signal propagation across hierarchical recurrent networks.
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28
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Paulk AC, Kfir Y, Khanna AR, Mustroph ML, Trautmann EM, Soper DJ, Stavisky SD, Welkenhuysen M, Dutta B, Shenoy KV, Hochberg LR, Richardson RM, Williams ZM, Cash SS. Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex. Nat Neurosci 2022; 25:252-263. [PMID: 35102333 DOI: 10.1038/s41593-021-00997-0] [Citation(s) in RCA: 70] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 12/07/2021] [Indexed: 12/20/2022]
Abstract
Recent advances in multi-electrode array technology have made it possible to monitor large neuronal ensembles at cellular resolution in animal models. In humans, however, current approaches restrict recordings to a few neurons per penetrating electrode or combine the signals of thousands of neurons in local field potential (LFP) recordings. Here we describe a new probe variant and set of techniques that enable simultaneous recording from over 200 well-isolated cortical single units in human participants during intraoperative neurosurgical procedures using silicon Neuropixels probes. We characterized a diversity of extracellular waveforms with eight separable single-unit classes, with differing firing rates, locations along the length of the electrode array, waveform spatial spread and modulation by LFP events such as inter-ictal discharges and burst suppression. Although some challenges remain in creating a turnkey recording system, high-density silicon arrays provide a path for studying human-specific cognitive processes and their dysfunction at unprecedented spatiotemporal resolution.
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Affiliation(s)
- 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.
| | - Yoav Kfir
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Arjun R 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 City, NY, USA
- Zuckerman Institute, Columbia University, New York City, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University Medical Center, New York City, NY, USA
- Department of Electrical Engineering, 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
- Columbia University, New York City, NY, USA
| | - Dan J Soper
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Sergey D Stavisky
- 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 Neurological Surgery, University of California at Davis, Davis, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | | | | | - Krishna V Shenoy
- Department of Electrical Engineering, 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 Neurosurgery, 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.
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29
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Chou N, Shin H, Kim K, Chae U, Jang M, Jeong U, Hwang K, Yi B, Lee SE, Woo J, Cho Y, Lee C, Baker BJ, Oh S, Nam M, Choi N, Cho I. A Multimodal Multi-Shank Fluorescence Neural Probe for Cell-Type-Specific Electrophysiology in Multiple Regions across a Neural Circuit. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2103564. [PMID: 34796701 PMCID: PMC8805556 DOI: 10.1002/advs.202103564] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/25/2021] [Indexed: 05/27/2023]
Abstract
Cell-type-specific, activity-dependent electrophysiology can allow in-depth analysis of functional connectivity inside complex neural circuits composed of various cell types. To date, optics-based fluorescence recording devices enable monitoring cell-type-specific activities. However, the monitoring is typically limited to a single brain region, and the temporal resolution is significantly low. Herein, a multimodal multi-shank fluorescence neural probe that allows cell-type-specific electrophysiology from multiple deep-brain regions at a high spatiotemporal resolution is presented. A photodiode and an electrode-array pair are monolithically integrated on each tip of a minimal-form-factor silicon device. Both fluorescence and electrical signals are successfully measured simultaneously in GCaMP6f expressing mice, and the cell type from sorted neural spikes is identified. The probe's capability of combined electro-optical recordings for cell-type-specific electrophysiology at multiple brain regions within a neural circuit is demonstrated. The new experimental paradigm to enable the precise investigation of functional connectivity inside and across complex neural circuits composed of various cell types is expected.
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Affiliation(s)
- Namsun Chou
- Center for BioMicrosystems, Brain Science InstituteKorea Institute of Science and Technology5, Hwarang‐ro 14‐gil, Seongbuk‐guSeoul02792Republic of Korea
| | - Hyogeun Shin
- Center for BioMicrosystems, Brain Science InstituteKorea Institute of Science and Technology5, Hwarang‐ro 14‐gil, Seongbuk‐guSeoul02792Republic of Korea
| | - Kanghwan Kim
- Center for BioMicrosystems, Brain Science InstituteKorea Institute of Science and Technology5, Hwarang‐ro 14‐gil, Seongbuk‐guSeoul02792Republic of Korea
| | - Uikyu Chae
- Center for BioMicrosystems, Brain Science InstituteKorea Institute of Science and Technology5, Hwarang‐ro 14‐gil, Seongbuk‐guSeoul02792Republic of Korea
- School of Electrical EngineeringKorea University145 Anam‐ro, Seongbuk‐guSeoul02841Republic of Korea
| | - Minsu Jang
- Center for BioMicrosystems, Brain Science InstituteKorea Institute of Science and Technology5, Hwarang‐ro 14‐gil, Seongbuk‐guSeoul02792Republic of Korea
| | - Ui‐Jin Jeong
- Center for BioMicrosystems, Brain Science InstituteKorea Institute of Science and Technology5, Hwarang‐ro 14‐gil, Seongbuk‐guSeoul02792Republic of Korea
- School of Electrical EngineeringKorea University145 Anam‐ro, Seongbuk‐guSeoul02841Republic of Korea
| | - Kyeong‐Seob Hwang
- Center for BioMicrosystems, Brain Science InstituteKorea Institute of Science and Technology5, Hwarang‐ro 14‐gil, Seongbuk‐guSeoul02792Republic of Korea
- School of Mechanical EngineeringYonsei University50 Yonsei‐ro, Seodaemun‐guSeoul03722Republic of Korea
| | - Bumjun Yi
- Center for Functional ConnectomicsBrain Science InstituteKorea Institute of Science and Technology5, Hwarang‐ro 14‐gil, Seongbuk‐guSeoul02792Republic of Korea
| | - Seung Eun Lee
- Virus Facility, Research Animal Resource CenterBrain Science InstituteKorea Institute of Science and Technology5, Hwarang‐ro 14‐gil, Seongbuk‐guSeoul02792Republic of Korea
| | - Jiwan Woo
- Center for Neuroscience, Brain Science InstituteKorea Institute of Science and Technology5, Hwarang‐ro 14‐gil, Seongbuk‐guSeoul02792Republic of Korea
| | - Yakdol Cho
- Center for Neuroscience, Brain Science InstituteKorea Institute of Science and Technology5, Hwarang‐ro 14‐gil, Seongbuk‐guSeoul02792Republic of Korea
| | - Changhyuk Lee
- Center for BioMicrosystems, Brain Science InstituteKorea Institute of Science and Technology5, Hwarang‐ro 14‐gil, Seongbuk‐guSeoul02792Republic of Korea
| | - Bradley J. Baker
- Center for Functional ConnectomicsBrain Science InstituteKorea Institute of Science and Technology5, Hwarang‐ro 14‐gil, Seongbuk‐guSeoul02792Republic of Korea
| | - Soo‐Jin Oh
- Center for Neuroscience, Brain Science InstituteKorea Institute of Science and Technology5, Hwarang‐ro 14‐gil, Seongbuk‐guSeoul02792Republic of Korea
| | - Min‐Ho Nam
- Center for Neuroscience, Brain Science InstituteKorea Institute of Science and Technology5, Hwarang‐ro 14‐gil, Seongbuk‐guSeoul02792Republic of Korea
| | - Nakwon Choi
- Center for BioMicrosystems, Brain Science InstituteKorea Institute of Science and Technology5, Hwarang‐ro 14‐gil, Seongbuk‐guSeoul02792Republic of Korea
- KU‐KIST Graduate School of Converging Science and TechnologyKorea University145 Anam‐ro, Seongbuk‐guSeoul02841Republic of Korea
- Division of Bio‐Medical Science and Technology, KIST SchoolKorea University of Science and Technology5, Hwarang‐ro 14‐gil, Seongbuk‐guSeoul02792Republic of Korea
| | - Il‐Joo Cho
- Center for BioMicrosystems, Brain Science InstituteKorea Institute of Science and Technology5, Hwarang‐ro 14‐gil, Seongbuk‐guSeoul02792Republic of Korea
- School of Electrical and Electronics EngineeringYonsei University50 Yonsei‐ro, Seodaemun‐guSeoul03722Republic of Korea
- Yonsei‐KIST Convergence Research InstituteYonsei University50 Yonsei‐ro, Seodaemun‐guSeoul03722Republic of Korea
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30
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Petersen PC, Siegle JH, Steinmetz NA, Mahallati S, Buzsáki G. CellExplorer: A framework for visualizing and characterizing single neurons. Neuron 2021; 109:3594-3608.e2. [PMID: 34592168 DOI: 10.1016/j.neuron.2021.09.002] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 07/19/2021] [Accepted: 08/30/2021] [Indexed: 01/28/2023]
Abstract
The large diversity of neuron types provides the means by which cortical circuits perform complex operations. Neuron can be described by biophysical and molecular characteristics, afferent inputs, and neuron targets. To quantify, visualize, and standardize those features, we developed the open-source, MATLAB-based framework CellExplorer. It consists of three components: a processing module, a flexible data structure, and a powerful graphical interface. The processing module calculates standardized physiological metrics, performs neuron-type classification, finds putative monosynaptic connections, and saves them to a standardized, yet flexible, machine-readable format. The graphical interface makes it possible to explore the computed features at the speed of a mouse click. The framework allows users to process, curate, and relate their data to a growing public collection of neurons. CellExplorer can link genetically identified cell types to physiological properties of neurons collected across laboratories and potentially lead to interlaboratory standards of single-cell metrics.
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Affiliation(s)
- Peter C Petersen
- Neuroscience Institute, Langone Medical Center, New York University, New York, NY 10016, USA.
| | - Joshua H Siegle
- MindScope Program, Allen Institute, 615 Westlake Avenue North, Seattle, WA 98109, USA
| | - Nicholas A Steinmetz
- Department of Biological Structure, University of Washington, Seattle, WA 98195, USA
| | - Sara Mahallati
- Institute of Biomedical Engineering, Krembil Research Institute, University of Toronto, Toronto, ON M5T 1M8, Canada
| | - György Buzsáki
- Neuroscience Institute, Langone Medical Center, New York University, New York, NY 10016, USA; Department of Neurology, Langone Medical Center, New York University, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA.
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31
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Lee EK, Balasubramanian H, Tsolias A, Anakwe SU, Medalla M, Shenoy KV, Chandrasekaran C. Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex. eLife 2021; 10:e67490. [PMID: 34355695 PMCID: PMC8452311 DOI: 10.7554/elife.67490] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 08/04/2021] [Indexed: 11/13/2022] Open
Abstract
Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only a small amount of variation. Here, we develop a new method (WaveMAP) that combines non-linear dimensionality reduction with graph clustering to identify putative cell types. We apply WaveMAP to extracellular waveforms recorded from dorsal premotor cortex of macaque monkeys performing a decision-making task. Using WaveMAP, we robustly establish eight waveform clusters and show that these clusters recapitulate previously identified narrow- and broad-spiking types while revealing previously unknown diversity within these subtypes. The eight clusters exhibited distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics. Such insights were weaker when using feature-based approaches. WaveMAP therefore provides a more nuanced understanding of the dynamics of cell types in cortical circuits.
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Affiliation(s)
- Eric Kenji Lee
- Psychological and Brain Sciences, Boston UniversityBostonUnited States
| | - Hymavathy Balasubramanian
- Bernstein Center for Computational Neuroscience, Bernstein Center for Computational NeuroscienceBerlinGermany
| | - Alexandra Tsolias
- Department of Anatomy and Neurobiology, Boston UniversityBostonUnited States
| | | | - Maria Medalla
- Department of Anatomy and Neurobiology, Boston UniversityBostonUnited States
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford UniversityStanfordUnited States
- Department of Bioengineering, Stanford UniversityStanfordUnited States
- Department of Neurobiology, Stanford UniversityStanfordUnited States
- Wu Tsai Neurosciences Institute, Stanford UniversityStanfordUnited States
- Bio-X Institute, Stanford UniversityStanfordUnited States
- Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
| | - Chandramouli Chandrasekaran
- Psychological and Brain Sciences, Boston UniversityBostonUnited States
- Department of Anatomy and Neurobiology, Boston UniversityBostonUnited States
- Center for Systems Neuroscience, Boston UniversityBostonUnited States
- Department of Biomedical Engineering, Boston UniversityBostonUnited States
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32
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Dvorak D, Chung A, Park EH, Fenton AA. Dentate spikes and external control of hippocampal function. Cell Rep 2021; 36:109497. [PMID: 34348165 PMCID: PMC8369486 DOI: 10.1016/j.celrep.2021.109497] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 06/04/2021] [Accepted: 07/14/2021] [Indexed: 11/11/2022] Open
Abstract
Mouse hippocampus CA1 place-cell discharge typically encodes current location, but during slow gamma dominance (SGdom), when SG oscillations (30-50 Hz) dominate mid-frequency gamma oscillations (70-90 Hz) in CA1 local field potentials, CA1 discharge switches to represent distant recollected locations. We report that dentate spike type 2 (DSM) events initiated by medial entorhinal cortex II (MECII)→ dentate gyrus (DG) inputs promote SGdom and change excitation-inhibition coordinated discharge in DG, CA3, and CA1, whereas type 1 (DSL) events initiated by lateral entorhinal cortex II (LECII)→DG inputs do not. Just before SGdom, LECII-originating SG oscillations in DG and CA3-originating SG oscillations in CA1 phase and frequency synchronize at the DSM peak when discharge within DG and CA3 increases to promote excitation-inhibition cofiring within and across the DG→CA3→CA1 pathway. This optimizes discharge for the 5-10 ms DG-to-CA1 neuro-transmission that SGdom initiates. DSM properties identify extrahippocampal control of SGdom and a cortico-hippocampal mechanism that switches between memory-related modes of information processing.
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Affiliation(s)
- Dino Dvorak
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - Ain Chung
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - Eun Hye Park
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - André Antonio Fenton
- Center for Neural Science, New York University, New York, NY 10003, USA; Neuroscience Institute at the NYU Langone Medical Center, New York, NY 10003, USA.
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33
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Siegle JH, Ledochowitsch P, Jia X, Millman DJ, Ocker GK, Caldejon S, Casal L, Cho A, Denman DJ, Durand S, Groblewski PA, Heller G, Kato I, Kivikas S, Lecoq J, Nayan C, Ngo K, Nicovich PR, North K, Ramirez TK, Swapp J, Waughman X, Williford A, Olsen SR, Koch C, Buice MA, de Vries SEJ. Reconciling functional differences in populations of neurons recorded with two-photon imaging and electrophysiology. eLife 2021; 10:e69068. [PMID: 34270411 PMCID: PMC8285106 DOI: 10.7554/elife.69068] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 07/02/2021] [Indexed: 11/20/2022] Open
Abstract
Extracellular electrophysiology and two-photon calcium imaging are widely used methods for measuring physiological activity with single-cell resolution across large populations of cortical neurons. While each of these two modalities has distinct advantages and disadvantages, neither provides complete, unbiased information about the underlying neural population. Here, we compare evoked responses in visual cortex recorded in awake mice under highly standardized conditions using either imaging of genetically expressed GCaMP6f or electrophysiology with silicon probes. Across all stimulus conditions tested, we observe a larger fraction of responsive neurons in electrophysiology and higher stimulus selectivity in calcium imaging, which was partially reconciled by applying a spikes-to-calcium forward model to the electrophysiology data. However, the forward model could only reconcile differences in responsiveness when restricted to neurons with low contamination and an event rate above a minimum threshold. This work established how the biases of these two modalities impact functional metrics that are fundamental for characterizing sensory-evoked responses.
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Affiliation(s)
| | | | - Xiaoxuan Jia
- MindScope Program, Allen InstituteSeattleUnited States
| | | | | | | | - Linzy Casal
- MindScope Program, Allen InstituteSeattleUnited States
| | - Andy Cho
- MindScope Program, Allen InstituteSeattleUnited States
| | - Daniel J Denman
- Allen Institute for Brain Science, Allen InstituteSeattleUnited States
| | | | | | - Gregg Heller
- MindScope Program, Allen InstituteSeattleUnited States
| | - India Kato
- MindScope Program, Allen InstituteSeattleUnited States
| | - Sara Kivikas
- MindScope Program, Allen InstituteSeattleUnited States
| | - Jérôme Lecoq
- MindScope Program, Allen InstituteSeattleUnited States
| | - Chelsea Nayan
- MindScope Program, Allen InstituteSeattleUnited States
| | - Kiet Ngo
- Allen Institute for Brain Science, Allen InstituteSeattleUnited States
| | - Philip R Nicovich
- Allen Institute for Brain Science, Allen InstituteSeattleUnited States
| | - Kat North
- MindScope Program, Allen InstituteSeattleUnited States
| | | | - Jackie Swapp
- MindScope Program, Allen InstituteSeattleUnited States
| | - Xana Waughman
- MindScope Program, Allen InstituteSeattleUnited States
| | - Ali Williford
- MindScope Program, Allen InstituteSeattleUnited States
| | - Shawn R Olsen
- MindScope Program, Allen InstituteSeattleUnited States
| | - Christof Koch
- MindScope Program, Allen InstituteSeattleUnited States
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34
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Horváth C, Tóth LF, Ulbert I, Fiáth R. Dataset of cortical activity recorded with high spatial resolution from anesthetized rats. Sci Data 2021; 8:180. [PMID: 34267214 PMCID: PMC8282648 DOI: 10.1038/s41597-021-00970-3] [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: 03/23/2021] [Accepted: 06/08/2021] [Indexed: 11/09/2022] Open
Abstract
Publicly available neural recordings obtained with high spatial resolution are scarce. Here, we present an electrophysiological dataset recorded from the neocortex of twenty rats anesthetized with ketamine/xylazine. The wideband, spontaneous recordings were acquired with a single-shank silicon-based probe having 128 densely-packed recording sites arranged in a 32 × 4 array. The dataset contains the activity of a total of 7126 sorted single units extracted from all layers of the cortex. Here, we share raw neural recordings, as well as spike times, extracellular spike waveforms and several properties of units packaged in a standardized electrophysiological data format. For technical validation of our dataset, we provide the distributions of derived single unit properties along with various spike sorting quality metrics. This large collection of in vivo data enables the investigation of the high-resolution electrical footprint of cortical neurons which in turn may aid their electrophysiology-based classification. Furthermore, the dataset might be used to study the laminar-specific neuronal activity during slow oscillation, a brain rhythm strongly involved in neural mechanisms underlying memory consolidation and sleep.
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Affiliation(s)
- Csaba Horváth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Eötvös Loránd Research Network, Budapest, Hungary
- School of Ph.D. Studies, Semmelweis University, Budapest, Hungary
| | - Lili Fanni Tóth
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - István Ulbert
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Eötvös Loránd Research Network, Budapest, Hungary.
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary.
| | - Richárd Fiáth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Eötvös Loránd Research Network, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
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35
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Paulk AC, Yang JC, Cleary DR, Soper DJ, Halgren M, O’Donnell AR, Lee SH, Ganji M, Ro YG, Oh H, Hossain L, Lee J, Tchoe Y, Rogers N, Kiliç K, Ryu SB, Lee SW, Hermiz J, Gilja V, Ulbert I, Fabó D, Thesen T, Doyle WK, Devinsky O, Madsen JR, Schomer DL, Eskandar EN, Lee JW, Maus D, Devor A, Fried SI, Jones PS, Nahed BV, Ben-Haim S, Bick SK, Richardson RM, Raslan AM, Siler DA, Cahill DP, Williams ZM, Cosgrove GR, Dayeh SA, Cash SS. Microscale Physiological Events on the Human Cortical Surface. Cereb Cortex 2021; 31:3678-3700. [PMID: 33749727 PMCID: PMC8258438 DOI: 10.1093/cercor/bhab040] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 02/05/2021] [Accepted: 02/07/2021] [Indexed: 01/14/2023] Open
Abstract
Despite ongoing advances in our understanding of local single-cellular and network-level activity of neuronal populations in the human brain, extraordinarily little is known about their "intermediate" microscale local circuit dynamics. Here, we utilized ultra-high-density microelectrode arrays and a rare opportunity to perform intracranial recordings across multiple cortical areas in human participants to discover three distinct classes of cortical activity that are not locked to ongoing natural brain rhythmic activity. The first included fast waveforms similar to extracellular single-unit activity. The other two types were discrete events with slower waveform dynamics and were found preferentially in upper cortical layers. These second and third types were also observed in rodents, nonhuman primates, and semi-chronic recordings from humans via laminar and Utah array microelectrodes. The rates of all three events were selectively modulated by auditory and electrical stimuli, pharmacological manipulation, and cold saline application and had small causal co-occurrences. These results suggest that the proper combination of high-resolution microelectrodes and analytic techniques can capture neuronal dynamics that lay between somatic action potentials and aggregate population activity. Understanding intermediate microscale dynamics in relation to single-cell and network dynamics may reveal important details about activity in the full cortical circuit.
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Affiliation(s)
- Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Jimmy C Yang
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Daniel R Cleary
- Departments of Neurosciences and Radiology, University of California San Diego, La Jolla, CA 92093, USA
- Department of Physics, University of California San Diego, La Jolla, CA 92093, USA
- Department of Neurosurgery, University of California San Diego, La Jolla, CA 92093, USA
| | - Daniel J Soper
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Mila Halgren
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Sang Heon Lee
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Mehran Ganji
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Yun Goo Ro
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Hongseok Oh
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Lorraine Hossain
- Materials Science and Engineering Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Jihwan Lee
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Youngbin Tchoe
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Nicholas Rogers
- Department of Physics, University of California San Diego, La Jolla, CA 92093, USA
| | - Kivilcim Kiliç
- Departments of Neurosciences and Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Sang Baek Ryu
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Seung Woo Lee
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - John Hermiz
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Vikash Gilja
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - István Ulbert
- Research Centre for Natural Sciences, Institute of Cognitive Neuroscience and Psychology, 1519 Budapest, Hungary
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, H-1444 Budapest, Hungary
| | - Daniel Fabó
- Epilepsy Centrum, National Institute of Clinical Neurosciences, 1145 Budapest, Hungary
| | - Thomas Thesen
- Department of Biomedical Sciences, University of Houston College of Medicine, Houston, TX 77204, USA
- Comprehensive Epilepsy Center, New York University School of Medicine, New York City, NY 10016, USA
| | - Werner K Doyle
- Comprehensive Epilepsy Center, New York University School of Medicine, New York City, NY 10016, USA
| | - Orrin Devinsky
- Comprehensive Epilepsy Center, New York University School of Medicine, New York City, NY 10016, USA
| | - Joseph R Madsen
- Departments of Neurosurgery, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Donald L Schomer
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Emad N Eskandar
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
- Albert Einstein College of Medicine, Montefiore Medical Center, Department of Neurosurgery, Bronx, NY 10467, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Douglas Maus
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Anna Devor
- Departments of Neurosciences and Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Shelley I Fried
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
- Boston VA Healthcare System, 150 South Huntington Avenue, Boston, MA 02130, USA
| | - Pamela S Jones
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Brian V Nahed
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Sharona Ben-Haim
- Department of Neurosurgery, University of California San Diego, La Jolla, CA 92093, USA
| | - Sarah K Bick
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Ahmed M Raslan
- Department of Neurological Surgery, Oregon Health and Science University, Portland, OR 97239, USA
| | - Dominic A Siler
- Department of Neurological Surgery, Oregon Health and Science University, Portland, OR 97239, USA
| | - Daniel P Cahill
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Ziv M Williams
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - G Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Shadi A Dayeh
- Department of Neurosurgery, University of California San Diego, La Jolla, CA 92093, USA
- Materials Science and Engineering Program, University of California San Diego, La Jolla, CA 92093, USA
- Department of Nanoengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
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36
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Trautmann EM, O'Shea DJ, Sun X, Marshel JH, Crow A, Hsueh B, Vesuna S, Cofer L, Bohner G, Allen W, Kauvar I, Quirin S, MacDougall M, Chen Y, Whitmire MP, Ramakrishnan C, Sahani M, Seidemann E, Ryu SI, Deisseroth K, Shenoy KV. Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface. Nat Commun 2021; 12:3689. [PMID: 34140486 PMCID: PMC8211867 DOI: 10.1038/s41467-021-23884-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 05/19/2021] [Indexed: 02/05/2023] Open
Abstract
Calcium imaging is a powerful tool for recording from large populations of neurons in vivo. Imaging in rhesus macaque motor cortex can enable the discovery of fundamental principles of motor cortical function and can inform the design of next generation brain-computer interfaces (BCIs). Surface two-photon imaging, however, cannot presently access somatic calcium signals of neurons from all layers of macaque motor cortex due to photon scattering. Here, we demonstrate an implant and imaging system capable of chronic, motion-stabilized two-photon imaging of neuronal calcium signals from macaques engaged in a motor task. By imaging apical dendrites, we achieved optical access to large populations of deep and superficial cortical neurons across dorsal premotor (PMd) and gyral primary motor (M1) cortices. Dendritic signals from individual neurons displayed tuning for different directions of arm movement. Combining several technical advances, we developed an optical BCI (oBCI) driven by these dendritic signalswhich successfully decoded movement direction online. By fusing two-photon functional imaging with CLARITY volumetric imaging, we verified that many imaged dendrites which contributed to oBCI decoding originated from layer 5 output neurons, including a putative Betz cell. This approach establishes new opportunities for studying motor control and designing BCIs via two photon imaging.
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Affiliation(s)
- Eric M Trautmann
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| | - Daniel J O'Shea
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| | - Xulu Sun
- Department of Biology, Stanford University, Stanford, CA, USA.
| | - James H Marshel
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Ailey Crow
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Brian Hsueh
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Sam Vesuna
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Lucas Cofer
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Gergő Bohner
- Gatsby Computational Neuroscience Unit, University College London, London, UK
| | - Will Allen
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Isaac Kauvar
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Sean Quirin
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | | | - Yuzhi Chen
- Center for Perceptual Systems, University of Texas, Austin, TX, USA
- Department of Psychology, University of Texas, Austin, TX, USA
- Department of Neuroscience, University of Texas, Austin, TX, USA
| | - Matthew P Whitmire
- Center for Perceptual Systems, University of Texas, Austin, TX, USA
- Department of Psychology, University of Texas, Austin, TX, USA
- Department of Neuroscience, University of Texas, Austin, TX, USA
| | | | - Maneesh Sahani
- Gatsby Computational Neuroscience Unit, University College London, London, UK
| | - Eyal Seidemann
- Center for Perceptual Systems, University of Texas, Austin, TX, USA
- Department of Psychology, University of Texas, Austin, TX, USA
- Department of Neuroscience, University of Texas, Austin, TX, USA
| | - Stephen I Ryu
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, USA
| | - Karl Deisseroth
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Department of Psychiatry and Behavioral Science, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
- Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA, USA.
| | - Krishna V Shenoy
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
- Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA, USA.
- Department of Neurobiology, Stanford University, Stanford, CA, USA.
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37
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Mosher CP, Wei Y, Kamiński J, Nandi A, Mamelak AN, Anastassiou CA, Rutishauser U. Cellular Classes in the Human Brain Revealed In Vivo by Heartbeat-Related Modulation of the Extracellular Action Potential Waveform. Cell Rep 2021; 30:3536-3551.e6. [PMID: 32160555 DOI: 10.1016/j.celrep.2020.02.027] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 12/23/2019] [Accepted: 02/05/2020] [Indexed: 01/01/2023] Open
Abstract
Determining cell types is critical for understanding neural circuits but remains elusive in the living human brain. Current approaches discriminate units into putative cell classes using features of the extracellular action potential (EAP); in absence of ground truth data, this remains a problematic procedure. We find that EAPs in deep structures of the brain exhibit robust and systematic variability during the cardiac cycle. These cardiac-related features refine neural classification. We use these features to link bio-realistic models generated from in vitro human whole-cell recordings of morphologically classified neurons to in vivo recordings. We differentiate aspiny inhibitory and spiny excitatory human hippocampal neurons and, in a second stage, demonstrate that cardiac-motion features reveal two types of spiny neurons with distinct intrinsic electrophysiological properties and phase-locking characteristics to endogenous oscillations. This multi-modal approach markedly improves cell classification in humans, offers interpretable cell classes, and is applicable to other brain areas and species.
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Affiliation(s)
- Clayton P Mosher
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Yina Wei
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jan Kamiński
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA
| | - Anirban Nandi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Adam N Mamelak
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Costas A Anastassiou
- Allen Institute for Brain Science, Seattle, WA 98109, USA; Division of Neurology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Center for Neural Science and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA.
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38
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Maier A, Tsuchiya N. Growing evidence for separate neural mechanisms for attention and consciousness. Atten Percept Psychophys 2021; 83:558-576. [PMID: 33034851 PMCID: PMC7886945 DOI: 10.3758/s13414-020-02146-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/08/2020] [Indexed: 11/08/2022]
Abstract
Our conscious experience of the world seems to go in lockstep with our attentional focus: We tend to see, hear, taste, and feel what we attend to, and vice versa. This tight coupling between attention and consciousness has given rise to the idea that these two phenomena are indivisible. In the late 1950s, the honoree of this special issue, Charles Eriksen, was among a small group of early pioneers that sought to investigate whether a transient increase in overall level of attention (alertness) in response to a noxious stimulus can be decoupled from conscious perception using experimental techniques. Recent years saw a similar debate regarding whether attention and consciousness are two dissociable processes. Initial evidence that attention and consciousness are two separate processes primarily rested on behavioral data. However, the past couple of years witnessed an explosion of studies aimed at testing this conjecture using neuroscientific techniques. Here we provide an overview of these and related empirical studies on the distinction between the neuronal correlates of attention and consciousness, and detail how advancements in theory and technology can bring about a more detailed understanding of the two. We argue that the most promising approach will combine ever-evolving neurophysiological and interventionist tools with quantitative, empirically testable theories of consciousness that are grounded in a mathematically formalized understanding of phenomenology.
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Affiliation(s)
- Alexander Maier
- Department of Psychology, Vanderbilt University, Nashville, TN, USA.
| | - Naotsugu Tsuchiya
- Turner Institute for Brain and Mental Health & School of Psychological Sciences, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Melbourne, VIC, Australia
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Suita, Osaka, 565-0871, Japan
- Advanced Telecommunications Research Computational Neuroscience Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan
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Siegle JH, Jia X, Durand S, Gale S, Bennett C, Graddis N, Heller G, Ramirez TK, Choi H, Luviano JA, Groblewski PA, Ahmed R, Arkhipov A, Bernard A, Billeh YN, Brown D, Buice MA, Cain N, Caldejon S, Casal L, Cho A, Chvilicek M, Cox TC, Dai K, Denman DJ, de Vries SEJ, Dietzman R, Esposito L, Farrell C, Feng D, Galbraith J, Garrett M, Gelfand EC, Hancock N, Harris JA, Howard R, Hu B, Hytnen R, Iyer R, Jessett E, Johnson K, Kato I, Kiggins J, Lambert S, Lecoq J, Ledochowitsch P, Lee JH, Leon A, Li Y, Liang E, Long F, Mace K, Melchior J, Millman D, Mollenkopf T, Nayan C, Ng L, Ngo K, Nguyen T, Nicovich PR, North K, Ocker GK, Ollerenshaw D, Oliver M, Pachitariu M, Perkins J, Reding M, Reid D, Robertson M, Ronellenfitch K, Seid S, Slaughterbeck C, Stoecklin M, Sullivan D, Sutton B, Swapp J, Thompson C, Turner K, Wakeman W, Whitesell JD, Williams D, Williford A, Young R, Zeng H, Naylor S, Phillips JW, Reid RC, Mihalas S, Olsen SR, Koch C. Survey of spiking in the mouse visual system reveals functional hierarchy. Nature 2021; 592:86-92. [PMID: 33473216 PMCID: PMC10399640 DOI: 10.1038/s41586-020-03171-x] [Citation(s) in RCA: 148] [Impact Index Per Article: 49.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 12/09/2020] [Indexed: 12/14/2022]
Abstract
The anatomy of the mammalian visual system, from the retina to the neocortex, is organized hierarchically1. However, direct observation of cellular-level functional interactions across this hierarchy is lacking due to the challenge of simultaneously recording activity across numerous regions. Here we describe a large, open dataset-part of the Allen Brain Observatory2-that surveys spiking from tens of thousands of units in six cortical and two thalamic regions in the brains of mice responding to a battery of visual stimuli. Using cross-correlation analysis, we reveal that the organization of inter-area functional connectivity during visual stimulation mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas3. We find that four classical hierarchical measures-response latency, receptive-field size, phase-locking to drifting gratings and response decay timescale-are all correlated with the hierarchy. Moreover, recordings obtained during a visual task reveal that the correlation between neural activity and behavioural choice also increases along the hierarchy. Our study provides a foundation for understanding coding and signal propagation across hierarchically organized cortical and thalamic visual areas.
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Affiliation(s)
| | - Xiaoxuan Jia
- Allen Institute for Brain Science, Seattle, WA, USA.
| | | | - Sam Gale
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Nile Graddis
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Hannah Choi
- Allen Institute for Brain Science, Seattle, WA, USA.,Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | | | | | | | | | - Amy Bernard
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Dillan Brown
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Nicolas Cain
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Linzy Casal
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Andrew Cho
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Timothy C Cox
- University of Missouri-Kansas City School of Dentistry, Kansas City, MO, USA
| | - Kael Dai
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Daniel J Denman
- Allen Institute for Brain Science, Seattle, WA, USA.,The University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | | | | | | | | | - David Feng
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | | | - Brian Hu
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ross Hytnen
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - India Kato
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Jerome Lecoq
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Arielle Leon
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Yang Li
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Fuhui Long
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kyla Mace
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kiet Ngo
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Kat North
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Jed Perkins
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - David Reid
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Sam Seid
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Ben Sutton
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jackie Swapp
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | | | - Rob Young
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Sarah Naylor
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Shawn R Olsen
- Allen Institute for Brain Science, Seattle, WA, USA.
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Ahn J, Phan HL, Cha S, Koo KI, Yoo Y, Goo YS. Synchrony of Spontaneous Burst Firing between Retinal Ganglion Cells Across Species. Exp Neurobiol 2020; 29:285-299. [PMID: 32921641 PMCID: PMC7492847 DOI: 10.5607/en20025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 08/27/2020] [Accepted: 08/31/2020] [Indexed: 01/16/2023] Open
Abstract
Neurons communicate with other neurons in response to environmental changes. Their goal is to transmit information to their targets reliably. A burst, which consists of multiple spikes within a short time interval, plays an essential role in enhancing the reliability of information transmission through synapses. In the visual system, retinal ganglion cells (RGCs), the output neurons of the retina, show bursting activity and transmit retinal information to the lateral geniculate neuron of the thalamus. In this study, to extend our interest to the population level, the burstings of multiple RGCs were simultaneously recorded using a multi-channel recording system. As the first step in network analysis, we focused on investigating the pairwise burst correlation between two RGCs. Furthermore, to assess if the population bursting is preserved across species, we compared the synchronized bursting of RGCs between marmoset monkey (callithrix jacchus), one species of the new world monkeys and mouse (C57BL/6J strain). First, monkey RGCs showed a larger number of spikes within a burst, while the inter-spike interval, burst duration, and inter-burst interval were smaller compared with mouse RGCs. Monkey RGCs showed a strong burst synchronization between RGCs, whereas mouse RGCs showed no correlated burst firing. Monkey RGC pairs showed significantly higher burst synchrony and mutual information than mouse RGC pairs did. Comprehensively, through this study, we emphasize that two species have a different bursting activity of RGCs and different burst synchronization suggesting two species have distinctive retinal processing.
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Affiliation(s)
- Jungryul Ahn
- Department of Physiology, Chungbuk National University School of Medicine, Cheongju 28644, Korea
| | - Huu Lam Phan
- Department of Biomedical Engineering, University of Ulsan, Ulsan 44610, Korea
| | - Seongkwang Cha
- Department of Physiology, Chungbuk National University School of Medicine, Cheongju 28644, Korea
| | - Kyo-In Koo
- Department of Biomedical Engineering, University of Ulsan, Ulsan 44610, Korea
| | - Yongseok Yoo
- Department of Electronics Engineering, Incheon National University, Incheon 22012, Korea
| | - Yong Sook Goo
- Department of Physiology, Chungbuk National University School of Medicine, Cheongju 28644, Korea
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Wickham J, Corna A, Schwarz N, Uysal B, Layer N, Honegger JB, Wuttke TV, Koch H, Zeck G. Human Cerebrospinal Fluid Induces Neuronal Excitability Changes in Resected Human Neocortical and Hippocampal Brain Slices. Front Neurosci 2020; 14:283. [PMID: 32372899 PMCID: PMC7186381 DOI: 10.3389/fnins.2020.00283] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Accepted: 03/12/2020] [Indexed: 12/22/2022] Open
Abstract
Human cerebrospinal fluid (hCSF) has proven advantageous over conventional medium for culturing both rodent and human brain tissue. In addition, increased activity and synchrony, closer to the dynamic states exclusively recorded in vivo, were reported in rodent slices and cell cultures switching from artificial cerebrospinal fluid (aCSF) to hCSF. This indicates that hCSF possesses properties that are not matched by the aCSF, which is generally used for most electrophysiological recordings. To evaluate the possible significance of using hCSF as an electrophysiological recording medium, also for human brain tissue, we compared the network and single-cell firing properties of human brain slice cultures during perfusion with hCSF and aCSF. For measuring the overall activity from a majority of neurons within neocortical and hippocampal human slices, we used a microelectrode array (MEA) recording technique with 252 electrodes covering an area of 3.2 × 3.2 mm2. A second CMOS-based MEA with 4225 sensors on a 2 × 2 mm2 area was used for detailed mapping of action potential waveforms and cell identification. We found that hCSF increased the number of active electrodes and neurons and the firing rate of the neurons in the slices and induced an increase in the numbers of single channel and population bursts. Interestingly, not only an increase in the overall activity in the slices was observed, but a reconfiguration of the network could also be detected with specific activation and inactivation of subpopulations of neuronal ensembles. In conclusion, hCSF is an important component to consider for future human brain slice studies, especially for experiments designed to mimic parts of physiology and disease observed in vivo.
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Affiliation(s)
- Jenny Wickham
- Neurophysics, Natural and Medical Sciences Institute, University of Tübingen, Reutlingen, Germany
| | - Andrea Corna
- Neurophysics, Natural and Medical Sciences Institute, University of Tübingen, Reutlingen, Germany
- Graduate School of Neural Information Processing/International Max Planck Research School, Tübingen, Germany
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
| | - Niklas Schwarz
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Betül Uysal
- Graduate School of Neural Information Processing/International Max Planck Research School, Tübingen, Germany
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Nikolas Layer
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | | | - Thomas V. Wuttke
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Department of Neurosurgery, University of Tübingen, Tübingen, Germany
| | - Henner Koch
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Department of Epileptology, Neurology, RWTH Aachen University, Aachen, Germany
| | - Günther Zeck
- Neurophysics, Natural and Medical Sciences Institute, University of Tübingen, Reutlingen, Germany
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