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Lee SY, Kozalakis K, Baftizadeh F, Campagnola L, Jarsky T, Koch C, Anastassiou CA. Cell-class-specific electric field entrainment of neural activity. Neuron 2024; 112:2614-2630.e5. [PMID: 38838670 PMCID: PMC11309920 DOI: 10.1016/j.neuron.2024.05.009] [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: 02/06/2023] [Revised: 12/14/2023] [Accepted: 05/08/2024] [Indexed: 06/07/2024]
Abstract
Electric fields affect the activity of neurons and brain circuits, yet how this happens at the cellular level remains enigmatic. Lack of understanding of how to stimulate the brain to promote or suppress specific activity significantly limits basic research and clinical applications. Here, we study how electric fields impact subthreshold and spiking properties of major cortical neuronal classes. We find that neurons in the rodent and human cortex exhibit strong, cell-class-dependent entrainment that depends on stimulation frequency. Excitatory pyramidal neurons, with their slower spike rate, entrain to both slow and fast electric fields, while inhibitory classes like Pvalb and Sst (with their fast spiking) predominantly phase-lock to fast fields. We show that this spike-field entrainment is the result of two effects: non-specific membrane polarization occurring across classes and class-specific excitability properties. Importantly, these properties are present across cortical areas and species. These findings allow for the design of selective and class-specific neuromodulation.
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Affiliation(s)
| | | | | | | | | | | | - 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; Center for Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
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2
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Johnsen KA, Cruzado NA, Menard ZC, Willats AA, Charles AS, Markowitz JE, Rozell CJ. Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.01.27.525963. [PMID: 39026717 PMCID: PMC11257437 DOI: 10.1101/2023.01.27.525963] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Systems neuroscience has experienced an explosion of new tools for reading and writing neural activity, enabling exciting new experiments such as all-optical or closed-loop control that effect powerful causal interventions. At the same time, improved computational models are capable of reproducing behavior and neural activity with increasing fidelity. Unfortunately, these advances have drastically increased the complexity of integrating different lines of research, resulting in the missed opportunities and untapped potential of suboptimal experiments. Experiment simulation can help bridge this gap, allowing model and experiment to better inform each other by providing a low-cost testbed for experiment design, model validation, and methods engineering. Specifically, this can be achieved by incorporating the simulation of the experimental interface into our models, but no existing tool integrates optogenetics, two-photon calcium imaging, electrode recording, and flexible closed-loop processing with neural population simulations. To address this need, we have developed Cleo: the Closed-Loop, Electrophysiology, and Optophysiology experiment simulation testbed. Cleo is a Python package enabling injection of recording and stimulation devices as well as closed-loop control with realistic latency into a Brian spiking neural network model. It is the only publicly available tool currently supporting two-photon and multi-opsin/wavelength optogenetics. To facilitate adoption and extension by the community, Cleo is open-source, modular, tested, and documented, and can export results to various data formats. Here we describe the design and features of Cleo, validate output of individual components and integrated experiments, and demonstrate its utility for advancing optogenetic techniques in prospective experiments using previously published systems neuroscience models.
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Affiliation(s)
- Kyle A. Johnsen
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | | | - Zachary C. Menard
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Adam A. Willats
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Adam S. Charles
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey E. Markowitz
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
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3
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Ramezani Z, André V, Khizroev S. Modeling the effect of magnetoelectric nanoparticles on neuronal electrical activity: An analog circuit approach. Biointerphases 2024; 19:031001. [PMID: 38738941 DOI: 10.1116/5.0199163] [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: 01/22/2024] [Accepted: 04/22/2024] [Indexed: 05/14/2024] Open
Abstract
This paper introduces a physical neuron model that incorporates magnetoelectric nanoparticles (MENPs) as an essential electrical circuit component to wirelessly control local neural activity. Availability of such a model is important as MENPs, due to their magnetoelectric effect, can wirelessly and noninvasively modulate neural activity, which, in turn, has implications for both finding cures for neurological diseases and creating a wireless noninvasive high-resolution brain-machine interface. When placed on a neuronal membrane, MENPs act as magnetic-field-controlled finite-size electric dipoles that generate local electric fields across the membrane in response to magnetic fields, thus allowing to controllably activate local ion channels and locally initiate an action potential. Herein, the neuronal electrical characteristic description is based on ion channel activation and inhibition mechanisms. A MENP-based memristive Hodgkin-Huxley circuit model is extracted by combining the Hodgkin-Huxley model and an equivalent circuit model for a single MENP. In this model, each MENP becomes an integral part of the neuron, thus enabling wireless local control of the neuron's electric circuit itself. Furthermore, the model is expanded to include multiple MENPs to describe collective effects in neural systems.
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Affiliation(s)
- Zeinab Ramezani
- Department of Electrical and Computer Engineering, College of Engineering, University of Miami, Miami, Florida 33146
| | - Victoria André
- Department of Biomedical Engineering, College of Engineering, University of Miami, Miami, Florida 33146
| | - Sakhrat Khizroev
- Department of Electrical and Computer Engineering, College of Engineering, University of Miami, Miami, Florida 33146
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4
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Pachitariu M, Sridhar S, Pennington J, Stringer C. Spike sorting with Kilosort4. Nat Methods 2024; 21:914-921. [PMID: 38589517 PMCID: PMC11093732 DOI: 10.1038/s41592-024-02232-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 03/01/2024] [Indexed: 04/10/2024]
Abstract
Spike sorting is the computational process of extracting the firing times of single neurons from recordings of local electrical fields. This is an important but hard problem in neuroscience, made complicated by the nonstationarity of the recordings and the dense overlap in electrical fields between nearby neurons. To address the spike-sorting problem, we have been openly developing the Kilosort framework. Here we describe the various algorithmic steps introduced in different versions of Kilosort. We also report the development of Kilosort4, a version with substantially improved performance due to clustering algorithms inspired by graph-based approaches. To test the performance of Kilosort, we developed a realistic simulation framework that uses densely sampled electrical fields from real experiments to generate nonstationary spike waveforms and realistic noise. We found that nearly all versions of Kilosort outperformed other algorithms on a variety of simulated conditions and that Kilosort4 performed best in all cases, correctly identifying even neurons with low amplitudes and small spatial extents in high drift conditions.
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Affiliation(s)
| | - Shashwat Sridhar
- HHMI, Ashburn, VA, USA
- Department of Ophthalmology, University Medical Center Göttingen, Göttingen, Germany
| | - Jacob Pennington
- HHMI, Ashburn, VA, USA
- Department of Mathematics, Washington State University, Vancouver, WA, USA
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5
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Dura-Bernal S, Griffith EY, Barczak A, O'Connell MN, McGinnis T, Moreira JVS, Schroeder CE, Lytton WW, Lakatos P, Neymotin SA. Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics. Cell Rep 2023; 42:113378. [PMID: 37925640 PMCID: PMC10727489 DOI: 10.1016/j.celrep.2023.113378] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 09/05/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023] Open
Abstract
We developed a detailed model of macaque auditory thalamocortical circuits, including primary auditory cortex (A1), medial geniculate body (MGB), and thalamic reticular nucleus, utilizing the NEURON simulator and NetPyNE tool. The A1 model simulates a cortical column with over 12,000 neurons and 25 million synapses, incorporating data on cell-type-specific neuron densities, morphology, and connectivity across six cortical layers. It is reciprocally connected to the MGB thalamus, which includes interneurons and core and matrix-layer-specific projections to A1. The model simulates multiscale measures, including physiological firing rates, local field potentials (LFPs), current source densities (CSDs), and electroencephalography (EEG) signals. Laminar CSD patterns, during spontaneous activity and in response to broadband noise stimulus trains, mirror experimental findings. Physiological oscillations emerge spontaneously across frequency bands comparable to those recorded in vivo. We elucidate population-specific contributions to observed oscillation events and relate them to firing and presynaptic input patterns. The model offers a quantitative theoretical framework to integrate and interpret experimental data and predict its underlying cellular and circuit mechanisms.
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Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Erica Y Griffith
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Annamaria Barczak
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Monica N O'Connell
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Tammy McGinnis
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Joao V S Moreira
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA
| | - Charles E Schroeder
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Departments of Psychiatry and Neurology, Columbia University Medical Center, New York, NY, USA
| | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Kings County Hospital Center, Brooklyn, NY, USA
| | - Peter Lakatos
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | - Samuel A Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department Psychiatry, NYU Grossman School of Medicine, New York, NY, USA.
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6
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Haufler D, Ito S, Koch C, Arkhipov A. Simulations of cortical networks using spatially extended conductance-based neuronal models. J Physiol 2023; 601:3123-3139. [PMID: 36567262 PMCID: PMC10290729 DOI: 10.1113/jp284030] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 12/19/2022] [Indexed: 12/27/2022] Open
Abstract
The Hodgkin-Huxley model of action potential generation and propagation, published in the Journal of Physiology in 1952, initiated the field of biophysically detailed computational modelling in neuroscience, which has expanded to encompass a variety of species and components of the nervous system. Here we review the developments in this area with a focus on efforts in the community towards modelling the mammalian neocortex using spatially extended conductance-based neuronal models. The Hodgkin-Huxley formalism and related foundational contributions, such as Rall's cable theory, remain widely used in these efforts to the current day. We argue that at present the field is undergoing a qualitative change due to new very rich datasets describing the composition, connectivity and functional activity of cortical circuits, which are being integrated systematically into large-scale network models. This trend, combined with the accelerating development of convenient software tools supporting such complex modelling projects, is giving rise to highly detailed models of the cortex that are extensively constrained by the data, enabling computational investigation of a multitude of questions about cortical structure and function.
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Affiliation(s)
| | - Shinya Ito
- Mindscope Program, Allen Institute, Seattle, 98109
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7
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Fietkiewicz C, McDougal RA, Corrales Marco D, Chiel HJ, Thomas PJ. Tutorial: using NEURON for neuromechanical simulations. Front Comput Neurosci 2023; 17:1143323. [PMID: 37583894 PMCID: PMC10424731 DOI: 10.3389/fncom.2023.1143323] [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: 01/12/2023] [Accepted: 06/20/2023] [Indexed: 08/17/2023] Open
Abstract
The dynamical properties of the brain and the dynamics of the body strongly influence one another. Their interaction generates complex adaptive behavior. While a wide variety of simulation tools exist for neural dynamics or biomechanics separately, there are few options for integrated brain-body modeling. Here, we provide a tutorial to demonstrate how the widely-used NEURON simulation platform can support integrated neuromechanical modeling. As a first step toward incorporating biomechanics into a NEURON simulation, we provide a framework for integrating inputs from a "periphery" and outputs to that periphery. In other words, "body" dynamics are driven in part by "brain" variables, such as voltages or firing rates, and "brain" dynamics are influenced by "body" variables through sensory feedback. To couple the "brain" and "body" components, we use NEURON's pointer construct to share information between "brain" and "body" modules. This approach allows separate specification of brain and body dynamics and code reuse. Though simple in concept, the use of pointers can be challenging due to a complicated syntax and several different programming options. In this paper, we present five different computational models, with increasing levels of complexity, to demonstrate the concepts of code modularity using pointers and the integration of neural and biomechanical modeling within NEURON. The models include: (1) a neuromuscular model of calcium dynamics and muscle force, (2) a neuromechanical, closed-loop model of a half-center oscillator coupled to a rudimentary motor system, (3) a closed-loop model of neural control for respiration, (4) a pedagogical model of a non-smooth "brain/body" system, and (5) a closed-loop model of feeding behavior in the sea hare Aplysia californica that incorporates biologically-motivated non-smooth dynamics. This tutorial illustrates how NEURON can be integrated with a broad range of neuromechanical models. Code available at https://github.com/fietkiewicz/PointerBuilder.
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Affiliation(s)
- Chris Fietkiewicz
- Department of Mathematics and Computer Science, Hobart and William Smith Colleges, Geneva, NY, United States
| | - Robert A. McDougal
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States
- Wu Tsai Institute, Yale University, New Haven, CT, United States
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States
- Section for Biomedical Informatics, Yale School of Medicine, New Haven, CT, United States
| | - David Corrales Marco
- Department of Mathematics and Computer Science, Hobart and William Smith Colleges, Geneva, NY, United States
| | - Hillel J. Chiel
- Department of Biology, Case Western Reserve University, Cleveland, OH, United States
- Department of Neurosciences, Case Western Reserve University, Cleveland, OH, United States
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Peter J. Thomas
- Department of Biology, Case Western Reserve University, Cleveland, OH, United States
- Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH, United States
- Department of Cognitive Science, Case Western Reserve University, Cleveland, OH, United States
- Department of Electrical, Control, and Systems Engineering, Case Western Reserve University, Cleveland, OH, United States
- Department of Data and Computer Science, Case Western Reserve University, Cleveland, OH, United States
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8
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Latimer B, Dopp D, Perumal MB, Sah P, Nair SS. A Novel Biophysical Model for the Generation of Sharp Wave Ripples in CA1 Hippocampus. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS. II, EXPRESS BRIEFS : A PUBLICATION OF THE IEEE CIRCUITS AND SYSTEMS SOCIETY 2023; 70:1784-1788. [PMID: 38045871 PMCID: PMC10688809 DOI: 10.1109/tcsii.2023.3262207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Synchronous activities among neurons in the brain generate emergent network oscillations such as the hippocampal Sharp-wave ripples (SPWRs) that facilitate information processing during memory formation. However, how neurons and circuits are functionally organized to generate oscillations remains unresolved. Biophysical models of neuronal networks can shed light on how thousands of neurons interact in intricate circuits to generate such emergent SPWR network events. Here we developed a large-scale biophysically realistic neural network model of CA1 hippocampus with functionally organized circuit modules containing distinct types of neurons. Model simulations reproduced synaptic, cellular and network aspects of physiological SPWRs. The model provided insights into the role of neuronal types and their microcircuit motifs in generating SPWRs in the CA1 region. The model also suggests experimentally testable predictions including the role of specific neuron types in the genesis of hippocampal SPWRs.
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Affiliation(s)
- Benjamin Latimer
- Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO 65211 USA
| | - Dan Dopp
- Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO 65211 USA
| | - Madhusoothanan B Perumal
- Eccles Institute of Neuroscience, John Curtin School of Medical Research, Australian National University, Canberra 2601 AU
| | | | - Satish S Nair
- Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO 65211 USA
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9
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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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10
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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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11
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Glickert G, Latimer B, Sah P, Nair SS. Reverse engineering information processing in lateral amygdala during auditory tones. INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING : [PROCEEDINGS]. INTERNATIONAL IEEE EMBS CONFERENCE ON NEURAL ENGINEERING 2023; 2023:10.1109/ner52421.2023.10123856. [PMID: 37366393 PMCID: PMC10292606 DOI: 10.1109/ner52421.2023.10123856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
Learning in the mammalian lateral amygdala (LA) during auditory fear conditioning (tone - foot shock pairing), one form of associative learning, requires N-methyl-D-aspartate (NMDA) receptor-dependent plasticity. Despite this fact being known for more than two decades, the biophysical details related to signal flow and the involvement of the coincidence detector, NMDAR, in this learning, remain unclear. Here we use a 4000-neuron computational model of the LA (containing two types of pyramidal cells, types A and C, and two types of interneurons, fast spiking FSI and low-threshold spiking LTS) to reverse engineer changes in information flow in the amygdala that underpin such learning; with a specific focus on the role of the coincidence detector NMDAR. The model also included a Ca2s based learning rule for synaptic plasticity. The physiologically constrained model provides insights into the underlying mechanisms that implement habituation to the tone, including the role of NMDARs in generating network activity which engenders synaptic plasticity in specific afferent synapses. Specifically, model runs revealed that NMDARs in tone-FSI synapses were more important during the spontaneous state, although LTS cells also played a role. Training trails with tone only also suggested long term depression in tone-PN as well as tone-FSI synapses, providing possible hypothesis related to underlying mechanisms that might implement the phenomenon of habituation.
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12
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Cobb EAW, Petroccione MA, Scimemi A. NRN-EZ: an application to streamline biophysical modeling of synaptic integration using NEURON. Sci Rep 2023; 13:464. [PMID: 36627356 PMCID: PMC9832141 DOI: 10.1038/s41598-022-27302-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 12/29/2022] [Indexed: 01/12/2023] Open
Abstract
One of the fundamental goals in neuroscience is to determine how the brain processes information and ultimately controls the execution of complex behaviors. Over the past four decades, there has been a steady growth in our knowledge of the morphological and functional diversity of neurons, the building blocks of the brain. These cells clearly differ not only for their anatomy and ion channel distribution, but also for the type, strength, location, and temporal pattern of activity of the many synaptic inputs they receive. Compartmental modeling programs like NEURON have become widely used in the neuroscience community to address a broad range of research questions, including how neurons integrate synaptic inputs and propagate information through complex neural networks. One of the main strengths of NEURON is its ability to incorporate user-defined information about the realistic morphology and biophysical properties of different cell types. Although the graphical user interface of the program can be used to run initial exploratory simulations, introducing a stochastic representation of synaptic weights, locations and activation times typically requires users to develop their own codes, a task that can be overwhelming for some beginner users. Here we describe NRN-EZ, an interactive application that allows users to specify complex patterns of synaptic input activity that can be integrated as part of NEURON simulations. Through its graphical user interface, NRN-EZ aims to ease the learning curve to run computational models in NEURON, for users that do not necessarily have a computer science background.
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Affiliation(s)
- Evan A. W. Cobb
- grid.265850.c0000 0001 2151 7947Department of Biology, SUNY Albany, 1400 Washington Avenue, Albany, NY 12222-0100 USA ,grid.265850.c0000 0001 2151 7947Department of Computer Science, SUNY Albany, 1400 Washington Avenue, Albany, NY 12222-0100 USA
| | - Maurice A. Petroccione
- grid.265850.c0000 0001 2151 7947Department of Biology, SUNY Albany, 1400 Washington Avenue, Albany, NY 12222-0100 USA
| | - Annalisa Scimemi
- Department of Biology, SUNY Albany, 1400 Washington Avenue, Albany, NY, 12222-0100, USA.
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13
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Buchin A, de Frates R, Nandi A, Mann R, Chong P, Ng L, Miller J, Hodge R, Kalmbach B, Bose S, Rutishauser U, McConoughey S, Lein E, Berg J, Sorensen S, Gwinn R, Koch C, Ting J, Anastassiou CA. Multi-modal characterization and simulation of human epileptic circuitry. Cell Rep 2022; 41:111873. [PMID: 36577383 PMCID: PMC9841067 DOI: 10.1016/j.celrep.2022.111873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 06/16/2022] [Accepted: 12/02/2022] [Indexed: 12/28/2022] Open
Abstract
Temporal lobe epilepsy is the fourth most common neurological disorder, with about 40% of patients not responding to pharmacological treatment. Increased cellular loss is linked to disease severity and pathological phenotypes such as heightened seizure propensity. While the hippocampus is the target of therapeutic interventions, the impact of the disease at the cellular level remains unclear. Here, we show that hippocampal granule cells change with disease progression as measured in living, resected hippocampal tissue excised from patients with epilepsy. We show that granule cells increase excitability and shorten response latency while also enlarging in cellular volume and spine density. Single-nucleus RNA sequencing combined with simulations ascribes the changes to three conductances: BK, Cav2.2, and Kir2.1. In a network model, we show that these changes related to disease progression bring the circuit into a more excitable state, while reversing them produces a less excitable, "early-disease-like" state.
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Affiliation(s)
- Anatoly Buchin
- Allen Institute for Brain Science, Seattle, WA, USA,Present address: Cajal Neuroscience, Inc., Seattle, WA, USA,Correspondence: (A.B.), (C.A.A.)
| | - Rebecca de Frates
- Allen Institute for Brain Science, Seattle, WA, USA,These authors contributed equally
| | - Anirban Nandi
- Allen Institute for Brain Science, Seattle, WA, USA,These authors contributed equally
| | - Rusty Mann
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Peter Chong
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lindsay Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Brian Kalmbach
- Allen Institute for Brain Science, Seattle, WA, USA,University of Washington, Seattle, WA, USA
| | - Soumita Bose
- Allen Institute for Brain Science, Seattle, WA, USA,CiperHealth, San Francisco, CA, USA
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA,Department of Neurology, 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
| | - Stephen McConoughey
- Allen Institute for Brain Science, Seattle, WA, USA,Present address: Institute for Advanced Clinical Trials for Children, 9200 Corporate Blvd, Suite 350, Rockville, MD 20850, USA
| | - Ed Lein
- Allen Institute for Brain Science, Seattle, WA, USA,University of Washington, Seattle, WA, USA
| | - Jim Berg
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Jonathan Ting
- Allen Institute for Brain Science, Seattle, WA, USA,University of Washington, Seattle, WA, USA
| | - Costas A. Anastassiou
- Allen Institute for Brain Science, Seattle, WA, USA,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,Correspondence: (A.B.), (C.A.A.)
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14
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Model simulations unveil the structure-function-dynamics relationship of the cerebellar cortical microcircuit. Commun Biol 2022; 5:1240. [PMCID: PMC9663576 DOI: 10.1038/s42003-022-04213-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 11/02/2022] [Indexed: 11/16/2022] Open
Abstract
AbstractThe cerebellar network is renowned for its regular architecture that has inspired foundational computational theories. However, the relationship between circuit structure, function and dynamics remains elusive. To tackle the issue, we developed an advanced computational modeling framework that allows us to reconstruct and simulate the structure and function of the mouse cerebellar cortex using morphologically realistic multi-compartmental neuron models. The cerebellar connectome is generated through appropriate connection rules, unifying a collection of scattered experimental data into a coherent construct and providing a new model-based ground-truth about circuit organization. Naturalistic background and sensory-burst stimulation are used for functional validation against recordings in vivo, monitoring the impact of cellular mechanisms on signal propagation, inhibitory control, and long-term synaptic plasticity. Our simulations show how mossy fibers entrain the local neuronal microcircuit, boosting the formation of columns of activity travelling from the granular to the molecular layer providing a new resource for the investigation of local microcircuit computation and of the neural correlates of behavior.
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15
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Awile O, Kumbhar P, Cornu N, Dura-Bernal S, King JG, Lupton O, Magkanaris I, McDougal RA, Newton AJH, Pereira F, Săvulescu A, Carnevale NT, Lytton WW, Hines ML, Schürmann F. Modernizing the NEURON Simulator for Sustainability, Portability, and Performance. Front Neuroinform 2022; 16:884046. [PMID: 35832575 PMCID: PMC9272742 DOI: 10.3389/fninf.2022.884046] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/26/2022] [Indexed: 12/25/2022] Open
Abstract
The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON's ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.
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Affiliation(s)
- Omar Awile
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Pramod Kumbhar
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Nicolas Cornu
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Salvador Dura-Bernal
- Department Physiology and Pharmacology, SUNY Downstate, Brooklyn, NY, United States
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - James Gonzalo King
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Olli Lupton
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Ioannis Magkanaris
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Robert A. McDougal
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States
- Yale Center for Medical Informatics, Yale University, New Haven, CT, United States
| | - Adam J. H. Newton
- Department Physiology and Pharmacology, SUNY Downstate, Brooklyn, NY, United States
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States
| | - Fernando Pereira
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Alexandru Săvulescu
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | | | - William W. Lytton
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - Michael L. Hines
- Department of Neuroscience, Yale University, New Haven, CT, United States
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
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16
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Schneider-Mizell CM, Bodor AL, Collman F, Brittain D, Bleckert A, Dorkenwald S, Turner NL, Macrina T, Lee K, Lu R, Wu J, Zhuang J, Nandi A, Hu B, Buchanan J, Takeno MM, Torres R, Mahalingam G, Bumbarger DJ, Li Y, Chartrand T, Kemnitz N, Silversmith WM, Ih D, Zung J, Zlateski A, Tartavull I, Popovych S, Wong W, Castro M, Jordan CS, Froudarakis E, Becker L, Suckow S, Reimer J, Tolias AS, Anastassiou CA, Seung HS, Reid RC, da Costa NM. Structure and function of axo-axonic inhibition. eLife 2021; 10:e73783. [PMID: 34851292 PMCID: PMC8758143 DOI: 10.7554/elife.73783] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 11/30/2021] [Indexed: 11/13/2022] Open
Abstract
Inhibitory neurons in mammalian cortex exhibit diverse physiological, morphological, molecular, and connectivity signatures. While considerable work has measured the average connectivity of several interneuron classes, there remains a fundamental lack of understanding of the connectivity distribution of distinct inhibitory cell types with synaptic resolution, how it relates to properties of target cells, and how it affects function. Here, we used large-scale electron microscopy and functional imaging to address these questions for chandelier cells in layer 2/3 of the mouse visual cortex. With dense reconstructions from electron microscopy, we mapped the complete chandelier input onto 153 pyramidal neurons. We found that synapse number is highly variable across the population and is correlated with several structural features of the target neuron. This variability in the number of axo-axonic ChC synapses is higher than the variability seen in perisomatic inhibition. Biophysical simulations show that the observed pattern of axo-axonic inhibition is particularly effective in controlling excitatory output when excitation and inhibition are co-active. Finally, we measured chandelier cell activity in awake animals using a cell-type-specific calcium imaging approach and saw highly correlated activity across chandelier cells. In the same experiments, in vivo chandelier population activity correlated with pupil dilation, a proxy for arousal. Together, these results suggest that chandelier cells provide a circuit-wide signal whose strength is adjusted relative to the properties of target neurons.
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Affiliation(s)
| | - Agnes L Bodor
- Allen Institute for Brain SciencesSeattleUnited States
| | | | | | - Adam Bleckert
- Allen Institute for Brain SciencesSeattleUnited States
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - Nicholas L Turner
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Brain & Cognitive Sciences Department, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jun Zhuang
- Allen Institute for Brain SciencesSeattleUnited States
| | - Anirban Nandi
- Allen Institute for Brain SciencesSeattleUnited States
| | - Brian Hu
- Allen Institute for Brain SciencesSeattleUnited States
| | | | - Marc M Takeno
- Allen Institute for Brain SciencesSeattleUnited States
| | - Russel Torres
- Allen Institute for Brain SciencesSeattleUnited States
| | | | | | - Yang Li
- Allen Institute for Brain SciencesSeattleUnited States
| | | | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | | | - Dodam Ih
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jonathan Zung
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Aleksandar Zlateski
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Ignacio Tartavull
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - William Wong
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Center for Neuroscience and Artificial Intelligence, Baylor College of MedicineHoustonUnited States
| | - Lynne Becker
- Allen Institute for Brain SciencesSeattleUnited States
| | - Shelby Suckow
- Allen Institute for Brain SciencesSeattleUnited States
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Center for Neuroscience and Artificial Intelligence, Baylor College of MedicineHoustonUnited States
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Center for Neuroscience and Artificial Intelligence, Baylor College of MedicineHoustonUnited States
- Department of Electrical and Computer Engineering, Rice UniversityHoustonUnited States
| | - Costas A Anastassiou
- Allen Institute for Brain SciencesSeattleUnited States
- Department of Neurology, University of British ColumbiaVancouverCanada
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - R Clay Reid
- Allen Institute for Brain SciencesSeattleUnited States
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17
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Sinha M, Narayanan R. Active Dendrites and Local Field Potentials: Biophysical Mechanisms and Computational Explorations. Neuroscience 2021; 489:111-142. [PMID: 34506834 PMCID: PMC7612676 DOI: 10.1016/j.neuroscience.2021.08.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 08/30/2021] [Accepted: 08/31/2021] [Indexed: 10/27/2022]
Abstract
Neurons and glial cells are endowed with membranes that express a rich repertoire of ion channels, transporters, and receptors. The constant flux of ions across the neuronal and glial membranes results in voltage fluctuations that can be recorded from the extracellular matrix. The high frequency components of this voltage signal contain information about the spiking activity, reflecting the output from the neurons surrounding the recording location. The low frequency components of the signal, referred to as the local field potential (LFP), have been traditionally thought to provide information about the synaptic inputs that impinge on the large dendritic trees of various neurons. In this review, we discuss recent computational and experimental studies pointing to a critical role of several active dendritic mechanisms that can influence the genesis and the location-dependent spectro-temporal dynamics of LFPs, spanning different brain regions. We strongly emphasize the need to account for the several fast and slow dendritic events and associated active mechanisms - including gradients in their expression profiles, inter- and intra-cellular spatio-temporal interactions spanning neurons and glia, heterogeneities and degeneracy across scales, neuromodulatory influences, and activitydependent plasticity - towards gaining important insights about the origins of LFP under different behavioral states in health and disease. We provide simple but essential guidelines on how to model LFPs taking into account these dendritic mechanisms, with detailed methodology on how to account for various heterogeneities and electrophysiological properties of neurons and synapses while studying LFPs.
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Affiliation(s)
- Manisha Sinha
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka 560012, India.
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18
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Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda. Neuroinformatics 2021; 19:685-701. [PMID: 34282528 PMCID: PMC8566446 DOI: 10.1007/s12021-021-09531-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/09/2021] [Indexed: 12/25/2022]
Abstract
Simulation of large-scale networks of neurons is an important approach to understanding and interpreting experimental data from healthy and diseased brains. Owing to the rapid development of simulation software and the accumulation of quantitative data of different neuronal types, it is possible to predict both computational and dynamical properties of local microcircuits in a ‘bottom-up’ manner. Simulated data from these models can be compared with experiments and ‘top-down’ modelling approaches, successively bridging the scales. Here we describe an open source pipeline, using the software Snudda, for predicting microcircuit connectivity and for setting up simulations using the NEURON simulation environment in a reproducible way. We also illustrate how to further ‘curate’ data on single neuron morphologies acquired from public databases. This model building pipeline was used to set up a first version of a full-scale cellular level model of mouse dorsal striatum. Model components from that work are here used to illustrate the different steps that are needed when modelling subcortical nuclei, such as the basal ganglia.
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19
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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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20
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Dai K, Gratiy SL, Billeh YN, Xu R, Cai B, Cain N, Rimehaug AE, Stasik AJ, Einevoll GT, Mihalas S, Koch C, Arkhipov A. Brain Modeling ToolKit: An open source software suite for multiscale modeling of brain circuits. PLoS Comput Biol 2020; 16:e1008386. [PMID: 33253147 PMCID: PMC7728187 DOI: 10.1371/journal.pcbi.1008386] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 12/10/2020] [Accepted: 09/16/2020] [Indexed: 11/26/2022] Open
Abstract
Experimental studies in neuroscience are producing data at a rapidly increasing rate, providing exciting opportunities and formidable challenges to existing theoretical and modeling approaches. To turn massive datasets into predictive quantitative frameworks, the field needs software solutions for systematic integration of data into realistic, multiscale models. Here we describe the Brain Modeling ToolKit (BMTK), a software suite for building models and performing simulations at multiple levels of resolution, from biophysically detailed multi-compartmental, to point-neuron, to population-statistical approaches. Leveraging the SONATA file format and existing software such as NEURON, NEST, and others, BMTK offers a consistent user experience across multiple levels of resolution. It permits highly sophisticated simulations to be set up with little coding required, thus lowering entry barriers to new users. We illustrate successful applications of BMTK to large-scale simulations of a cortical area. BMTK is an open-source package provided as a resource supporting modeling-based discovery in the community.
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Affiliation(s)
- Kael Dai
- Allen Institute, Seattle, Washington, United States of America
| | | | - Yazan N. Billeh
- Allen Institute, Seattle, Washington, United States of America
| | - Richard Xu
- Allen Institute, Seattle, Washington, United States of America
| | - Binghuang Cai
- Allen Institute, Seattle, Washington, United States of America
| | - Nicholas Cain
- Allen Institute, Seattle, Washington, United States of America
| | - Atle E. Rimehaug
- Norwegian University of Life Sciences & University of Oslo, Oslo, Norway
| | | | - Gaute T. Einevoll
- Norwegian University of Life Sciences & University of Oslo, Oslo, Norway
| | - Stefan Mihalas
- Allen Institute, Seattle, Washington, United States of America
| | - Christof Koch
- Allen Institute, Seattle, Washington, United States of America
| | - Anton Arkhipov
- Allen Institute, Seattle, Washington, United States of America
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21
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Næss S, Halnes G, Hagen E, Hagler DJ, Dale AM, Einevoll GT, Ness TV. Biophysically detailed forward modeling of the neural origin of EEG and MEG signals. Neuroimage 2020; 225:117467. [PMID: 33075556 DOI: 10.1016/j.neuroimage.2020.117467] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 09/28/2020] [Accepted: 10/12/2020] [Indexed: 12/22/2022] Open
Abstract
Electroencephalography (EEG) and magnetoencephalography (MEG) are among the most important techniques for non-invasively studying cognition and disease in the human brain. These signals are known to originate from cortical neural activity, typically described in terms of current dipoles. While the link between cortical current dipoles and EEG/MEG signals is relatively well understood, surprisingly little is known about the link between different kinds of neural activity and the current dipoles themselves. Detailed biophysical modeling has played an important role in exploring the neural origin of intracranial electric signals, like extracellular spikes and local field potentials. However, this approach has not yet been taken full advantage of in the context of exploring the neural origin of the cortical current dipoles that are causing EEG/MEG signals. Here, we present a method for reducing arbitrary simulated neural activity to single current dipoles. We find that the method is applicable for calculating extracranial signals, but less suited for calculating intracranial electrocorticography (ECoG) signals. We demonstrate that this approach can serve as a powerful tool for investigating the neural origin of EEG/MEG signals. This is done through example studies of the single-neuron EEG contribution, the putative EEG contribution from calcium spikes, and from calculating EEG signals from large-scale neural network simulations. We also demonstrate how the simulated current dipoles can be used directly in combination with detailed head models, allowing for simulated EEG signals with an unprecedented level of biophysical details. In conclusion, this paper presents a framework for biophysically detailed modeling of EEG and MEG signals, which can be used to better our understanding of non-inasively measured neural activity in humans.
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Affiliation(s)
- Solveig Næss
- Department of Informatics, University of Oslo, Oslo 0316, Norway
| | - Geir Halnes
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway
| | - Espen Hagen
- Department of Physics, University of Oslo, Oslo 0316, Norway
| | - Donald J Hagler
- Department of Radiology, University of California, La Jolla, CA 92093, USA
| | - Anders M Dale
- Department of Radiology, University of California, La Jolla, CA 92093, USA; Department of Neurosciences, University of California, La Jolla, CA 92093, USA
| | - Gaute T Einevoll
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway; Department of Physics, University of Oslo, Oslo 0316, Norway.
| | - Torbjørn V Ness
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway.
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22
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Magland J, Jun JJ, Lovero E, Morley AJ, Hurwitz CL, Buccino AP, Garcia S, Barnett AH. SpikeForest, reproducible web-facing ground-truth validation of automated neural spike sorters. eLife 2020; 9:e55167. [PMID: 32427564 PMCID: PMC7237210 DOI: 10.7554/elife.55167] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 05/05/2020] [Indexed: 01/03/2023] Open
Abstract
Spike sorting is a crucial step in electrophysiological studies of neuronal activity. While many spike sorting packages are available, there is little consensus about which are most accurate under different experimental conditions. SpikeForest is an open-source and reproducible software suite that benchmarks the performance of automated spike sorting algorithms across an extensive, curated database of ground-truth electrophysiological recordings, displaying results interactively on a continuously-updating website. With contributions from eleven laboratories, our database currently comprises 650 recordings (1.3 TB total size) with around 35,000 ground-truth units. These data include paired intracellular/extracellular recordings and state-of-the-art simulated recordings. Ten of the most popular spike sorting codes are wrapped in a Python package and evaluated on a compute cluster using an automated pipeline. SpikeForest documents community progress in automated spike sorting, and guides neuroscientists to an optimal choice of sorter and parameters for a wide range of probes and brain regions.
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Affiliation(s)
- Jeremy Magland
- Center for Computational Mathematics, Flatiron InstituteNew YorkUnited States
| | - James J Jun
- Center for Computational Mathematics, Flatiron InstituteNew YorkUnited States
| | - Elizabeth Lovero
- Scientific Computing Core, Flatiron InstituteNew YorkUnited States
| | - Alexander J Morley
- Medical Research Council Brain Network Dynamics Unit, University of OxfordOxfordUnited Kingdom
| | - Cole Lincoln Hurwitz
- Institute for Adaptive and Neural Computation Informatics, University of EdinburghEdinburghUnited Kingdom
| | | | - Samuel Garcia
- Centre de Recherche en Neuroscience de Lyon, Université de LyonLyonFrance
| | - Alex H Barnett
- Center for Computational Mathematics, Flatiron InstituteNew YorkUnited States
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23
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Systematic Integration of Structural and Functional Data into Multi-scale Models of Mouse Primary Visual Cortex. Neuron 2020; 106:388-403.e18. [DOI: 10.1016/j.neuron.2020.01.040] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 10/17/2019] [Accepted: 01/27/2020] [Indexed: 01/08/2023]
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24
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Skaar JEW, Stasik AJ, Hagen E, Ness TV, Einevoll GT. Estimation of neural network model parameters from local field potentials (LFPs). PLoS Comput Biol 2020; 16:e1007725. [PMID: 32155141 PMCID: PMC7083334 DOI: 10.1371/journal.pcbi.1007725] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 03/20/2020] [Accepted: 02/12/2020] [Indexed: 11/20/2022] Open
Abstract
Most modeling in systems neuroscience has been descriptive where neural representations such as ‘receptive fields’, have been found by statistically correlating neural activity to sensory input. In the traditional physics approach to modelling, hypotheses are represented by mechanistic models based on the underlying building blocks of the system, and candidate models are validated by comparing with experiments. Until now validation of mechanistic cortical network models has been based on comparison with neuronal spikes, found from the high-frequency part of extracellular electrical potentials. In this computational study we investigated to what extent the low-frequency part of the signal, the local field potential (LFP), can be used to validate and infer properties of mechanistic cortical network models. In particular, we asked the question whether the LFP can be used to accurately estimate synaptic connection weights in the underlying network. We considered the thoroughly analysed Brunel network comprising an excitatory and an inhibitory population of recurrently connected integrate-and-fire (LIF) neurons. This model exhibits a high diversity of spiking network dynamics depending on the values of only three network parameters. The LFP generated by the network was computed using a hybrid scheme where spikes computed from the point-neuron network were replayed on biophysically detailed multicompartmental neurons. We assessed how accurately the three model parameters could be estimated from power spectra of stationary ‘background’ LFP signals by application of convolutional neural nets (CNNs). All network parameters could be very accurately estimated, suggesting that LFPs indeed can be used for network model validation. Most of what we have learned about brain networks in vivo has come from the measurement of spikes (action potentials) recorded by extracellular electrodes. The low-frequency part of these signals, the local field potential (LFP), contains unique information about how dendrites in neuronal populations integrate synaptic inputs, but has so far played a lesser role. To investigate whether the LFP can be used to validate network models, we computed LFP signals for a recurrent network model (the Brunel network) for which the ground-truth parameters are known. By application of convolutional neural nets (CNNs) we found that the synaptic weights indeed could be accurately estimated from ‘background’ LFP signals, suggesting a future key role for LFP in development of network models.
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Affiliation(s)
- Jan-Eirik W. Skaar
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | | | - Espen Hagen
- Department of Physics, University of Oslo, Oslo, Norway
| | - Torbjørn V. Ness
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Gaute T. Einevoll
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
- Department of Physics, University of Oslo, Oslo, Norway
- * E-mail:
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25
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Dai K, Hernando J, Billeh YN, Gratiy SL, Planas J, Davison AP, Dura-Bernal S, Gleeson P, Devresse A, Dichter BK, Gevaert M, King JG, Van Geit WAH, Povolotsky AV, Muller E, Courcol JD, Arkhipov A. The SONATA data format for efficient description of large-scale network models. PLoS Comput Biol 2020; 16:e1007696. [PMID: 32092054 PMCID: PMC7058350 DOI: 10.1371/journal.pcbi.1007696] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 03/05/2020] [Accepted: 01/28/2020] [Indexed: 12/04/2022] Open
Abstract
Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility. Neuroscience is experiencing a rapid growth of data streams characterizing composition, connectivity, and activity of brain networks in ever increasing details. Data-driven modeling will be essential to integrate these multimodal and complex data into predictive simulations to advance our understanding of brain function and mechanisms. To enable efficient development and sharing of such large-scale models utilizing diverse data types, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is already supported by several popular tools for model building, simulations, and visualization. It is free and open for everyone to use and build upon and will enable increased efficiency, reproducibility, and scientific exchange in the community.
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Affiliation(s)
- Kael Dai
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Juan Hernando
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Yazan N. Billeh
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Sergey L. Gratiy
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Judit Planas
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Andrew P. Davison
- Paris-Saclay Institute of Neuroscience UMR, Centre National de la Recherche Scientifique/Université Paris-Saclay, Gif-sur-Yvette, France
| | - Salvador Dura-Bernal
- State University of New York Downstate Medical Center, Brooklyn, New York, United States of America
- Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom
| | - Adrien Devresse
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Benjamin K. Dichter
- Department of Neurosurgery, Stanford University, Stanford, California, United States of America
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Michael Gevaert
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - James G. King
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Werner A. H. Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Arseny V. Povolotsky
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Eilif Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Jean-Denis Courcol
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Anton Arkhipov
- Allen Institute for Brain Science, Seattle, Washington, United States of America
- * E-mail:
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26
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Neymotin SA, Daniels DS, Caldwell B, McDougal RA, Carnevale NT, Jas M, Moore CI, Hines ML, Hämäläinen M, Jones SR. Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data. eLife 2020; 9:e51214. [PMID: 31967544 PMCID: PMC7018509 DOI: 10.7554/elife.51214] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 01/22/2020] [Indexed: 12/26/2022] Open
Abstract
Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution providing reliable markers of healthy and disease states. Relating these macroscopic signals to underlying cellular- and circuit-level generators is a limitation that constrains using MEG/EEG to reveal novel principles of information processing or to translate findings into new therapies for neuropathology. To address this problem, we built Human Neocortical Neurosolver (HNN, https://hnn.brown.edu) software. HNN has a graphical user interface designed to help researchers and clinicians interpret the neural origins of MEG/EEG. HNN's core is a neocortical circuit model that accounts for biophysical origins of electrical currents generating MEG/EEG. Data can be directly compared to simulated signals and parameters easily manipulated to develop/test hypotheses on a signal's origin. Tutorials teach users to simulate commonly measured signals, including event related potentials and brain rhythms. HNN's ability to associate signals across scales makes it a unique tool for translational neuroscience research.
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Affiliation(s)
- Samuel A Neymotin
- Department Neuroscience, Carney Institute for Brain SciencesBrown UniversityProvidenceUnited States
- Center for Biomedical Imaging and NeuromodulationNathan S. Kline Institute for Psychiatric ResearchOrangeburgUnited States
| | - Dylan S Daniels
- Department Neuroscience, Carney Institute for Brain SciencesBrown UniversityProvidenceUnited States
| | - Blake Caldwell
- Department Neuroscience, Carney Institute for Brain SciencesBrown UniversityProvidenceUnited States
| | - Robert A McDougal
- Department NeuroscienceYale UniversityNew HavenUnited States
- Department of BiostatisticsYale UniversityNew HavenUnited States
| | | | - Mainak Jas
- Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalCharlestownUnited States
- Harvard Medical SchoolBostonUnited States
| | - Christopher I Moore
- Department Neuroscience, Carney Institute for Brain SciencesBrown UniversityProvidenceUnited States
| | - Michael L Hines
- Department NeuroscienceYale UniversityNew HavenUnited States
| | - Matti Hämäläinen
- Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalCharlestownUnited States
- Harvard Medical SchoolBostonUnited States
| | - Stephanie R Jones
- Department Neuroscience, Carney Institute for Brain SciencesBrown UniversityProvidenceUnited States
- Center for Neurorestoration and NeurotechnologyProvidence VAMCProvidenceUnited States
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27
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Amsalem O, Eyal G, Rogozinski N, Gevaert M, Kumbhar P, Schürmann F, Segev I. An efficient analytical reduction of detailed nonlinear neuron models. Nat Commun 2020; 11:288. [PMID: 31941884 PMCID: PMC6962154 DOI: 10.1038/s41467-019-13932-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 12/09/2019] [Indexed: 12/31/2022] Open
Abstract
Detailed conductance-based nonlinear neuron models consisting of thousands of synapses are key for understanding of the computational properties of single neurons and large neuronal networks, and for interpreting experimental results. Simulations of these models are computationally expensive, considerably curtailing their utility. Neuron_Reduce is a new analytical approach to reduce the morphological complexity and computational time of nonlinear neuron models. Synapses and active membrane channels are mapped to the reduced model preserving their transfer impedance to the soma; synapses with identical transfer impedance are merged into one NEURON process still retaining their individual activation times. Neuron_Reduce accelerates the simulations by 40-250 folds for a variety of cell types and realistic number (10,000-100,000) of synapses while closely replicating voltage dynamics and specific dendritic computations. The reduced neuron-models will enable realistic simulations of neural networks at unprecedented scale, including networks emerging from micro-connectomics efforts and biologically-inspired "deep networks". Neuron_Reduce is publicly available and is straightforward to implement.
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Affiliation(s)
- Oren Amsalem
- Department of Neurobiology, Hebrew University of Jerusalem, 9190401, Jerusalem, Israel.
| | - Guy Eyal
- Department of Neurobiology, Hebrew University of Jerusalem, 9190401, Jerusalem, Israel
| | - Noa Rogozinski
- Department of Neurobiology, Hebrew University of Jerusalem, 9190401, Jerusalem, Israel
| | - Michael Gevaert
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202, Geneva, Switzerland
| | - Pramod Kumbhar
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202, Geneva, Switzerland
| | - Felix Schürmann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202, Geneva, Switzerland
| | - Idan Segev
- Department of Neurobiology, Hebrew University of Jerusalem, 9190401, Jerusalem, Israel
- Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, 9190401, Jerusalem, Israel
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28
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Gast R, Rose D, Salomon C, Möller HE, Weiskopf N, Knösche TR. PyRates-A Python framework for rate-based neural simulations. PLoS One 2019; 14:e0225900. [PMID: 31841550 PMCID: PMC6913930 DOI: 10.1371/journal.pone.0225900] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 11/14/2019] [Indexed: 12/13/2022] Open
Abstract
In neuroscience, computational modeling has become an important source of insight into brain states and dynamics. A basic requirement for computational modeling studies is the availability of efficient software for setting up models and performing numerical simulations. While many such tools exist for different families of neural models, there is a lack of tools allowing for both a generic model definition and efficiently parallelized simulations. In this work, we present PyRates, a Python framework that provides the means to build a large variety of rate-based neural models. PyRates provides intuitive access to and modification of all mathematical operators in a graph, thus allowing for a highly generic model definition. For computational efficiency and parallelization, the model is translated into a compute graph. Using the example of two different neural models belonging to the family of rate-based population models, we explain the mathematical formalism, software structure and user interfaces of PyRates. We show via numerical simulations that the behavior of the PyRates model implementations is consistent with the literature. Finally, we demonstrate the computational capacities and scalability of PyRates via a number of benchmark simulations of neural networks differing in size and connectivity.
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Affiliation(s)
- Richard Gast
- MEG and Cortical Networks Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany
- Nuclear Magnetic Resonance Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany
- Neurophysics Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany
| | - Daniel Rose
- Neurophysics Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany
| | - Christoph Salomon
- MEG and Cortical Networks Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany
- Institute for Biomedical Engineering and Informatics, TU Ilmenau, Ilmenau, Thuringia, Germany
| | - Harald E. Möller
- Nuclear Magnetic Resonance Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany
| | - Nikolaus Weiskopf
- Neurophysics Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany
| | - Thomas R. Knösche
- MEG and Cortical Networks Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, Germany
- Institute for Biomedical Engineering and Informatics, TU Ilmenau, Ilmenau, Thuringia, Germany
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29
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Shifman AR, Lewis JE. E LFENN: A Generalized Platform for Modeling Ephaptic Coupling in Spiking Neuron Models. Front Neuroinform 2019; 13:35. [PMID: 31214004 PMCID: PMC6555196 DOI: 10.3389/fninf.2019.00035] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 04/24/2019] [Indexed: 12/18/2022] Open
Abstract
The transmembrane ionic currents that underlie changes in a cell's membrane potential give rise to electric fields in the extracellular space. In the context of brain activity, these electric fields form the basis for extracellularly recorded signals, such as multiunit activity, local field potentials and electroencephalograms. Understanding the underlying neuronal dynamics and localizing current sources using these signals is often challenging, and therefore effective computational modeling approaches are critical. Typically, the electric fields from neural activity are modeled in a post-hoc form, i.e., a traditional neuronal model is used to first generate the membrane currents, which in turn are then used to calculate the electric fields. When the conductivity of the extracellular space is high, the electric fields are weak, and therefore treating membrane currents and electric fields separately is justified. However, in brain regions of lower conductivity, extracellular fields can feed back and significantly influence the underlying transmembrane currents and dynamics of nearby neurons—this is often referred to as ephaptic coupling. The closed-loop nature of ephaptic coupling cannot be modeled using the post-hoc approaches implemented by existing software tools; instead, electric fields and neuronal dynamics must be solved simultaneously. To this end, we have developed a generalized modeling toolbox for studying ephaptic coupling in compartmental neuron models: ELFENN (ELectric Field Effects in Neural Networks). In open loop conditions, we validate the separate components of ELFENN for modeling membrane dynamics and associated field potentials against standard approaches (NEURON and LFPy). Unlike standard approaches however, ELFENN enables the closed-loop condition to be modeled as well, in that the field potentials can feed back and influence membrane dynamics. As an example closed-loop case, we use ELFENN to study phase-locking of action potentials generated by a population of axons running parallel in a bundle. Being able to efficiently explore ephaptic coupling from a computational perspective using tools, such as ELFENN will allow us to better understand the physical basis of electric fields in the brain, as well as the conditions in which these fields may influence neuronal dynamics in general.
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Affiliation(s)
- Aaron R Shifman
- Department of Biology, University of Ottawa, Ottawa, ON, Canada.,Center for Neural Dynamics, University of Ottawa, Ottawa, ON, Canada.,uOttawa Brain and Mind Research Institute, Ottawa, ON, Canada
| | - John E Lewis
- Department of Biology, University of Ottawa, Ottawa, ON, Canada.,Center for Neural Dynamics, University of Ottawa, Ottawa, ON, Canada.,uOttawa Brain and Mind Research Institute, Ottawa, ON, Canada
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30
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Einevoll GT, Destexhe A, Diesmann M, Grün S, Jirsa V, de Kamps M, Migliore M, Ness TV, Plesser HE, Schürmann F. The Scientific Case for Brain Simulations. Neuron 2019; 102:735-744. [DOI: 10.1016/j.neuron.2019.03.027] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 02/06/2019] [Accepted: 03/18/2019] [Indexed: 01/30/2023]
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31
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Dura-Bernal S, Suter BA, Gleeson P, Cantarelli M, Quintana A, Rodriguez F, Kedziora DJ, Chadderdon GL, Kerr CC, Neymotin SA, McDougal RA, Hines M, Shepherd GMG, Lytton WW. NetPyNE, a tool for data-driven multiscale modeling of brain circuits. eLife 2019; 8:e44494. [PMID: 31025934 PMCID: PMC6534378 DOI: 10.7554/elife.44494] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 04/25/2019] [Indexed: 12/22/2022] Open
Abstract
Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis - connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.
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Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
| | - Benjamin A Suter
- Department of PhysiologyNorthwestern UniversityChicagoUnited States
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and PharmacologyUniversity College LondonLondonUnited Kingdom
| | | | | | - Facundo Rodriguez
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
- MetaCell LLCBostonUnited States
| | - David J Kedziora
- Complex Systems Group, School of PhysicsUniversity of SydneySydneyAustralia
| | - George L Chadderdon
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
| | - Cliff C Kerr
- Complex Systems Group, School of PhysicsUniversity of SydneySydneyAustralia
| | - Samuel A Neymotin
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
- Nathan Kline Institute for Psychiatric ResearchOrangeburgUnited States
| | - Robert A McDougal
- Department of Neuroscience and School of MedicineYale UniversityNew HavenUnited States
- Center for Medical InformaticsYale UniversityNew HavenUnited States
| | - Michael Hines
- Department of Neuroscience and School of MedicineYale UniversityNew HavenUnited States
| | | | - William W Lytton
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
- Department of NeurologyKings County HospitalBrooklynUnited States
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32
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Hagen E, Næss S, Ness TV, Einevoll GT. Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0. Front Neuroinform 2018; 12:92. [PMID: 30618697 PMCID: PMC6305460 DOI: 10.3389/fninf.2018.00092] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Accepted: 11/21/2018] [Indexed: 11/13/2022] Open
Abstract
Recordings of extracellular electrical, and later also magnetic, brain signals have been the dominant technique for measuring brain activity for decades. The interpretation of such signals is however nontrivial, as the measured signals result from both local and distant neuronal activity. In volume-conductor theory the extracellular potentials can be calculated from a distance-weighted sum of contributions from transmembrane currents of neurons. Given the same transmembrane currents, the contributions to the magnetic field recorded both inside and outside the brain can also be computed. This allows for the development of computational tools implementing forward models grounded in the biophysics underlying electrical and magnetic measurement modalities. LFPy (LFPy.readthedocs.io) incorporated a well-established scheme for predicting extracellular potentials of individual neurons with arbitrary levels of biological detail. It relies on NEURON (neuron.yale.edu) to compute transmembrane currents of multicompartment neurons which is then used in combination with an electrostatic forward model. Its functionality is now extended to allow for modeling of networks of multicompartment neurons with concurrent calculations of extracellular potentials and current dipole moments. The current dipole moments are then, in combination with suitable volume-conductor head models, used to compute non-invasive measures of neuronal activity, like scalp potentials (electroencephalographic recordings; EEG) and magnetic fields outside the head (magnetoencephalographic recordings; MEG). One such built-in head model is the four-sphere head model incorporating the different electric conductivities of brain, cerebrospinal fluid, skull and scalp. We demonstrate the new functionality of the software by constructing a network of biophysically detailed multicompartment neuron models from the Neocortical Microcircuit Collaboration (NMC) Portal (bbp.epfl.ch/nmc-portal) with corresponding statistics of connections and synapses, and compute in vivo-like extracellular potentials (local field potentials, LFP; electrocorticographical signals, ECoG) and corresponding current dipole moments. From the current dipole moments we estimate corresponding EEG and MEG signals using the four-sphere head model. We also show strong scaling performance of LFPy with different numbers of message-passing interface (MPI) processes, and for different network sizes with different density of connections. The open-source software LFPy is equally suitable for execution on laptops and in parallel on high-performance computing (HPC) facilities and is publicly available on GitHub.com.
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Affiliation(s)
- Espen Hagen
- Department of Physics, University of Oslo, Oslo, Norway.,Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Solveig Næss
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Torbjørn V Ness
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Gaute T Einevoll
- Department of Physics, University of Oslo, Oslo, Norway.,Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
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33
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Gorur-Shandilya S, Hoyland A, Marder E. Xolotl: An Intuitive and Approachable Neuron and Network Simulator for Research and Teaching. Front Neuroinform 2018; 12:87. [PMID: 30534067 PMCID: PMC6275287 DOI: 10.3389/fninf.2018.00087] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 11/05/2018] [Indexed: 11/13/2022] Open
Abstract
Conductance-based models of neurons are used extensively in computational neuroscience. Working with these models can be challenging due to their high dimensionality and large number of parameters. Here, we present a neuron and network simulator built on a novel automatic type system that binds object-oriented code written in C++ to objects in MATLAB. Our approach builds on the tradition of uniting the speed of languages like C++ with the ease-of-use and feature-set of scientific programming languages like MATLAB. Xolotl allows for the creation and manipulation of hierarchical models with components that are named and searchable, permitting intuitive high-level programmatic control over all parts of the model. The simulator's architecture allows for the interactive manipulation of any parameter in any model, and for visualizing the effects of changing that parameter immediately. Xolotl is fully featured with hundreds of ion channel models from the electrophysiological literature, and can be extended to include arbitrary conductances, synapses, and mechanisms. Several core features like bookmarking of parameters and automatic hashing of source code facilitate reproducible and auditable research. Its ease of use and rich visualization capabilities make it an attractive option in teaching environments. Finally, xolotl is written in a modular fashion, includes detailed tutorials and worked examples, and is freely available at https://github.com/sg-s/xolotl, enabling seamless integration into the workflows of other researchers.
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Affiliation(s)
- Srinivas Gorur-Shandilya
- Volen National Center for Complex Systems and Biology Department, Brandeis University, Waltham, MA, United States
| | - Alec Hoyland
- Volen National Center for Complex Systems and Biology Department, Brandeis University, Waltham, MA, United States
| | - Eve Marder
- Volen National Center for Complex Systems and Biology Department, Brandeis University, Waltham, MA, United States
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34
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Arkhipov A, Gouwens NW, Billeh YN, Gratiy S, Iyer R, Wei Z, Xu Z, Abbasi-Asl R, Berg J, Buice M, Cain N, da Costa N, de Vries S, Denman D, Durand S, Feng D, Jarsky T, Lecoq J, Lee B, Li L, Mihalas S, Ocker GK, Olsen SR, Reid RC, Soler-Llavina G, Sorensen SA, Wang Q, Waters J, Scanziani M, Koch C. Visual physiology of the layer 4 cortical circuit in silico. PLoS Comput Biol 2018; 14:e1006535. [PMID: 30419013 PMCID: PMC6258373 DOI: 10.1371/journal.pcbi.1006535] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 11/26/2018] [Accepted: 09/29/2018] [Indexed: 01/15/2023] Open
Abstract
Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations. How can we capture the incredible complexity of brain circuits in quantitative models, and what can such models teach us about mechanisms underlying brain activity? To answer these questions, we set out to build extensive, bio-realistic models of brain circuitry by employing systematic datasets on brain structure and function. Here we report the first modeling results of this project, focusing on the layer 4 of the primary visual cortex (V1) of the mouse. Our simulations reproduced a variety of experimental observations in response to a large battery of visual stimuli. The results elucidated circuit mechanisms determining patters of neuronal activity in layer 4 –in particular, the roles of feedforward thalamic inputs and specific patterns of intracortical connectivity in producing tuning of neuronal responses to the orientation of motion. Simplification of neuronal models led to specific deficiencies in reproducing experimental data, giving insights into how biological details contribute to various aspects of brain activity. To enable future development of more sophisticated models, we make the software code, the model, and simulation results publicly available.
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Affiliation(s)
- Anton Arkhipov
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Nathan W Gouwens
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Yazan N Billeh
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Sergey Gratiy
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Ramakrishnan Iyer
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Ziqiang Wei
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America
| | - Zihao Xu
- University of California San Diego, La Jolla, CA, United States of America
| | - Reza Abbasi-Asl
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Jim Berg
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Michael Buice
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Nicholas Cain
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Nuno da Costa
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Saskia de Vries
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Daniel Denman
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Severine Durand
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - David Feng
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Tim Jarsky
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Jérôme Lecoq
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Brian Lee
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Lu Li
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Stefan Mihalas
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Gabriel K Ocker
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Shawn R Olsen
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | | | - Staci A Sorensen
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Quanxin Wang
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Jack Waters
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Massimo Scanziani
- Howard Hughes Medical Institute and Department of Physiology, University of California San Francisco, San Francisco, California, United States of America
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, Washington, United States of America
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35
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Kalmbach BE, Buchin A, Long B, Close J, Nandi A, Miller JA, Bakken TE, Hodge RD, Chong P, de Frates R, Dai K, Maltzer Z, Nicovich PR, Keene CD, Silbergeld DL, Gwinn RP, Cobbs C, Ko AL, Ojemann JG, Koch C, Anastassiou CA, Lein ES, Ting JT. h-Channels Contribute to Divergent Intrinsic Membrane Properties of Supragranular Pyramidal Neurons in Human versus Mouse Cerebral Cortex. Neuron 2018; 100:1194-1208.e5. [PMID: 30392798 DOI: 10.1016/j.neuron.2018.10.012] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 09/05/2018] [Accepted: 10/05/2018] [Indexed: 12/18/2022]
Abstract
Gene expression studies suggest that differential ion channel expression contributes to differences in rodent versus human neuronal physiology. We tested whether h-channels more prominently contribute to the physiological properties of human compared to mouse supragranular pyramidal neurons. Single-cell/nucleus RNA sequencing revealed ubiquitous HCN1-subunit expression in excitatory neurons in human, but not mouse, supragranular layers. Using patch-clamp recordings, we found stronger h-channel-related membrane properties in supragranular pyramidal neurons in human temporal cortex, compared to mouse supragranular pyramidal neurons in temporal association area. The magnitude of these differences depended upon cortical depth and was largest in pyramidal neurons in deep L3. Additionally, pharmacologically blocking h-channels produced a larger change in membrane properties in human compared to mouse neurons. Finally, using biophysical modeling, we provide evidence that h-channels promote the transfer of theta frequencies from dendrite-to-soma in human L3 pyramidal neurons. Thus, h-channels contribute to between-species differences in a fundamental neuronal property.
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Affiliation(s)
- Brian E Kalmbach
- Allen Institute for Brain Science, Seattle, WA 98109, USA; Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA.
| | - Anatoly Buchin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Brian Long
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jennie Close
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Anirban Nandi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | | | - Peter Chong
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Kael Dai
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Zoe Maltzer
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - C Dirk Keene
- Department of Pathology, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Daniel L Silbergeld
- Department of Neurological Surgery, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Ryder P Gwinn
- Epilepsy Surgery and Functional Neurosurgery, Swedish Neuroscience Institute, Seattle, WA 98122, USA
| | - Charles Cobbs
- The Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA 98122, USA
| | - Andrew L Ko
- Department of Neurological Surgery, University of Washington School of Medicine, Seattle, WA 98195, USA; Regional Epilepsy Center at Harborview Medical Center, Seattle, WA 98104, USA
| | - Jeffrey G Ojemann
- Department of Neurological Surgery, University of Washington School of Medicine, Seattle, WA 98195, USA; Regional Epilepsy Center at Harborview Medical Center, Seattle, WA 98104, USA
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Costas A Anastassiou
- Allen Institute for Brain Science, Seattle, WA 98109, USA; Department of Neurology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA; Department of Neurological Surgery, University of Washington School of Medicine, Seattle, WA 98195, USA
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