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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] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Gratiy SL, Billeh YN, Dai K, Mitelut C, Feng D, Gouwens NW, Cain N, Koch C, Anastassiou CA, Arkhipov A. BioNet: A Python interface to NEURON for modeling large-scale networks. PLoS One 2018; 13:e0201630. [PMID: 30071069 PMCID: PMC6072024 DOI: 10.1371/journal.pone.0201630] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 07/18/2018] [Indexed: 01/07/2023] Open
Abstract
There is a significant interest in the neuroscience community in the development of large-scale network models that would integrate diverse sets of experimental data to help elucidate mechanisms underlying neuronal activity and computations. Although powerful numerical simulators (e.g., NEURON, NEST) exist, data-driven large-scale modeling remains challenging due to difficulties involved in setting up and running network simulations. We developed a high-level application programming interface (API) in Python that facilitates building large-scale biophysically detailed networks and simulating them with NEURON on parallel computer architecture. This tool, termed "BioNet", is designed to support a modular workflow whereby the description of a constructed model is saved as files that could be subsequently loaded for further refinement and/or simulation. The API supports both NEURON's built-in as well as user-defined models of cells and synapses. It is capable of simulating a variety of observables directly supported by NEURON (e.g., spikes, membrane voltage, intracellular [Ca++]), as well as plugging in modules for computing additional observables (e.g. extracellular potential). The high-level API platform obviates the time-consuming development of custom code for implementing individual models, and enables easy model sharing via standardized files. This tool will help refocus neuroscientists on addressing outstanding scientific questions rather than developing narrow-purpose modeling code.
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Affiliation(s)
| | | | - Kael Dai
- Allen Institute, Seattle, WA, United States of America
| | | | - David Feng
- Allen Institute, Seattle, WA, United States of America
| | | | - Nicholas Cain
- Allen Institute, Seattle, WA, United States of America
| | - Christof Koch
- Allen Institute, Seattle, WA, United States of America
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Jun JJ, Steinmetz NA, Siegle JH, Denman DJ, Bauza M, Barbarits B, Lee AK, Anastassiou CA, Andrei A, Aydın Ç, Barbic M, Blanche TJ, Bonin V, Couto J, Dutta B, Gratiy SL, Gutnisky DA, Häusser M, Karsh B, Ledochowitsch P, Lopez CM, Mitelut C, Musa S, Okun M, Pachitariu M, Putzeys J, Rich PD, Rossant C, Sun WL, Svoboda K, Carandini M, Harris KD, Koch C, O'Keefe J, Harris TD. Fully integrated silicon probes for high-density recording of neural activity. Nature 2017; 551:232-236. [PMID: 29120427 PMCID: PMC5955206 DOI: 10.1038/nature24636] [Citation(s) in RCA: 951] [Impact Index Per Article: 135.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 10/16/2017] [Indexed: 12/24/2022]
Abstract
Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures. Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution, but from only a few dozen neurons per shank. Optical Ca2+ imaging offers more coverage but lacks the temporal resolution needed to distinguish individual spikes reliably and does not measure local field potentials. Until now, no technology compatible with use in unrestrained animals has combined high spatiotemporal resolution with large volume coverage. Here we design, fabricate and test a new silicon probe known as Neuropixels to meet this need. Each probe has 384 recording channels that can programmably address 960 complementary metal-oxide-semiconductor (CMOS) processing-compatible low-impedance TiN sites that tile a single 10-mm long, 70 × 20-μm cross-section shank. The 6 × 9-mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed and digitized on the base, allowing the direct transmission of noise-free digital data from the probe. The combination of dense recording sites and high channel count yielded well-isolated spiking activity from hundreds of neurons per probe implanted in mice and rats. Using two probes, more than 700 well-isolated single neurons were recorded simultaneously from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens a path towards recording of brain-wide neural activity during behaviour.
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Affiliation(s)
- James J. Jun
- HHMI Janelia Research Campus, 19700 Helix Dr., Ashburn, VA 20147
| | - Nicholas A. Steinmetz
- UCL Institute of Neurology, University College London, London WC1N 3BG, UK
- Department of Neuroscience, Physiology and Pharmacology, University College London, London WC1E 6DE, UK
- UCL Institute of Ophthalmology, University College London, London EC1V 9EL, UK
| | - Joshua H. Siegle
- Allen Institute for Brain Science, 615 Westlake Ave N, Seattle, WA 98109
| | - Daniel J. Denman
- Allen Institute for Brain Science, 615 Westlake Ave N, Seattle, WA 98109
| | - Marius Bauza
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
- Sainsbury Wellcome Center, University College London, London W1T 4JG, UK
| | - Brian Barbarits
- HHMI Janelia Research Campus, 19700 Helix Dr., Ashburn, VA 20147
| | - Albert K. Lee
- HHMI Janelia Research Campus, 19700 Helix Dr., Ashburn, VA 20147
| | | | | | - Çağatay Aydın
- Neuro-Electronics Research Flanders, Kapeldreef 75, 3001 Leuven Belgium
- KU Leuven, Department of Biology, Naamsestraat 59, 3000 Leuven, Belgium
| | - Mladen Barbic
- HHMI Janelia Research Campus, 19700 Helix Dr., Ashburn, VA 20147
| | - Timothy J. Blanche
- Allen Institute for Brain Science, 615 Westlake Ave N, Seattle, WA 98109
- White Matter LLC, Seattle, USA
| | - Vincent Bonin
- imec, Kapeldreef 75, 3001 Heverlee, Leuven Belgium
- Neuro-Electronics Research Flanders, Kapeldreef 75, 3001 Leuven Belgium
- KU Leuven, Department of Biology, Naamsestraat 59, 3000 Leuven, Belgium
- VIB, 3001 Leuven, Belgium
| | - João Couto
- Neuro-Electronics Research Flanders, Kapeldreef 75, 3001 Leuven Belgium
- KU Leuven, Department of Biology, Naamsestraat 59, 3000 Leuven, Belgium
| | | | - Sergey L. Gratiy
- Allen Institute for Brain Science, 615 Westlake Ave N, Seattle, WA 98109
| | | | - Michael Häusser
- Department of Neuroscience, Physiology and Pharmacology, University College London, London WC1E 6DE, UK
- Wolfson Institute for Biomedical Research, University College London, Gower Street, London WC1E 6BT, UK
| | - Bill Karsh
- HHMI Janelia Research Campus, 19700 Helix Dr., Ashburn, VA 20147
| | | | | | - Catalin Mitelut
- Allen Institute for Brain Science, 615 Westlake Ave N, Seattle, WA 98109
| | - Silke Musa
- imec, Kapeldreef 75, 3001 Heverlee, Leuven Belgium
| | - Michael Okun
- UCL Institute of Neurology, University College London, London WC1N 3BG, UK
- Department of Neuroscience, Physiology and Pharmacology, University College London, London WC1E 6DE, UK
- Centre for Systems Neuroscience, University of Leicester, Leicester LE1 7QR, UK
| | - Marius Pachitariu
- UCL Institute of Neurology, University College London, London WC1N 3BG, UK
- Department of Neuroscience, Physiology and Pharmacology, University College London, London WC1E 6DE, UK
| | - Jan Putzeys
- imec, Kapeldreef 75, 3001 Heverlee, Leuven Belgium
| | - P. Dylan Rich
- HHMI Janelia Research Campus, 19700 Helix Dr., Ashburn, VA 20147
| | - Cyrille Rossant
- UCL Institute of Neurology, University College London, London WC1N 3BG, UK
- Department of Neuroscience, Physiology and Pharmacology, University College London, London WC1E 6DE, UK
| | - Wei-lung Sun
- HHMI Janelia Research Campus, 19700 Helix Dr., Ashburn, VA 20147
| | - Karel Svoboda
- HHMI Janelia Research Campus, 19700 Helix Dr., Ashburn, VA 20147
| | - Matteo Carandini
- UCL Institute of Ophthalmology, University College London, London EC1V 9EL, UK
| | - Kenneth D. Harris
- UCL Institute of Neurology, University College London, London WC1N 3BG, UK
- Department of Neuroscience, Physiology and Pharmacology, University College London, London WC1E 6DE, UK
| | - Christof Koch
- Allen Institute for Brain Science, 615 Westlake Ave N, Seattle, WA 98109
| | - John O'Keefe
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
- Sainsbury Wellcome Center, University College London, London W1T 4JG, UK
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Gratiy SL, Halnes G, Denman D, Hawrylycz MJ, Koch C, Einevoll GT, Anastassiou CA. From Maxwell's equations to the theory of current-source density analysis. Eur J Neurosci 2017; 45:1013-1023. [PMID: 28177156 PMCID: PMC5413824 DOI: 10.1111/ejn.13534] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Revised: 01/17/2017] [Accepted: 01/30/2017] [Indexed: 12/31/2022]
Abstract
Despite the widespread use of current‐source density (CSD) analysis of extracellular potential recordings in the brain, the physical mechanisms responsible for the generation of the signal are still debated. While the extracellular potential is thought to be exclusively generated by the transmembrane currents, recent studies suggest that extracellular diffusive, advective and displacement currents—traditionally neglected—may also contribute considerably toward extracellular potential recordings. Here, we first justify the application of the electro‐quasistatic approximation of Maxwell's equations to describe the electromagnetic field of physiological origin. Subsequently, we perform spatial averaging of currents in neural tissue to arrive at the notion of the CSD and derive an equation relating it to the extracellular potential. We show that, in general, the extracellular potential is determined by the CSD of membrane currents as well as the gradients of the putative extracellular diffusion current. The diffusion current can contribute significantly to the extracellular potential at frequencies less than a few Hertz; in which case it must be subtracted to obtain correct CSD estimates. We also show that the advective and displacement currents in the extracellular space are negligible for physiological frequencies while, within cellular membrane, displacement current contributes toward the CSD as a capacitive current. Taken together, these findings elucidate the relationship between electric currents and the extracellular potential in brain tissue and form the necessary foundation for the analysis of extracellular recordings.
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Affiliation(s)
| | - Geir Halnes
- Faculty of Science and Technology, Norwegian University of Life Sciences, Aas, Norway
| | - Daniel Denman
- Allen Institute for Brain Science, Seattle, WA, 98109, USA
| | | | - Christof Koch
- Allen Institute for Brain Science, Seattle, WA, 98109, USA
| | - Gaute T Einevoll
- Faculty of Science and Technology, Norwegian University of Life Sciences, Aas, Norway.,Department of Physics, University of Oslo, Oslo, Norway
| | - Costas A Anastassiou
- Allen Institute for Brain Science, Seattle, WA, 98109, USA.,Department of Neurology, University of British Columbia, Vancouver, BC, Canada
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Gratiy SL, Pettersen KH, Einevoll GT, Dale AM. Pitfalls in the interpretation of multielectrode data: on the infeasibility of the neuronal current-source monopoles. J Neurophysiol 2013; 109:1681-2. [PMID: 23503556 DOI: 10.1152/jn.01047.2012] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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Reznichenko L, Cheng Q, Nizar K, Gratiy SL, Saisan PA, Rockenstein EM, González T, Patrick C, Spencer B, Desplats P, Dale AM, Devor A, Masliah E. Increased Calcium Influx and Decreased Buffering Capacity of Intracellular Stores Underlie Neuropathology Induced by Over-Expression of α-Synuclein. Biophys J 2012. [DOI: 10.1016/j.bpj.2011.11.2321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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Gratiy SL, Devor A, Einevoll GT, Dale AM. Estimation of Population-Specific Synaptic Currents from Laminar Multielectrode Local Field Potential Recordings. Biophys J 2012. [DOI: 10.1016/j.bpj.2011.11.2979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022] Open
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Gratiy SL, Devor A, Einevoll GT, Dale AM. On the estimation of population-specific synaptic currents from laminar multielectrode recordings. Front Neuroinform 2011; 5:32. [PMID: 22203801 PMCID: PMC3243925 DOI: 10.3389/fninf.2011.00032] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2011] [Accepted: 11/21/2011] [Indexed: 11/21/2022] Open
Abstract
Multielectrode array recordings of extracellular electrical field potentials along the depth axis of the cerebral cortex are gaining popularity as an approach for investigating the activity of cortical neuronal circuits. The low-frequency band of extracellular potential, i.e., the local field potential (LFP), is assumed to reflect synaptic activity and can be used to extract the laminar current source density (CSD) profile. However, physiological interpretation of the CSD profile is uncertain because it does not disambiguate synaptic inputs from passive return currents and does not identify population-specific contributions to the signal. These limitations prevent interpretation of the CSD in terms of synaptic functional connectivity in the columnar microcircuit. Here we present a novel anatomically informed model for decomposing the LFP signal into population-specific contributions and for estimating the corresponding activated synaptic projections. This involves a linear forward model, which predicts the population-specific laminar LFP in response to synaptic inputs applied at different positions along each population and a linear inverse model, which reconstructs laminar profiles of synaptic inputs from laminar LFP data based on the forward model. Assuming spatially smooth synaptic inputs within individual populations, the model decomposes the columnar LFP into population-specific contributions and estimates the corresponding laminar profiles of synaptic input as a function of time. It should be noted that constant synaptic currents at all positions along a neuronal population cannot be reconstructed, as this does not result in a change in extracellular potential. However, constraining the solution using a priori knowledge of the spatial distribution of synaptic connectivity provides the further advantage of estimating the strength of active synaptic projections from the columnar LFP profile thus fully specifying synaptic inputs.
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Affiliation(s)
- Sergey L Gratiy
- Department of Radiology, University of California San Diego La Jolla, CA, USA
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