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Hawrylycz M, Martone ME, Ascoli GA, Bjaalie JG, Dong HW, Ghosh SS, Gillis J, Hertzano R, Haynor DR, Hof PR, Kim Y, Lein E, Liu Y, Miller JA, Mitra PP, Mukamel E, Ng L, Osumi-Sutherland D, Peng H, Ray PL, Sanchez R, Regev A, Ropelewski A, Scheuermann RH, Tan SZK, Thompson CL, Tickle T, Tilgner H, Varghese M, Wester B, White O, Zeng H, Aevermann B, Allemang D, Ament S, Athey TL, Baker C, Baker KS, Baker PM, Bandrowski A, Banerjee S, Bishwakarma P, Carr A, Chen M, Choudhury R, Cool J, Creasy H, D’Orazi F, Degatano K, Dichter B, Ding SL, Dolbeare T, Ecker JR, Fang R, Fillion-Robin JC, Fliss TP, Gee J, Gillespie T, Gouwens N, Zhang GQ, Halchenko YO, Harris NL, Herb BR, Hintiryan H, Hood G, Horvath S, Huo B, Jarecka D, Jiang S, Khajouei F, Kiernan EA, Kir H, Kruse L, Lee C, Lelieveldt B, Li Y, Liu H, Liu L, Markuhar A, Mathews J, Mathews KL, Mezias C, Miller MI, Mollenkopf T, Mufti S, Mungall CJ, Orvis J, Puchades MA, Qu L, Receveur JP, Ren B, Sjoquist N, Staats B, Tward D, van Velthoven CTJ, Wang Q, Xie F, Xu H, Yao Z, Yun Z, Zhang YR, Zheng WJ, Zingg B. A guide to the BRAIN Initiative Cell Census Network data ecosystem. PLoS Biol 2023; 21:e3002133. [PMID: 37390046 PMCID: PMC10313015 DOI: 10.1371/journal.pbio.3002133] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [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] [Indexed: 07/02/2023] Open
Abstract
Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.
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Affiliation(s)
- Michael Hawrylycz
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Maryann E. Martone
- Department of Neuroscience, University of California San Diego, San Diego, California, United States of America
- San Francisco Veterans Affairs Medical Center, San Francisco, California, United States of America
| | - Giorgio A. Ascoli
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, Volgenau School of Engineering, George Mason University, Fairfax, Virginia, United States of America
| | - Jan G. Bjaalie
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Hong-Wei Dong
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at University of California, Los Angeles, California, United States of America
| | - Satrajit S. Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Jesse Gillis
- Department of Physiology, University of Toronto, Toronto, Ontario, Canada
| | - Ronna Hertzano
- Department of Otorhinolaryngology Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
- Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - David R. Haynor
- Department of Radiology, University of Washington, Seattle, Washington, United States of America
| | - Patrick R. Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania, United States of America
| | - Ed Lein
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Yufeng Liu
- SEU-Allen Institute Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu Province, China
| | - Jeremy A. Miller
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Partha P. Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Eran Mukamel
- Department of Cognitive Science, University of California, San Diego, La Jolla, California, United States of America
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - David Osumi-Sutherland
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Hanchuan Peng
- SEU-Allen Institute Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu Province, China
| | - Patrick L. Ray
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Raymond Sanchez
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Aviv Regev
- Genentech, South San Francisco, California, United States of America
| | - Alex Ropelewski
- Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | | | - Shawn Zheng Kai Tan
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Carol L. Thompson
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Timothy Tickle
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Hagen Tilgner
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, United States of America
| | - Merina Varghese
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Brock Wester
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America
| | - Owen White
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Brian Aevermann
- Chan Zuckerberg Initiative, Redwood City, California, United States of America
| | - David Allemang
- Kitware Inc., Albany, New York, United States of America
| | - Seth Ament
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Thomas L. Athey
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Cody Baker
- CatalystNeuro, Benicia, California, United States of America
| | - Katherine S. Baker
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Pamela M. Baker
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Anita Bandrowski
- Department of Neuroscience, University of California San Diego, San Diego, California, United States of America
| | - Samik Banerjee
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Prajal Bishwakarma
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Ambrose Carr
- Chan Zuckerberg Initiative, Redwood City, California, United States of America
| | - Min Chen
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Roni Choudhury
- Kitware Inc., Albany, New York, United States of America
| | - Jonah Cool
- Chan Zuckerberg Initiative, Redwood City, California, United States of America
| | - Heather Creasy
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Florence D’Orazi
- Chan Zuckerberg Initiative, Redwood City, California, United States of America
| | - Kylee Degatano
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | | | - Song-Lin Ding
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Tim Dolbeare
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Joseph R. Ecker
- Genomic Analysis Laboratory, Howard Hughes Medical Institute, The Salk Institute for Biological Studies La Jolla, California, United States of America
| | - Rongxin Fang
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, California, United States of America
| | | | - Timothy P. Fliss
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - James Gee
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Tom Gillespie
- Department of Neuroscience, University of California San Diego, San Diego, California, United States of America
| | - Nathan Gouwens
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Guo-Qiang Zhang
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Yaroslav O. Halchenko
- Department of Psychological and Brain Sciences, Dartmouth College, Hannover, New Hampshire, United States of America
| | - Nomi L. Harris
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Brian R. Herb
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Houri Hintiryan
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at University of California, Los Angeles, California, United States of America
| | - Gregory Hood
- Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Sam Horvath
- Kitware Inc., Albany, New York, United States of America
| | - Bingxing Huo
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Dorota Jarecka
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Shengdian Jiang
- SEU-Allen Institute Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu Province, China
| | - Farzaneh Khajouei
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Elizabeth A. Kiernan
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Huseyin Kir
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Lauren Kruse
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Changkyu Lee
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Boudewijn Lelieveldt
- Department of Intelligent Systems, Delft University of Technology, Delft, the Netherlands
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Yang Li
- Center for Epigenomics, Department of Cellular and Molecular Medicine, UC San Diego School of Medicine, La Jolla, California, United States of America
| | - Hanqing Liu
- Genomic Analysis Laboratory, Howard Hughes Medical Institute, The Salk Institute for Biological Studies La Jolla, California, United States of America
| | - Lijuan Liu
- SEU-Allen Institute Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu Province, China
| | - Anup Markuhar
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - James Mathews
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Kaylee L. Mathews
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Chris Mezias
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Michael I. Miller
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Tyler Mollenkopf
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Shoaib Mufti
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Christopher J. Mungall
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Joshua Orvis
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Maja A. Puchades
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Lei Qu
- SEU-Allen Institute Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu Province, China
| | - Joseph P. Receveur
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Bing Ren
- Center for Epigenomics, Department of Cellular and Molecular Medicine, UC San Diego School of Medicine, La Jolla, California, United States of America
- Ludwig Institute for Cancer Research, La Jolla, California, United States of America
| | - Nathan Sjoquist
- Microsoft Corporation, Seattle, Washington, United States of America
| | - Brian Staats
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Daniel Tward
- UCLA Brain Mapping Center, University of California, Los Angeles, California, United States of America
| | | | - Quanxin Wang
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Fangming Xie
- Department of Chemistry and Biochemistry, University of California Los Angeles, California, United States of America
| | - Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Zhixi Yun
- SEU-Allen Institute Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu Province, China
| | - Yun Renee Zhang
- J. Craig Venter Institute, La Jolla, California, United States of America
| | - W. Jim Zheng
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Brian Zingg
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at University of California, Los Angeles, California, United States of America
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2
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Hahn JD, Gao L, Boesen T, Gou L, Hintiryan H, Dong HW. Macroscale connections of the mouse lateral preoptic area and anterior lateral hypothalamic area. J Comp Neurol 2022; 530:2254-2285. [PMID: 35579973 PMCID: PMC9283274 DOI: 10.1002/cne.25331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 10/01/2021] [Revised: 04/05/2022] [Accepted: 04/07/2022] [Indexed: 11/25/2022]
Abstract
The macroscale neuronal connections of the lateral preoptic area (LPO) and the caudally adjacent lateral hypothalamic area anterior region (LHAa) were investigated in mice by anterograde and retrograde axonal tracing. Both hypothalamic regions are highly and diversely connected, with connections to >200 gray matter regions spanning the forebrain, midbrain, and rhombicbrain. Intrahypothalamic connections predominate, followed by connections with the cerebral cortex and cerebral nuclei. A similar overall pattern of LPO and LHAa connections contrasts with substantial differences between their input and output connections. Strongest connections include outputs to the lateral habenula, medial septal and diagonal band nuclei, and inputs from rostral and caudal lateral septal nuclei; however, numerous additional robust connections were also observed. The results are discussed in relation to a current model for the mammalian forebrain network that associates LPO and LHAa with a range of functional roles, including reward prediction, innate survival behaviors (including integrated somatomotor and physiological control), and affect. The present data suggest a broad and intricate role for LPO and LHAa in behavioral control, similar in that regard to previously investigated LHA regions, contributing to the finely tuned sensory‐motor integration that is necessary for behavioral guidance supporting survival and reproduction.
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Affiliation(s)
- Joel D Hahn
- Department of Biological Sciences, University of Southern California, Los Angeles, California, USA
| | - Lei Gao
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Tyler Boesen
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Lin Gou
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Houri Hintiryan
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Hong-Wei Dong
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
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3
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Hintiryan H, Dong HW. Brain Networks of Connectionally Unique Basolateral Amygdala Cell Types. Neurosci Insights 2022; 17:26331055221080175. [PMID: 35252870 PMCID: PMC8891918 DOI: 10.1177/26331055221080175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 01/27/2022] [Indexed: 11/25/2022] Open
Abstract
Different brain regions structurally interconnected through networks regulate behavior output. Therefore, understanding the functional organization of the brain in health and disease necessitates a foundational anatomic roadmap to its network organization. To provide this to the research community, our lab has systematically traced thousands of pathways in the mouse brain and has applied computational measures to determine the network architecture of major brain systems. Toward this effort, the brain-wide networks of the basolateral amygdalar complex (BLA) were recently generated. The data revealed uniquely connected cell types within the same BLA nucleus that were constituents of distinct neural networks. Here, we elaborate on how these connectionally unique BLA cell types fit within the larger cortico-basal ganglia and limbic networks that were previously described by our team. The significance and utility of high quality, detailed anatomic data is also discussed.
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Affiliation(s)
- Houri Hintiryan
- Department of Neurobiology, Brain Research & Artificial Intelligence Nexus (B.R.A.I.N), University of California, Los Angeles (UCLA), Los Angeles, CA, USA
| | - Hong-Wei Dong
- Department of Neurobiology, Brain Research & Artificial Intelligence Nexus (B.R.A.I.N), University of California, Los Angeles (UCLA), Los Angeles, CA, USA
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4
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Foster NN, Barry J, Korobkova L, Garcia L, Gao L, Becerra M, Sherafat Y, Peng B, Li X, Choi JH, Gou L, Zingg B, Azam S, Lo D, Khanjani N, Zhang B, Stanis J, Bowman I, Cotter K, Cao C, Yamashita S, Tugangui A, Li A, Jiang T, Jia X, Feng Z, Aquino S, Mun HS, Zhu M, Santarelli A, Benavidez NL, Song M, Dan G, Fayzullina M, Ustrell S, Boesen T, Johnson DL, Xu H, Bienkowski MS, Yang XW, Gong H, Levine MS, Wickersham I, Luo Q, Hahn JD, Lim BK, Zhang LI, Cepeda C, Hintiryan H, Dong HW. The mouse cortico-basal ganglia-thalamic network. Nature 2021; 598:188-194. [PMID: 34616074 PMCID: PMC8494639 DOI: 10.1038/s41586-021-03993-3] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 09/03/2021] [Indexed: 12/05/2022]
Abstract
The cortico–basal ganglia–thalamo–cortical loop is one of the fundamental network motifs in the brain. Revealing its structural and functional organization is critical to understanding cognition, sensorimotor behaviour, and the natural history of many neurological and neuropsychiatric disorders. Classically, this network is conceptualized to contain three information channels: motor, limbic and associative1–4. Yet this three-channel view cannot explain the myriad functions of the basal ganglia. We previously subdivided the dorsal striatum into 29 functional domains on the basis of the topography of inputs from the entire cortex5. Here we map the multi-synaptic output pathways of these striatal domains through the globus pallidus external part (GPe), substantia nigra reticular part (SNr), thalamic nuclei and cortex. Accordingly, we identify 14 SNr and 36 GPe domains and a direct cortico-SNr projection. The striatonigral direct pathway displays a greater convergence of striatal inputs than the more parallel striatopallidal indirect pathway, although direct and indirect pathways originating from the same striatal domain ultimately converge onto the same postsynaptic SNr neurons. Following the SNr outputs, we delineate six domains in the parafascicular and ventromedial thalamic nuclei. Subsequently, we identify six parallel cortico–basal ganglia–thalamic subnetworks that sequentially transduce specific subsets of cortical information through every elemental node of the cortico–basal ganglia–thalamic loop. Thalamic domains relay this output back to the originating corticostriatal neurons of each subnetwork in a bona fide closed loop. Mesoscale connectomic mapping of the cortico–basal ganglia–thalamic network reveals key architectural and information processing features.
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Affiliation(s)
- Nicholas N Foster
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. .,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Joshua Barry
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Laura Korobkova
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Luis Garcia
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Lei Gao
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marlene Becerra
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yasmine Sherafat
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Bo Peng
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Jun-Hyeok Choi
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Lin Gou
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Brian Zingg
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sana Azam
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Darrick Lo
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Neda Khanjani
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Bin Zhang
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jim Stanis
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ian Bowman
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kaelan Cotter
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chunru Cao
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Seita Yamashita
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Amanda Tugangui
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai, China
| | - Tao Jiang
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Xueyan Jia
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Zhao Feng
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Sarvia Aquino
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hyun-Seung Mun
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Muye Zhu
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Anthony Santarelli
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Nora L Benavidez
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Monica Song
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Gordon Dan
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marina Fayzullina
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarah Ustrell
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Tyler Boesen
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - David L Johnson
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hanpeng Xu
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Michael S Bienkowski
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - X William Yang
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience, Los Angeles, CA, USA
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai, China
| | - Michael S Levine
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Ian Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China.,School of Biomedical Engineering, Hainan University, Haikou, China
| | - Joel D Hahn
- Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Byung Kook Lim
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Li I Zhang
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Carlos Cepeda
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Houri Hintiryan
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hong-Wei Dong
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. .,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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5
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Muñoz-Castañeda R, Zingg B, Matho KS, Chen X, Wang Q, Foster NN, Li A, Narasimhan A, Hirokawa KE, Huo B, Bannerjee S, Korobkova L, Park CS, Park YG, Bienkowski MS, Chon U, Wheeler DW, Li X, Wang Y, Naeemi M, Xie P, Liu L, Kelly K, An X, Attili SM, Bowman I, Bludova A, Cetin A, Ding L, Drewes R, D'Orazi F, Elowsky C, Fischer S, Galbavy W, Gao L, Gillis J, Groblewski PA, Gou L, Hahn JD, Hatfield JT, Hintiryan H, Huang JJ, Kondo H, Kuang X, Lesnar P, Li X, Li Y, Lin M, Lo D, Mizrachi J, Mok S, Nicovich PR, Palaniswamy R, Palmer J, Qi X, Shen E, Sun YC, Tao HW, Wakemen W, Wang Y, Yao S, Yuan J, Zhan H, Zhu M, Ng L, Zhang LI, Lim BK, Hawrylycz M, Gong H, Gee JC, Kim Y, Chung K, Yang XW, Peng H, Luo Q, Mitra PP, Zador AM, Zeng H, Ascoli GA, Josh Huang Z, Osten P, Harris JA, Dong HW. Cellular anatomy of the mouse primary motor cortex. Nature 2021; 598:159-166. [PMID: 34616071 PMCID: PMC8494646 DOI: 10.1038/s41586-021-03970-w] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 08/27/2021] [Indexed: 12/24/2022]
Abstract
An essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input-output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture.
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Affiliation(s)
| | - Brian Zingg
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Xiaoyin Chen
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Quanxin Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nicholas N Foster
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | | | - Karla E Hirokawa
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | - Bingxing Huo
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Laura Korobkova
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Chris Sin Park
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Young-Gyun Park
- Institute for Medical Engineering and Science, Department of Chemical Engineering, Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Michael S Bienkowski
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Uree Chon
- Department of Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, USA
| | - Diek W Wheeler
- Center for Neural Informatics, Structures and Plasticity, Bioengineering Department and Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Yun Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Peng Xie
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Lijuan Liu
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Kathleen Kelly
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xu An
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Sarojini M Attili
- Center for Neural Informatics, Structures and Plasticity, Bioengineering Department and Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Ian Bowman
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Ali Cetin
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Liya Ding
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Rhonda Drewes
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Corey Elowsky
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | | | - Lei Gao
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Jesse Gillis
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Lin Gou
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Joel D Hahn
- Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Joshua T Hatfield
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Houri Hintiryan
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Junxiang Jason Huang
- Center for Neural Circuits and Sensory Processing Disorders, Zilkha Neurogenetics Institute (ZNI), Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hideki Kondo
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xiuli Kuang
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | - Xu Li
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Yaoyao Li
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Mengkuan Lin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Darrick Lo
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | | | - Philip R Nicovich
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | | | - Jason Palmer
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xiaoli Qi
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Elise Shen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Yu-Chi Sun
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Huizhong W Tao
- Center for Neural Circuits and Sensory Processing Disorders, Zilkha Neurogenetics Institute (ZNI), Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Yimin Wang
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jing Yuan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Huiqing Zhan
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Muye Zhu
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Li I Zhang
- Center for Neural Circuits and Sensory Processing Disorders, Zilkha Neurogenetics Institute (ZNI), Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Byung Kook Lim
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
- Division of Biological Science, Neurobiology section, University of California San Diego, San Diego, CA, USA
| | | | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - James C Gee
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, USA
| | - Kwanghun Chung
- Institute for Medical Engineering and Science, Department of Chemical Engineering, Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - X William Yang
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Hanchuan Peng
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Partha P Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures and Plasticity, Bioengineering Department and Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA.
| | - Z Josh Huang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA.
| | - Pavel Osten
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA, USA.
- Cajal Neuroscience, Seattle, WA, USA.
| | - Hong-Wei Dong
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
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6
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Callaway EM, Dong HW, Ecker JR, Hawrylycz MJ, Huang ZJ, Lein ES, Ngai J, Osten P, Ren B, Tolias AS, White O, Zeng H, Zhuang X, Ascoli GA, Behrens MM, Chun J, Feng G, Gee JC, Ghosh SS, Halchenko YO, Hertzano R, Lim BK, Martone ME, Ng L, Pachter L, Ropelewski AJ, Tickle TL, Yang XW, Zhang K, Bakken TE, Berens P, Daigle TL, Harris JA, Jorstad NL, Kalmbach BE, Kobak D, Li YE, Liu H, Matho KS, Mukamel EA, Naeemi M, Scala F, Tan P, Ting JT, Xie F, Zhang M, Zhang Z, Zhou J, Zingg B, Armand E, Yao Z, Bertagnolli D, Casper T, Crichton K, Dee N, Diep D, Ding SL, Dong W, Dougherty EL, Fong O, Goldman M, Goldy J, Hodge RD, Hu L, Keene CD, Krienen FM, Kroll M, Lake BB, Lathia K, Linnarsson S, Liu CS, Macosko EZ, McCarroll SA, McMillen D, Nadaf NM, Nguyen TN, Palmer CR, Pham T, Plongthongkum N, Reed NM, Regev A, Rimorin C, Romanow WJ, Savoia S, Siletti K, Smith K, Sulc J, Tasic B, Tieu M, Torkelson A, Tung H, van Velthoven CTJ, Vanderburg CR, Yanny AM, Fang R, Hou X, Lucero JD, Osteen JK, Pinto-Duarte A, Poirion O, Preissl S, Wang X, Aldridge AI, Bartlett A, Boggeman L, O’Connor C, Castanon RG, Chen H, Fitzpatrick C, Luo C, Nery JR, Nunn M, Rivkin AC, Tian W, Dominguez B, Ito-Cole T, Jacobs M, Jin X, Lee CT, Lee KF, Miyazaki PA, Pang Y, Rashid M, Smith JB, Vu M, Williams E, Biancalani T, Booeshaghi AS, Crow M, Dudoit S, Fischer S, Gillis J, Hu Q, Kharchenko PV, Niu SY, Ntranos V, Purdom E, Risso D, de Bézieux HR, Somasundaram S, Street K, Svensson V, Vaishnav ED, Van den Berge K, Welch JD, An X, Bateup HS, Bowman I, Chance RK, Foster NN, Galbavy W, Gong H, Gou L, Hatfield JT, Hintiryan H, Hirokawa KE, Kim G, Kramer DJ, Li A, Li X, Luo Q, Muñoz-Castañeda R, Stafford DA, Feng Z, Jia X, Jiang S, Jiang T, Kuang X, Larsen R, Lesnar P, Li Y, Li Y, Liu L, Peng H, Qu L, Ren M, Ruan Z, Shen E, Song Y, Wakeman W, Wang P, Wang Y, Wang Y, Yin L, Yuan J, Zhao S, Zhao X, Narasimhan A, Palaniswamy R, Banerjee S, Ding L, Huilgol D, Huo B, Kuo HC, Laturnus S, Li X, Mitra PP, Mizrachi J, Wang Q, Xie P, Xiong F, Yu Y, Eichhorn SW, Berg J, Bernabucci M, Bernaerts Y, Cadwell CR, Castro JR, Dalley R, Hartmanis L, Horwitz GD, Jiang X, Ko AL, Miranda E, Mulherkar S, Nicovich PR, Owen SF, Sandberg R, Sorensen SA, Tan ZH, Allen S, Hockemeyer D, Lee AY, Veldman MB, Adkins RS, Ament SA, Bravo HC, Carter R, Chatterjee A, Colantuoni C, Crabtree J, Creasy H, Felix V, Giglio M, Herb BR, Kancherla J, Mahurkar A, McCracken C, Nickel L, Olley D, Orvis J, Schor M, Hood G, Dichter B, Grauer M, Helba B, Bandrowski A, Barkas N, Carlin B, D’Orazi FD, Degatano K, Gillespie TH, Khajouei F, Konwar K, Thompson C, Kelly K, Mok S, Sunkin S. A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 2021; 598:86-102. [PMID: 34616075 PMCID: PMC8494634 DOI: 10.1038/s41586-021-03950-0] [Citation(s) in RCA: 205] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Accepted: 08/25/2021] [Indexed: 12/14/2022]
Abstract
Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1-5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.
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7
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Benavidez NL, Bienkowski MS, Zhu M, Garcia LH, Fayzullina M, Gao L, Bowman I, Gou L, Khanjani N, Cotter KR, Korobkova L, Becerra M, Cao C, Song MY, Zhang B, Yamashita S, Tugangui AJ, Zingg B, Rose K, Lo D, Foster NN, Boesen T, Mun HS, Aquino S, Wickersham IR, Ascoli GA, Hintiryan H, Dong HW. Organization of the inputs and outputs of the mouse superior colliculus. Nat Commun 2021; 12:4004. [PMID: 34183678 PMCID: PMC8239028 DOI: 10.1038/s41467-021-24241-2] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/02/2021] [Indexed: 11/16/2022] Open
Abstract
The superior colliculus (SC) receives diverse and robust cortical inputs to drive a range of cognitive and sensorimotor behaviors. However, it remains unclear how descending cortical input arising from higher-order associative areas coordinate with SC sensorimotor networks to influence its outputs. Here, we construct a comprehensive map of all cortico-tectal projections and identify four collicular zones with differential cortical inputs: medial (SC.m), centromedial (SC.cm), centrolateral (SC.cl) and lateral (SC.l). Further, we delineate the distinctive brain-wide input/output organization of each collicular zone, assemble multiple parallel cortico-tecto-thalamic subnetworks, and identify the somatotopic map in the SC that displays distinguishable spatial properties from the somatotopic maps in the neocortex and basal ganglia. Finally, we characterize interactions between those cortico-tecto-thalamic and cortico-basal ganglia-thalamic subnetworks. This study provides a structural basis for understanding how SC is involved in integrating different sensory modalities, translating sensory information to motor command, and coordinating different actions in goal-directed behaviors.
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Affiliation(s)
- Nora L Benavidez
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Michael S Bienkowski
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Muye Zhu
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Luis H Garcia
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Marina Fayzullina
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Lei Gao
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Ian Bowman
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Lin Gou
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Neda Khanjani
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kaelan R Cotter
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Laura Korobkova
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marlene Becerra
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chunru Cao
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Monica Y Song
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Bin Zhang
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Seita Yamashita
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Amanda J Tugangui
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Brian Zingg
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Kasey Rose
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
| | - Darrick Lo
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Nicholas N Foster
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Tyler Boesen
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Hyun-Seung Mun
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Sarvia Aquino
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ian R Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Giorgio A Ascoli
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Houri Hintiryan
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Hong-Wei Dong
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
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8
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Hintiryan H, Bowman I, Johnson DL, Korobkova L, Zhu M, Khanjani N, Gou L, Gao L, Yamashita S, Bienkowski MS, Garcia L, Foster NN, Benavidez NL, Song MY, Lo D, Cotter KR, Becerra M, Aquino S, Cao C, Cabeen RP, Stanis J, Fayzullina M, Ustrell SA, Boesen T, Tugangui AJ, Zhang ZG, Peng B, Fanselow MS, Golshani P, Hahn JD, Wickersham IR, Ascoli GA, Zhang LI, Dong HW. Connectivity characterization of the mouse basolateral amygdalar complex. Nat Commun 2021; 12:2859. [PMID: 34001873 PMCID: PMC8129205 DOI: 10.1038/s41467-021-22915-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 03/25/2021] [Indexed: 11/08/2022] Open
Abstract
The basolateral amygdalar complex (BLA) is implicated in behaviors ranging from fear acquisition to addiction. Optogenetic methods have enabled the association of circuit-specific functions to uniquely connected BLA cell types. Thus, a systematic and detailed connectivity profile of BLA projection neurons to inform granular, cell type-specific interrogations is warranted. Here, we apply machine-learning based computational and informatics analysis techniques to the results of circuit-tracing experiments to create a foundational, comprehensive BLA connectivity map. The analyses identify three distinct domains within the anterior BLA (BLAa) that house target-specific projection neurons with distinguishable morphological features. We identify brain-wide targets of projection neurons in the three BLAa domains, as well as in the posterior BLA, ventral BLA, posterior basomedial, and lateral amygdalar nuclei. Inputs to each nucleus also are identified via retrograde tracing. The data suggests that connectionally unique, domain-specific BLAa neurons are associated with distinct behavior networks.
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Affiliation(s)
- Houri Hintiryan
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
| | - Ian Bowman
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - David L Johnson
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Laura Korobkova
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Muye Zhu
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Neda Khanjani
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Lin Gou
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Lei Gao
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Seita Yamashita
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Michael S Bienkowski
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Luis Garcia
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Nicholas N Foster
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Nora L Benavidez
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Monica Y Song
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Darrick Lo
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Kaelan R Cotter
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Marlene Becerra
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarvia Aquino
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chunru Cao
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Ryan P Cabeen
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jim Stanis
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marina Fayzullina
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarah A Ustrell
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Tyler Boesen
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Amanda J Tugangui
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Zheng-Gang Zhang
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Physiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Bo Peng
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Michael S Fanselow
- Brain Research Institute, Department of Psychology, University of California, Los Angeles, CA, USA
| | - Peyman Golshani
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- West Los Angeles Veterans Administration Medical Center, Los Angeles, CA, USA
| | - Joel D Hahn
- Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Ian R Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Giorgio A Ascoli
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Li I Zhang
- Center for Neural Circuitry & Sensory Processing Disorders, Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hong-Wei Dong
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
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9
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Bienkowski MS, Sepehrband F, Kurniawan ND, Stanis J, Korobkova L, Khanjani N, Clark K, Hintiryan H, Miller CA, Dong HW. Homologous laminar organization of the mouse and human subiculum. Sci Rep 2021; 11:3729. [PMID: 33580088 PMCID: PMC7881248 DOI: 10.1038/s41598-021-81362-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 08/10/2020] [Indexed: 11/09/2022] Open
Abstract
The subiculum is the major output component of the hippocampal formation and one of the major brain structures most affected by Alzheimer's disease. Our previous work revealed a hidden laminar architecture within the mouse subiculum. However, the rotation of the hippocampal longitudinal axis across species makes it unclear how the laminar organization is represented in human subiculum. Using in situ hybridization data from the Allen Human Brain Atlas, we demonstrate that the human subiculum also contains complementary laminar gene expression patterns similar to the mouse. In addition, we provide evidence that the molecular domain boundaries in human subiculum correspond to microstructural differences observed in high resolution MRI and fiber density imaging. Finally, we show both similarities and differences in the gene expression profile of subiculum pyramidal cells within homologous lamina. Overall, we present a new 3D model of the anatomical organization of human subiculum and its evolution from the mouse.
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Affiliation(s)
- Michael S Bienkowski
- USC Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging (LONI), Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA. .,Zilkha Neurogenetic Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA.
| | - Farshid Sepehrband
- USC Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging (LONI), Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA.,Department of Neurology, Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA
| | - Nyoman D Kurniawan
- Center for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Jim Stanis
- USC Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging (LONI), Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA
| | - Laura Korobkova
- USC Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging (LONI), Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA
| | - Neda Khanjani
- USC Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging (LONI), Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA
| | - Kristi Clark
- Department of Neurology, Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA
| | - Houri Hintiryan
- USC Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging (LONI), Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA.,Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Carol A Miller
- Department of Pathology, Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA
| | - Hong-Wei Dong
- USC Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging (LONI), Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA. .,Zilkha Neurogenetic Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA. .,Department of Neurology, Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA. .,Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.
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10
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Hahn JD, Swanson LW, Bowman I, Foster NN, Zingg B, Bienkowski MS, Hintiryan H, Dong HW. An open access mouse brain flatmap and upgraded rat and human brain flatmaps based on current reference atlases. J Comp Neurol 2020; 529:576-594. [PMID: 32511750 DOI: 10.1002/cne.24966] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 05/22/2020] [Accepted: 05/22/2020] [Indexed: 12/11/2022]
Abstract
Here we present a flatmap of the mouse central nervous system (CNS) (brain) and substantially enhanced flatmaps of the rat and human brain. Also included are enhanced representations of nervous system white matter tracts, ganglia, and nerves, and an enhanced series of 10 flatmaps showing different stages of rat brain development. The adult mouse and rat brain flatmaps provide layered diagrammatic representation of CNS divisions, according to their arrangement in corresponding reference atlases: Brain Maps 4.0 (BM4, rat) (Swanson, The Journal of Comparative Neurology, 2018, 526, 935-943), and the first version of the Allen Reference Atlas (mouse) (Dong, The Allen reference atlas, (book + CD-ROM): A digital color brain atlas of the C57BL/6J male mouse, 2007). To facilitate comparative analysis, both flatmaps are scaled equally, and the divisional hierarchy of gray matter follows a topographic arrangement used in BM4. Also included with the mouse and rat brain flatmaps are cerebral cortex atlas level contours based on the reference atlases, and direct graphical and tabular comparison of regional parcellation. To encourage use of the brain flatmaps, they were designed and organized, with supporting reference tables, for ease-of-use and to be amenable to computational applications. We demonstrate how they can be adapted to represent novel parcellations resulting from experimental data, and we provide a proof-of-concept for how they could form the basis of a web-based graphical data viewer and analysis platform. The mouse, rat, and human brain flatmap vector graphics files (Adobe Reader/Acrobat viewable and Adobe Illustrator editable) and supporting tables are provided open access; they constitute a broadly applicable neuroscience toolbox resource for researchers seeking to map and perform comparative analysis of brain data.
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Affiliation(s)
- Joel D Hahn
- Department of Biological Sciences, University of Southern California, California, Los Angeles, USA.,Center for Integrated Connectomics (CIC), Keck School of Medicine of University of Southern California, University of Southern California Stevens Neuroimaging and Informatics Institute, Los Angeles, California, USA
| | - Larry W Swanson
- Department of Biological Sciences, University of Southern California, California, Los Angeles, USA
| | - Ian Bowman
- Center for Integrated Connectomics (CIC), Keck School of Medicine of University of Southern California, University of Southern California Stevens Neuroimaging and Informatics Institute, Los Angeles, California, USA
| | - Nicholas N Foster
- Center for Integrated Connectomics (CIC), Keck School of Medicine of University of Southern California, University of Southern California Stevens Neuroimaging and Informatics Institute, Los Angeles, California, USA
| | - Brian Zingg
- Center for Integrated Connectomics (CIC), Keck School of Medicine of University of Southern California, University of Southern California Stevens Neuroimaging and Informatics Institute, Los Angeles, California, USA
| | - Michael S Bienkowski
- Center for Integrated Connectomics (CIC), Keck School of Medicine of University of Southern California, University of Southern California Stevens Neuroimaging and Informatics Institute, Los Angeles, California, USA
| | - Houri Hintiryan
- Center for Integrated Connectomics (CIC), Keck School of Medicine of University of Southern California, University of Southern California Stevens Neuroimaging and Informatics Institute, Los Angeles, California, USA
| | - Hong-Wei Dong
- Center for Integrated Connectomics (CIC), Keck School of Medicine of University of Southern California, University of Southern California Stevens Neuroimaging and Informatics Institute, Los Angeles, California, USA.,Department of Neurology, Keck School of Medicine of University of Southern California, Los Angeles, California, USA.,Department of Physiology and Neuroscience, and Zilkha Neurogenetic Institute, Keck School of Medicine of University of Southern California, Los Angeles, California, USA
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11
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Bota M, Talpalaru Ş, Hintiryan H, Dong HW, Swanson LW. BAMS2 workspace: a comprehensive and versatile neuroinformatic platform for collating and processing neuroanatomical connections. J Comp Neurol 2014; 522:3160-76. [PMID: 24668342 PMCID: PMC4107155 DOI: 10.1002/cne.23592] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2013] [Revised: 03/17/2014] [Accepted: 03/21/2014] [Indexed: 01/19/2023]
Abstract
We describe a novel neuroinformatic platform, the BAMS2 Workspace (http://brancusi1.usc.edu), designed for storing and processing information on gray matter region axonal connections. This de novo constructed module allows registered users to collate their data directly by using a simple and versatile visual interface. It also allows construction and analysis of sets of connections associated with gray matter region nomenclatures from any designated species. The Workspace includes a set of tools allowing the display of data in matrix and networks formats and the uploading of processed information in visual, PDF, CSV, and Excel formats. Finally, the Workspace can be accessed anonymously by third-party systems to create individualized connectivity networks. All features of the BAMS2 Workspace are described in detail and are demonstrated with connectivity reports collated in BAMS and associated with the rat sensory-motor cortex, medial frontal cortex, and amygdalar regions.
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Affiliation(s)
- Mihail Bota
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089
| | | | - Houri Hintiryan
- Department of Neurology and Institute for Imaging and Informatics, University of Southern California, Los Angeles, CA 90089
| | - Hong-Wei Dong
- Department of Neurology and Institute for Imaging and Informatics, University of Southern California, Los Angeles, CA 90089
| | - Larry W. Swanson
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089
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12
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Zingg B, Hintiryan H, Gou L, Song MY, Bay M, Bienkowski MS, Foster NN, Yamashita S, Bowman I, Toga AW, Dong HW. Neural networks of the mouse neocortex. Cell 2014; 156:1096-111. [PMID: 24581503 DOI: 10.1016/j.cell.2014.02.023] [Citation(s) in RCA: 490] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2013] [Revised: 01/25/2014] [Accepted: 02/10/2014] [Indexed: 10/25/2022]
Abstract
Numerous studies have examined the neuronal inputs and outputs of many areas within the mammalian cerebral cortex, but how these areas are organized into neural networks that communicate across the entire cortex is unclear. Over 600 labeled neuronal pathways acquired from tracer injections placed across the entire mouse neocortex enabled us to generate a cortical connectivity atlas. A total of 240 intracortical connections were manually reconstructed within a common neuroanatomic framework, forming a cortico-cortical connectivity map that facilitates comparison of connections from different cortical targets. Connectivity matrices were generated to provide an overview of all intracortical connections and subnetwork clusterings. The connectivity matrices and cortical map revealed that the entire cortex is organized into four somatic sensorimotor, two medial, and two lateral subnetworks that display unique topologies and can interact through select cortical areas. Together, these data provide a resource that can be used to further investigate cortical networks and their corresponding functions.
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Affiliation(s)
- Brian Zingg
- Zilkha Neurogenetic Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Houri Hintiryan
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Lin Gou
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Monica Y Song
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Maxwell Bay
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Michael S Bienkowski
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Nicholas N Foster
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Seita Yamashita
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Ian Bowman
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Arthur W Toga
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA; Department of Neurology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Hong-Wei Dong
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA; Department of Neurology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA.
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13
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Hintiryan H, Gou L, Zingg B, Yamashita S, Lyden HM, Song MY, Grewal AK, Zhang X, Toga AW, Dong HW. Comprehensive connectivity of the mouse main olfactory bulb: analysis and online digital atlas. Front Neuroanat 2012; 6:30. [PMID: 22891053 PMCID: PMC3412993 DOI: 10.3389/fnana.2012.00030] [Citation(s) in RCA: 44] [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: 05/09/2012] [Accepted: 07/19/2012] [Indexed: 11/24/2022] Open
Abstract
We introduce the first open resource for mouse olfactory connectivity data produced as part of the Mouse Connectome Project (MCP) at UCLA. The MCP aims to assemble a whole-brain connectivity atlas for the C57Bl/6J mouse using a double coinjection tracing method. Each coinjection consists of one anterograde and one retrograde tracer, which affords the advantage of simultaneously identifying efferent and afferent pathways and directly identifying reciprocal connectivity of injection sites. The systematic application of double coinjections potentially reveals interaction stations between injections and allows for the study of connectivity at the network level. To facilitate use of the data, raw images are made publicly accessible through our online interactive visualization tool, the iConnectome, where users can view and annotate the high-resolution, multi-fluorescent connectivity data (www.MouseConnectome.org). Systematic double coinjections were made into different regions of the main olfactory bulb (MOB) and data from 18 MOB cases (~72 pathways; 36 efferent/36 afferent) currently are available to view in iConnectome within their corresponding atlas level and their own bright-field cytoarchitectural background. Additional MOB injections and injections of the accessory olfactory bulb (AOB), anterior olfactory nucleus (AON), and other olfactory cortical areas gradually will be made available. Analysis of connections from different regions of the MOB revealed a novel, topographically arranged MOB projection roadmap, demonstrated disparate MOB connectivity with anterior versus posterior piriform cortical area (PIR), and exposed some novel aspects of well-established cortical olfactory projections.
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Affiliation(s)
- Houri Hintiryan
- Laboratory of Neuro Imaging, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles Los Angeles, CA, USA
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14
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Biag J, Huang Y, Gou L, Hintiryan H, Askarinam A, Hahn JD, Toga AW, Dong HW. Cyto- and chemoarchitecture of the hypothalamic paraventricular nucleus in the C57BL/6J male mouse: A study of immunostaining and multiple fluorescent tract tracing. J Comp Neurol 2012. [DOI: 10.1002/cne.23002] [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: 11/09/2022]
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15
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Biag J, Huang Y, Gou L, Hintiryan H, Askarinam A, Hahn JD, Toga AW, Dong HW. Cyto- and chemoarchitecture of the hypothalamic paraventricular nucleus in the C57BL/6J male mouse: a study of immunostaining and multiple fluorescent tract tracing. J Comp Neurol 2012; 520:6-33. [PMID: 21674499 PMCID: PMC4104804 DOI: 10.1002/cne.22698] [Citation(s) in RCA: 148] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The paraventricular nucleus of the hypothalamus (PVH) plays a critical role in the regulation of autonomic, neuroendocrine, and behavioral activities. This understanding has come from extensive characterization of the PVH in rats, and for this mammalian species we now have a robust model of basic PVH neuroanatomy and function. However, in mice, whose use as a model research animal has burgeoned with the increasing sophistication of tools for genetic manipulation, a comparable level of PVH characterization has not been achieved. To address this, we employed a variety of fluorescent tract tracing and immunostaining techniques in several different combinations to determine the neuronal connections and cyto- and chemoarchitecture of the PVH in the commonly used C57BL/6J male mouse. Our findings reveal a distinct organization in the mouse PVH that is substantially different from the PVH of male rats. The differences are particularly evident with respect to the spatial relations of two principal neuroendocrine divisions (magnocellular and parvicellular) and three descending preautonomic populations in the PVH. We discuss these data in relation to what is known about PVH function and provide the work as a resource for further studies of the neuronal architecture and function of the mouse PVH.
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Affiliation(s)
- Jonathan Biag
- Laboratory of Neuroimaging, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095-7334
| | - Yi Huang
- Laboratory of Neuroimaging, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095-7334
| | - Lin Gou
- Laboratory of Neuroimaging, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095-7334
| | - Houri Hintiryan
- Laboratory of Neuroimaging, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095-7334
| | - Asal Askarinam
- Laboratory of Neuroimaging, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095-7334
| | - Joel D. Hahn
- Brain Architecture Center, University of Southern California, Los Angeles, California 90089-2520
| | - Arthur W. Toga
- Laboratory of Neuroimaging, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095-7334
| | - Hong-Wei Dong
- Laboratory of Neuroimaging, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095-7334
- Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, California 90095-7334
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16
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Almor A, Aronoff JM, MacDonald MC, Gonnerman LM, Kempler D, Hintiryan H, Hayes UL, Arunachalam S, Andersen ES. A common mechanism in verb and noun naming deficits in Alzheimer's patients. Brain Lang 2009; 111:8-19. [PMID: 19699513 PMCID: PMC2774798 DOI: 10.1016/j.bandl.2009.07.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2007] [Revised: 06/16/2009] [Accepted: 07/16/2009] [Indexed: 05/28/2023]
Abstract
We tested the ability of Alzheimer's patients and elderly controls to name living and non-living nouns, and manner and instrument verbs. Patients' error patterns and relative performance with different categories showed evidence of graceful degradation for both nouns and verbs, with particular domain-specific impairments for living nouns and instrument verbs. Our results support feature-based, semantic representations for nouns and verbs and support the role of inter-correlated features in noun impairment, and the role of noun knowledge in instrument verb impairment.
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Affiliation(s)
- Amit Almor
- University of South Carolina, Columbia, SC 29208, United States.
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17
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Hintiryan H, Foster NN, Chambers KC. Dissociating the conditioning and the anorectic effects of estradiol in female rats. Behav Neurosci 2009; 123:1226-37. [DOI: 10.1037/a0017701] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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18
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Abstract
Taste reactivity testing (TRT), which entails infusing a solution into the oral cavity of subjects, is used across a wide range of studies. For laboratories inexperienced in the conventional technique of implanting cheek fistulae, the surgery can be problematic for both the subjects and the experimenter. We have proposed a refined method for fistulae implantation that is less invasive, thereby reducing the pain and distress of the animals. Using this refined technique, we were able to replicate the findings of previous TRT studies, namely that a high dose of lithium chloride produces an increase in aversive and a decrease in ingestive orofacial and somatic responses. Using indices of health, we demonstrate that unlike animals with the conventional method of fistulae implantation, subjects that receive the refined technique regain their pre-surgery body weights rapidly and show no physical signs of discomfort. Additional advantages of the refined technique are discussed.
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Affiliation(s)
- Houri Hintiryan
- Department of Psychology, University of Southern California, Los Angeles, California, USA
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19
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Hintiryan H, Hayes UL, Chambers KC. The role of histamine in estradiol-induced conditioned consumption reductions. Physiol Behav 2005; 84:117-28. [PMID: 15642614 DOI: 10.1016/j.physbeh.2004.10.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2003] [Revised: 09/21/2004] [Accepted: 10/21/2004] [Indexed: 11/26/2022]
Abstract
Conditioned consumption reductions (CCRs) develop toward novel taste stimuli as a consequence of associating those tastes with certain physiological changes. Few studies have focused on the neurochemical basis of this learned behavior. The purpose of these experiments was to reexamine the role of histamine in CCRs elicited by estradiol. Previous studies have suggested that histamine mediates CCRs induced by radiation, centrifugal rotation, and estradiol. However, because the animals were trained in a drug state, but tested in a nondrug state, it is possible that state-dependent learning confounded the results of these studies. The following series of experiments was performed to test this possibility for estradiol-induced CCRs. Implementing our own methodologies in Experiment 1, we demonstrated that an estradiol-induced CCR was blocked by treatment with the histamine 1 receptor blocker, chlorpheniramine maleate, before sucrose consumption during acquisition. In Experiment 2, identical states were maintained during acquisition and extinction by administering chlorpheniramine prior to sucrose exposure during both phases. The results indicated that chlorpheniramine blocked the estradiol-induced CCR. However, circumventing state-dependency in Experiment 3 by administering chlorpheniramine following exposure to sucrose during acquisition augmented the estradiol CCR. Taken together, the results of these experiments suggest that the ability of chlorpheniramine to abolish estradiol-induced CCRs is not due to state-dependency or to the antihistaminergic properties of chlorpheniramine. It is proposed that the results of all of the experiments can be accounted for by the aversive properties of chlorpheniramine.
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Affiliation(s)
- Houri Hintiryan
- Department of Psychology, Seeley G. Mudd Building 501, University of Southern California, Los Angeles, CA 90089-1061, USA.
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