1
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Pailthorpe BA. Network analysis of marmoset cortical connections reveals pFC and sensory clusters. Front Neuroanat 2024; 18:1403170. [PMID: 38933918 PMCID: PMC11199858 DOI: 10.3389/fnana.2024.1403170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 05/10/2024] [Indexed: 06/28/2024] Open
Abstract
A new analysis is presented of the retrograde tracer measurements of connections between anatomical areas of the marmoset cortex. The original normalisation of raw data yields the fractional link weight measure, FLNe. That is re-examined to consider other possible measures that reveal the underlying in link weights. Predictions arising from both are used to examine network modules and hubs. With inclusion of the in weights the InfoMap algorithm identifies eight structural modules in marmoset cortex. In and out hubs and major connector nodes are identified using module assignment and participation coefficients. Time evolving network tracing around the major hubs reveals medium sized clusters in pFC, temporal, auditory and visual areas; the most tightly coupled and significant of which is in the pFC. A complementary viewpoint is provided by examining the highest traffic links in the cortical network, and reveals parallel sensory flows to pFC and via association areas to frontal areas.
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Affiliation(s)
- Bernard A. Pailthorpe
- Brain Dynamics Group, School of Physics, The University of Sydney, Sydney, NSW, Australia
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2
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Pailthorpe BA. Simulated dynamical transitions in a heterogeneous marmoset pFC cluster. Front Comput Neurosci 2024; 18:1398898. [PMID: 38863681 PMCID: PMC11165126 DOI: 10.3389/fncom.2024.1398898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 05/06/2024] [Indexed: 06/13/2024] Open
Abstract
Network analysis of the marmoset cortical connectivity data indicates a significant 3D cluster in and around the pre-frontal cortex. A multi-node, heterogeneous neural mass model of this six-node cluster was constructed. Its parameters were informed by available experimental and simulation data so that each neural mass oscillated in a characteristic frequency band. Nodes were connected with directed, weighted links derived from the marmoset structural connectivity data. Heterogeneity arose from the different link weights and model parameters for each node. Stimulation of the cluster with an incident pulse train modulated in the standard frequency bands induced a variety of dynamical state transitions that lasted in the range of 5-10 s, suggestive of timescales relevant to short-term memory. A short gamma burst rapidly reset the beta-induced transition. The theta-induced transition state showed a spontaneous, delayed reset to the resting state. An additional, continuous gamma wave stimulus induced a new beating oscillatory state. Longer or repeated gamma bursts were phase-aligned with the beta oscillation, delivering increasing energy input and causing shorter transition times. The relevance of these results to working memory is yet to be established, but they suggest interesting opportunities.
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Affiliation(s)
- Bernard A. Pailthorpe
- Brain Dynamics Group, School of Physics, University of Sydney, Sydney, NSW, Australia
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3
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Molnár F, Horvát S, Ribeiro Gomes AR, Martinez Armas J, Molnár B, Ercsey-Ravasz M, Knoblauch K, Kennedy H, Toroczkai Z. Predictability of cortico-cortical connections in the mammalian brain. Netw Neurosci 2024; 8:138-157. [PMID: 38562298 PMCID: PMC10861169 DOI: 10.1162/netn_a_00345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 10/23/2023] [Indexed: 04/04/2024] Open
Abstract
Despite a five order of magnitude range in size, the brains of mammals share many anatomical and functional characteristics that translate into cortical network commonalities. Here we develop a machine learning framework to quantify the degree of predictability of the weighted interareal cortical matrix. Partial network connectivity data were obtained with retrograde tract-tracing experiments generated with a consistent methodology, supplemented by projection length measurements in a nonhuman primate (macaque) and a rodent (mouse). We show that there is a significant level of predictability embedded in the interareal cortical networks of both species. At the binary level, links are predictable with an area under the ROC curve of at least 0.8 for the macaque. Weighted medium and strong links are predictable with an 85%-90% accuracy (mouse) and 70%-80% (macaque), whereas weak links are not predictable in either species. These observations reinforce earlier observations that the formation and evolution of the cortical network at the mesoscale is, to a large extent, rule based. Using the methodology presented here, we performed imputations on all area pairs, generating samples for the complete interareal network in both species. These are necessary for comparative studies of the connectome with minimal bias, both within and across species.
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Affiliation(s)
- Ferenc Molnár
- Department of Physics, University of Notre Dame, Notre Dame, IN, USA
| | - Szabolcs Horvát
- Center for Systems Biology Dresden, Dresden, Germany
- Max Planck Institute for Cell Biology and Genetics, Dresden, Germany
- Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
- Department of Computer Science, Reykjavik University, Reykjavík, Iceland
| | - Ana R. Ribeiro Gomes
- Univ Lyon, Université Claude Bernard Lyon 1, INSERM, Stem Cell and Brain Research Institute, Bron, France
| | | | - Botond Molnár
- Faculty of Mathematics and Computer Science, Babeş-Bolyai University, Cluj-Napoca, Romania
- Faculty of Physics, Babeş-Bolyai University, Cluj-Napoca, Romania
- Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Mária Ercsey-Ravasz
- Faculty of Physics, Babeş-Bolyai University, Cluj-Napoca, Romania
- Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Kenneth Knoblauch
- National Centre for Optics, Vision and Eye Care, Faculty of Health and Social Sciences, University of South-Eastern Norway, Kongsberg, Norway
| | - Henry Kennedy
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China
| | - Zoltan Toroczkai
- Department of Physics, University of Notre Dame, Notre Dame, IN, USA
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4
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Saleem KS, Avram AV, Glen D, Schram V, Basser PJ. The Subcortical Atlas of the Marmoset ("SAM") monkey based on high-resolution MRI and histology. Cereb Cortex 2024; 34:bhae120. [PMID: 38647221 DOI: 10.1093/cercor/bhae120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 03/07/2024] [Accepted: 03/07/2024] [Indexed: 04/25/2024] Open
Abstract
A comprehensive three-dimensional digital brain atlas of cortical and subcortical regions based on MRI and histology has a broad array of applications in anatomical, functional, and clinical studies. We first generated a Subcortical Atlas of the Marmoset, called the "SAM," from 251 delineated subcortical regions (e.g. thalamic subregions, etc.) derived from high-resolution Mean Apparent Propagator-MRI, T2W, and magnetization transfer ratio images ex vivo. We then confirmed the location and borders of these segmented regions in the MRI data using matched histological sections with multiple stains obtained from the same specimen. Finally, we estimated and confirmed the atlas-based areal boundaries of subcortical regions by registering this ex vivo atlas template to in vivo T1- or T2W MRI datasets of different age groups (single vs. multisubject population-based marmoset control adults) using a novel pipeline developed within Analysis of Functional NeuroImages software. Tracing and validating these important deep brain structures in 3D will improve neurosurgical planning, anatomical tract tracer injections, navigation of deep brain stimulation probes, functional MRI and brain connectivity studies, and our understanding of brain structure-function relationships. This new ex vivo template and atlas are available as volumes in standard NIFTI and GIFTI file formats and are intended for use as a reference standard for marmoset brain research.
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Affiliation(s)
- Kadharbatcha S Saleem
- Section on Quantitative Imaging and Tissue Sciences (SQITS), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institute of Health (NIH), 13, South Drive, Bethesda, MD 20892, United States
- Military Traumatic Brain Injury Initiative (MTBI2), Henry M. Jackson Foundation for the Advancement of Military Medicine, 6720A Rockledge Drive, Bethesda, MD 20817, United States
| | - Alexandru V Avram
- Section on Quantitative Imaging and Tissue Sciences (SQITS), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institute of Health (NIH), 13, South Drive, Bethesda, MD 20892, United States
| | - Daniel Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health (NIMH), NIH, 10 Center Drive, Bethesda, MD 20817, United States
| | - Vincent Schram
- Microscopy and Imaging Core (MIC), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), NIH, 35 Convent Drive, Bethesda, MD 20892, United States
| | - Peter J Basser
- Section on Quantitative Imaging and Tissue Sciences (SQITS), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institute of Health (NIH), 13, South Drive, Bethesda, MD 20892, United States
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5
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Xia J, Liu C, Li J, Meng Y, Yang S, Chen H, Liao W. Decomposing cortical activity through neuronal tracing connectome-eigenmodes in marmosets. Nat Commun 2024; 15:2289. [PMID: 38480767 PMCID: PMC10937940 DOI: 10.1038/s41467-024-46651-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 03/06/2024] [Indexed: 03/17/2024] Open
Abstract
Deciphering the complex relationship between neuroanatomical connections and functional activity in primate brains remains a daunting task, especially regarding the influence of monosynaptic connectivity on cortical activity. Here, we investigate the anatomical-functional relationship and decompose the neuronal-tracing connectome of marmoset brains into a series of eigenmodes using graph signal processing. These cellular connectome eigenmodes effectively constrain the cortical activity derived from resting-state functional MRI, and uncover a patterned cellular-functional decoupling. This pattern reveals a spatial gradient from coupled dorsal-posterior to decoupled ventral-anterior cortices, and recapitulates micro-structural profiles and macro-scale hierarchical cortical organization. Notably, these marmoset-derived eigenmodes may facilitate the inference of spontaneous cortical activity and functional connectivity of homologous areas in humans, highlighting the potential generalizing of the connectomic constraints across species. Collectively, our findings illuminate how neuronal-tracing connectome eigenmodes constrain cortical activity and improve our understanding of the brain's anatomical-functional relationship.
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Affiliation(s)
- Jie Xia
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Cirong Liu
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, P.R. China
| | - Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Siqi Yang
- School of Cybersecurity, Chengdu University of Information Technology, Chengdu, 610225, P.R. China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
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6
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Okuno T, Ichinohe N, Woodward A. A reappraisal of the default mode and frontoparietal networks in the common marmoset brain. FRONTIERS IN NEUROIMAGING 2024; 2:1345643. [PMID: 38264540 PMCID: PMC10803424 DOI: 10.3389/fnimg.2023.1345643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 12/20/2023] [Indexed: 01/25/2024]
Abstract
In recent years the common marmoset homolog of the human default mode network (DMN) has been a hot topic of discussion in the marmoset research field. Previously, the posterior cingulate cortex regions (PGM, A19M) and posterior parietal cortex regions (LIP, MIP) were defined as the DMN, but some studies claim that these form the frontoparietal network (FPN). We restarted from a neuroanatomical point of view and identified two DMN candidates: Comp-A (which has been called both the DMN and FPN) and Comp-B. We performed GLM analysis on auditory task-fMRI and found Comp-B to be more appropriate as the DMN, and Comp-A as the FPN. Additionally, through fingerprint analysis, a DMN and FPN in the tasking human was closer to the resting common marmoset. The human DMN appears to have an advanced function that may be underdeveloped in the common marmoset brain.
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Affiliation(s)
- Takuto Okuno
- Connectome Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Noritaka Ichinohe
- Laboratory for Ultrastructure Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Alexander Woodward
- Connectome Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama, Japan
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7
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Saleem KS, Avram AV, Glen D, Schram V, Basser PJ. The Subcortical Atlas of the Marmoset ("SAM") monkey based on high-resolution MRI and histology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.06.574429. [PMID: 38260391 PMCID: PMC10802408 DOI: 10.1101/2024.01.06.574429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
A comprehensive three-dimensional digital brain atlas of cortical and subcortical regions based on MRI and histology has a broad array of applications for anatomical, functional, and clinical studies. We first generated a Subcortical Atlas of the Marmoset, called the "SAM," from 251 delineated subcortical regions (e.g., thalamic subregions, etc.) derived from the high-resolution MAP-MRI, T2W, and MTR images ex vivo. We then confirmed the location and borders of these segmented regions in MRI data using matched histological sections with multiple stains obtained from the same specimen. Finally, we estimated and confirmed the atlas-based areal boundaries of subcortical regions by registering this ex vivo atlas template to in vivo T1- or T2W MRI datasets of different age groups (single vs. multisubject population-based marmoset control adults) using a novel pipeline developed within AFNI. Tracing and validating these important deep brain structures in 3D improves neurosurgical planning, anatomical tract tracer injections, navigation of deep brain stimulation probes, fMRI and brain connectivity studies, and our understanding of brain structure-function relationships. This new ex vivo template and atlas are available as volumes in standard NIFTI and GIFTI file formats and are intended for use as a reference standard for marmoset brain research.
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Affiliation(s)
- Kadharbatcha S Saleem
- Section on Quantitative Imaging and Tissue Sciences (SQITS), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), NIH, Bethesda, MD 20892
- Military Traumatic Brain Injury Initiative (MTBI), Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817
| | - Alexandru V Avram
- Section on Quantitative Imaging and Tissue Sciences (SQITS), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), NIH, Bethesda, MD 20892
| | - Daniel Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health (NIMH)
| | - Vincent Schram
- Microscopy and Imaging Core (MIC), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), NIH, Bethesda, MD 20892
| | - Peter J Basser
- Section on Quantitative Imaging and Tissue Sciences (SQITS), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), NIH, Bethesda, MD 20892
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8
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Pagani M, Gutierrez-Barragan D, de Guzman AE, Xu T, Gozzi A. Mapping and comparing fMRI connectivity networks across species. Commun Biol 2023; 6:1238. [PMID: 38062107 PMCID: PMC10703935 DOI: 10.1038/s42003-023-05629-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
Technical advances in neuroimaging, notably in fMRI, have allowed distributed patterns of functional connectivity to be mapped in the human brain with increasing spatiotemporal resolution. Recent years have seen a growing interest in extending this approach to rodents and non-human primates to understand the mechanism of fMRI connectivity and complement human investigations of the functional connectome. Here, we discuss current challenges and opportunities of fMRI connectivity mapping across species. We underscore the critical importance of physiologically decoding neuroimaging measures of brain (dys)connectivity via multiscale mechanistic investigations in animals. We next highlight a set of general principles governing the organization of mammalian connectivity networks across species. These include the presence of evolutionarily conserved network systems, a dominant cortical axis of functional connectivity, and a common repertoire of topographically conserved fMRI spatiotemporal modes. We finally describe emerging approaches allowing comparisons and extrapolations of fMRI connectivity findings across species. As neuroscientists gain access to increasingly sophisticated perturbational, computational and recording tools, cross-species fMRI offers novel opportunities to investigate the large-scale organization of the mammalian brain in health and disease.
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Affiliation(s)
- Marco Pagani
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
- Autism Center, Child Mind Institute, New York, NY, USA
- IMT School for Advanced Studies, Lucca, Italy
| | - Daniel Gutierrez-Barragan
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - A Elizabeth de Guzman
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Ting Xu
- Center for the Integrative Developmental Neuroscience, Child Mind Institute, New York, NY, USA
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy.
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9
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Costantini I, Morgan L, Yang J, Balbastre Y, Varadarajan D, Pesce L, Scardigli M, Mazzamuto G, Gavryusev V, Castelli FM, Roffilli M, Silvestri L, Laffey J, Raia S, Varghese M, Wicinski B, Chang S, Chen IA, Wang H, Cordero D, Vera M, Nolan J, Nestor K, Mora J, Iglesias JE, Garcia Pallares E, Evancic K, Augustinack JC, Fogarty M, Dalca AV, Frosch MP, Magnain C, Frost R, van der Kouwe A, Chen SC, Boas DA, Pavone FS, Fischl B, Hof PR. A cellular resolution atlas of Broca's area. SCIENCE ADVANCES 2023; 9:eadg3844. [PMID: 37824623 PMCID: PMC10569704 DOI: 10.1126/sciadv.adg3844] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 05/03/2023] [Indexed: 10/14/2023]
Abstract
Brain cells are arranged in laminar, nuclear, or columnar structures, spanning a range of scales. Here, we construct a reliable cell census in the frontal lobe of human cerebral cortex at micrometer resolution in a magnetic resonance imaging (MRI)-referenced system using innovative imaging and analysis methodologies. MRI establishes a macroscopic reference coordinate system of laminar and cytoarchitectural boundaries. Cell counting is obtained with a digital stereological approach on the 3D reconstruction at cellular resolution from a custom-made inverted confocal light-sheet fluorescence microscope (LSFM). Mesoscale optical coherence tomography enables the registration of the distorted histological cell typing obtained with LSFM to the MRI-based atlas coordinate system. The outcome is an integrated high-resolution cellular census of Broca's area in a human postmortem specimen, within a whole-brain reference space atlas.
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Affiliation(s)
- Irene Costantini
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- Department of Biology, University of Florence, Florence, Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
| | - Leah Morgan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jiarui Yang
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Yael Balbastre
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Divya Varadarajan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Luca Pesce
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
| | - Marina Scardigli
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
- Division of Physiology, Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Giacomo Mazzamuto
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
| | - Vladislav Gavryusev
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
| | - Filippo Maria Castelli
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
- Bioretics srl, Cesena, Italy
| | | | - Ludovico Silvestri
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
| | - Jessie Laffey
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sophia Raia
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Merina Varghese
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bridget Wicinski
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shuaibin Chang
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | | | - Hui Wang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Devani Cordero
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Matthew Vera
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jackson Nolan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Kimberly Nestor
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Jocelyn Mora
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Juan Eugenio Iglesias
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Erendira Garcia Pallares
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Kathryn Evancic
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Jean C. Augustinack
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Morgan Fogarty
- Imaging Science Program, Washington University McKelvey School of Engineering, St. Louis, MO, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Adrian V. Dalca
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Matthew P. Frosch
- C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Caroline Magnain
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Robert Frost
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Andre van der Kouwe
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Department of Human Biology, University of Cape Town, Cape Town, South Africa
| | - Shih-Chi Chen
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - David A. Boas
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Francesco Saverio Pavone
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
| | - Bruce Fischl
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- HST, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Patrick R. Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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10
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Krienen FM, Levandowski KM, Zaniewski H, del Rosario RC, Schroeder ME, Goldman M, Wienisch M, Lutservitz A, Beja-Glasser VF, Chen C, Zhang Q, Chan KY, Li KX, Sharma J, McCormack D, Shin TW, Harrahill A, Nyase E, Mudhar G, Mauermann A, Wysoker A, Nemesh J, Kashin S, Vergara J, Chelini G, Dimidschstein J, Berretta S, Deverman BE, Boyden E, McCarroll SA, Feng G. A marmoset brain cell census reveals regional specialization of cellular identities. SCIENCE ADVANCES 2023; 9:eadk3986. [PMID: 37824615 PMCID: PMC10569717 DOI: 10.1126/sciadv.adk3986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 09/26/2023] [Indexed: 10/14/2023]
Abstract
The mammalian brain is composed of many brain structures, each with its own ontogenetic and developmental history. We used single-nucleus RNA sequencing to sample over 2.4 million brain cells across 18 locations in the common marmoset, a New World monkey primed for genetic engineering, and examined gene expression patterns of cell types within and across brain structures. The adult transcriptomic identity of most neuronal types is shaped more by developmental origin than by neurotransmitter signaling repertoire. Quantitative mapping of GABAergic types with single-molecule FISH (smFISH) reveals that interneurons in the striatum and neocortex follow distinct spatial principles, and that lateral prefrontal and other higher-order cortical association areas are distinguished by high proportions of VIP+ neurons. We use cell type-specific enhancers to drive AAV-GFP and reconstruct the morphologies of molecularly resolved interneuron types in neocortex and striatum. Our analyses highlight how lineage, local context, and functional class contribute to the transcriptional identity and biodistribution of primate brain cell types.
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Affiliation(s)
- Fenna M. Krienen
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kirsten M. Levandowski
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Heather Zaniewski
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ricardo C.H. del Rosario
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Margaret E. Schroeder
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Melissa Goldman
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Martin Wienisch
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Alyssa Lutservitz
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Victoria F. Beja-Glasser
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Cindy Chen
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Qiangge Zhang
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ken Y. Chan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Katelyn X. Li
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jitendra Sharma
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Dana McCormack
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Tay Won Shin
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Howard Hughes Medical Institute, Cambridge, MA 02139, USA
| | - Andrew Harrahill
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Eric Nyase
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Gagandeep Mudhar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Abigail Mauermann
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Howard Hughes Medical Institute, Cambridge, MA 02139, USA
| | - Alec Wysoker
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - James Nemesh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Seva Kashin
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Josselyn Vergara
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Gabriele Chelini
- Center for Mind/Brain Sciences, University of Trento, Piazza della Manifattura n.1, Rovereto (TN) 38068, Italy
| | - Jordane Dimidschstein
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Sabina Berretta
- Basic Neuroscience Division, McLean Hospital, Belmont, MA 02478, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA
| | - Benjamin E. Deverman
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ed Boyden
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Howard Hughes Medical Institute, Cambridge, MA 02139, USA
| | - Steven A. McCarroll
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Guoping Feng
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
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11
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Reznik D, Trampel R, Weiskopf N, Witter MP, Doeller CF. Dissociating distinct cortical networks associated with subregions of the human medial temporal lobe using precision neuroimaging. Neuron 2023; 111:2756-2772.e7. [PMID: 37390820 DOI: 10.1016/j.neuron.2023.05.029] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 05/26/2023] [Accepted: 05/27/2023] [Indexed: 07/02/2023]
Abstract
Tract-tracing studies in primates indicate that different subregions of the medial temporal lobe (MTL) are connected with multiple brain regions. However, no clear framework defining the distributed anatomy associated with the human MTL exists. This gap in knowledge originates in notoriously low MRI data quality in the anterior human MTL and in group-level blurring of idiosyncratic anatomy between adjacent brain regions, such as entorhinal and perirhinal cortices, and parahippocampal areas TH/TF. Using MRI, we intensively scanned four human individuals and collected whole-brain data with unprecedented MTL signal quality. Following detailed exploration of cortical networks associated with MTL subregions within each individual, we discovered three biologically meaningful networks associated with the entorhinal cortex, perirhinal cortex, and parahippocampal area TH, respectively. Our findings define the anatomical constraints within which human mnemonic functions must operate and are insightful for examining the evolutionary trajectory of the MTL connectivity across species.
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Affiliation(s)
- Daniel Reznik
- Department of Psychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Robert Trampel
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Menno P Witter
- Kavli Institute for Systems Neuroscience, Centre for Neural Computation, Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, Jebsen Centre for Alzheimer's Disease, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Christian F Doeller
- Department of Psychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Kavli Institute for Systems Neuroscience, Centre for Neural Computation, Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, Jebsen Centre for Alzheimer's Disease, NTNU Norwegian University of Science and Technology, Trondheim, Norway; Wilhelm Wundt Institute of Psychology, Leipzig University, Leipzig, Germany; Department of Psychology, Technische Universität Dresden, Dresden, Germany.
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12
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Watakabe A, Skibbe H, Nakae K, Abe H, Ichinohe N, Rachmadi MF, Wang J, Takaji M, Mizukami H, Woodward A, Gong R, Hata J, Van Essen DC, Okano H, Ishii S, Yamamori T. Local and long-distance organization of prefrontal cortex circuits in the marmoset brain. Neuron 2023; 111:2258-2273.e10. [PMID: 37196659 PMCID: PMC10789578 DOI: 10.1016/j.neuron.2023.04.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 03/13/2023] [Accepted: 04/25/2023] [Indexed: 05/19/2023]
Abstract
The prefrontal cortex (PFC) has dramatically expanded in primates, but its organization and interactions with other brain regions are only partially understood. We performed high-resolution connectomic mapping of the marmoset PFC and found two contrasting corticocortical and corticostriatal projection patterns: "patchy" projections that formed many columns of submillimeter scale in nearby and distant regions and "diffuse" projections that spread widely across the cortex and striatum. Parcellation-free analyses revealed representations of PFC gradients in these projections' local and global distribution patterns. We also demonstrated column-scale precision of reciprocal corticocortical connectivity, suggesting that PFC contains a mosaic of discrete columns. Diffuse projections showed considerable diversity in the laminar patterns of axonal spread. Altogether, these fine-grained analyses reveal important principles of local and long-distance PFC circuits in marmosets and provide insights into the functional organization of the primate brain.
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Affiliation(s)
- Akiya Watakabe
- Laboratory for Molecular Analysis of Higher Brain Function, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan; Laboratory for Haptic Perception and Cognitive Physiology, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan.
| | - Henrik Skibbe
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan.
| | - Ken Nakae
- Integrated Systems Biology Laboratory, Department of Systems Science, Graduate School of Informatics, Kyoto University, Kyoto, Kyoto 606-8501, Japan; Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, Okazaki, Aichi 444-8787, Japan
| | - Hiroshi Abe
- Laboratory for Molecular Analysis of Higher Brain Function, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan; Laboratory for Haptic Perception and Cognitive Physiology, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan
| | - Noritaka Ichinohe
- Laboratory for Molecular Analysis of Higher Brain Function, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan; Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo 187-0031, Japan
| | - Muhammad Febrian Rachmadi
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan; Faculty of Computer Science, Universitas Indonesia, Depok, Jawa Barat 16424, Indonesia
| | - Jian Wang
- Laboratory for Molecular Analysis of Higher Brain Function, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan
| | - Masafumi Takaji
- Laboratory for Molecular Analysis of Higher Brain Function, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan; Laboratory for Haptic Perception and Cognitive Physiology, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan
| | - Hiroaki Mizukami
- Division of Genetic Therapeutics, Center for Molecular Medicine, Jichi Medical University, Shimotsuke, Tochigi 329-0498, Japan
| | - Alexander Woodward
- Connectome Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan
| | - Rui Gong
- Connectome Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan
| | - Junichi Hata
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan; Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo 116-8551, Japan
| | - David C Van Essen
- Department of Neuroscience, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan; Department of Physiology, Keio University School of Medicine, Tokyo 108-8345, Japan
| | - Shin Ishii
- Integrated Systems Biology Laboratory, Department of Systems Science, Graduate School of Informatics, Kyoto University, Kyoto, Kyoto 606-8501, Japan
| | - Tetsuo Yamamori
- Laboratory for Molecular Analysis of Higher Brain Function, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan; Laboratory for Haptic Perception and Cognitive Physiology, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan; Department of Marmoset Biology and Medicine, Central Institute for Experimental Animals, Kawasaki, Kanagawa 210-0821, Japan.
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13
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Froudist-Walsh S, Xu T, Niu M, Rapan L, Zhao L, Margulies DS, Zilles K, Wang XJ, Palomero-Gallagher N. Gradients of neurotransmitter receptor expression in the macaque cortex. Nat Neurosci 2023; 26:1281-1294. [PMID: 37336976 PMCID: PMC10322721 DOI: 10.1038/s41593-023-01351-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 05/01/2023] [Indexed: 06/21/2023]
Abstract
Dynamics and functions of neural circuits depend on interactions mediated by receptors. Therefore, a comprehensive map of receptor organization across cortical regions is needed. In this study, we used in vitro receptor autoradiography to measure the density of 14 neurotransmitter receptor types in 109 areas of macaque cortex. We integrated the receptor data with anatomical, genetic and functional connectivity data into a common cortical space. We uncovered a principal gradient of receptor expression per neuron. This aligns with the cortical hierarchy from sensory cortex to higher cognitive areas. A second gradient, driven by serotonin 5-HT1A receptors, peaks in the anterior cingulate, default mode and salience networks. We found a similar pattern of 5-HT1A expression in the human brain. Thus, the macaque may be a promising translational model of serotonergic processing and disorders. The receptor gradients may enable rapid, reliable information processing in sensory cortical areas and slow, flexible integration in higher cognitive areas.
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MESH Headings
- Aged
- Animals
- Female
- Humans
- Male
- Rats
- Autoradiography
- Brain Mapping
- Cerebral Cortex/cytology
- Cerebral Cortex/metabolism
- Cognition
- Dendritic Spines
- Gyrus Cinguli/cytology
- Gyrus Cinguli/metabolism
- Macaca fascicularis
- Rats, Inbred Lew
- Receptor, Serotonin, 5-HT1A/analysis
- Receptor, Serotonin, 5-HT1A/metabolism
- Receptors, Cholinergic/analysis
- Receptors, Cholinergic/metabolism
- Receptors, Dopamine/analysis
- Receptors, Dopamine/metabolism
- Receptors, Neurotransmitter/analysis
- Receptors, Neurotransmitter/metabolism
- Serotonin/metabolism
- Species Specificity
- Myelin Sheath/metabolism
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Affiliation(s)
- Sean Froudist-Walsh
- Computational Neuroscience Unit, Faculty of Engineering, University of Bristol, Bristol, UK
- Center for Neural Science, New York University, New York, NY, USA
| | - Ting Xu
- Child Mind Institute, New York, NY, USA
| | - Meiqi Niu
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Lucija Rapan
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Ling Zhao
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center, University of Paris Cité, Paris, France
| | | | - Xiao-Jing Wang
- Center for Neural Science, New York University, New York, NY, USA
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
- Cécile and Oskar Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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14
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Skibbe H, Rachmadi MF, Nakae K, Gutierrez CE, Hata J, Tsukada H, Poon C, Schlachter M, Doya K, Majka P, Rosa MGP, Okano H, Yamamori T, Ishii S, Reisert M, Watakabe A. The Brain/MINDS Marmoset Connectivity Resource: An open-access platform for cellular-level tracing and tractography in the primate brain. PLoS Biol 2023; 21:e3002158. [PMID: 37384809 DOI: 10.1371/journal.pbio.3002158] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 05/11/2023] [Indexed: 07/01/2023] Open
Abstract
The primate brain has unique anatomical characteristics, which translate into advanced cognitive, sensory, and motor abilities. Thus, it is important that we gain insight on its structure to provide a solid basis for models that will clarify function. Here, we report on the implementation and features of the Brain/MINDS Marmoset Connectivity Resource (BMCR), a new open-access platform that provides access to high-resolution anterograde neuronal tracer data in the marmoset brain, integrated to retrograde tracer and tractography data. Unlike other existing image explorers, the BMCR allows visualization of data from different individuals and modalities in a common reference space. This feature, allied to an unprecedented high resolution, enables analyses of features such as reciprocity, directionality, and spatial segregation of connections. The present release of the BMCR focuses on the prefrontal cortex (PFC), a uniquely developed region of the primate brain that is linked to advanced cognition, including the results of 52 anterograde and 164 retrograde tracer injections in the cortex of the marmoset. Moreover, the inclusion of tractography data from diffusion MRI allows systematic analyses of this noninvasive modality against gold-standard cellular connectivity data, enabling detection of false positives and negatives, which provide a basis for future development of tractography. This paper introduces the BMCR image preprocessing pipeline and resources, which include new tools for exploring and reviewing the data.
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Affiliation(s)
- Henrik Skibbe
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | | | - Ken Nakae
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Aichi, Japan
| | - Carlos Enrique Gutierrez
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Onna Village, Japan
| | - Junichi Hata
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Saitama, Japan
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Hiromichi Tsukada
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Onna Village, Japan
- Center for Mathematical Science and Artificial Intelligence, Chubu University, Kasugai, Aichi, Japan
| | - Charissa Poon
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Matthias Schlachter
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Kenji Doya
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Onna Village, Japan
| | - Piotr Majka
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
- Australian Research Council, Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, Australia
- Neuroscience Program, Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, Australia
| | - Marcello G P Rosa
- Australian Research Council, Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, Australia
- Neuroscience Program, Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, Australia
| | - Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Saitama, Japan
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Tetsuo Yamamori
- Laboratory of Haptic Perception and Cognitive Physiology, RIKEN Center for Brain Science, Wako, Saitama, Japan
- Department of Marmoset Biology and Medicine, Central Institute for Experimental Animals, Kawasaki, Japan
| | - Shin Ishii
- Department of Systems Science, Kyoto University, Kyoto, Japan
| | - Marco Reisert
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama, Japan
- Department of Stereotactic and Functional Neurosurgery, Medical Center of the University of Freiburg, Freiburg Im Breisgau, Germany
- Medical Faculty of the University of Freiburg, Freiburg Im Breisgau, Germany
- Department of Diagnostic and Interventional Radiology, Medical Physics, Medical Center-University of Freiburg, Freiburg Im Breisgau, Germany
| | - Akiya Watakabe
- Laboratory of Haptic Perception and Cognitive Physiology, RIKEN Center for Brain Science, Wako, Saitama, Japan
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15
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Belin P, Trapeau R, Obliger-Debouche M. A small, but vocal, brain. Cell Rep 2023; 42:112651. [PMID: 37314925 DOI: 10.1016/j.celrep.2023.112651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 05/24/2023] [Accepted: 05/30/2023] [Indexed: 06/16/2023] Open
Abstract
In the May issue of Cell Reports, Jafari et al.1 used ultra-high-field fMRI to show that marmosets, like humans and macaques, possess an extensive network of voice-selective areas.
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Affiliation(s)
- Pascal Belin
- La Timone Neuroscience Institute, Marseille, France.
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16
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Arefin TM, Lee CH, Liang Z, Rallapalli H, Wadghiri YZ, Turnbull DH, Zhang J. Towards reliable reconstruction of the mouse brain corticothalamic connectivity using diffusion MRI. Neuroimage 2023; 273:120111. [PMID: 37060936 PMCID: PMC10149621 DOI: 10.1016/j.neuroimage.2023.120111] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/29/2023] [Accepted: 04/12/2023] [Indexed: 04/17/2023] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography has yielded intriguing insights into brain circuits and their relationship to behavior in response to gene mutations or neurological diseases across a number of species. Still, existing tractography approaches suffer from limited sensitivity and specificity, leading to uncertain interpretation of the reconstructed connections. Hence, in this study, we aimed to optimize the imaging and computational pipeline to achieve the best possible spatial overlaps between the tractography and tracer-based axonal projection maps within the mouse brain corticothalamic network. We developed a dMRI-based atlas of the mouse forebrain with structural labels imported from the Allen Mouse Brain Atlas (AMBA). Using the atlas and dMRI tractography, we first reconstructed detailed node-to-node mouse brain corticothalamic structural connectivity matrices using different imaging and tractography parameters. We then investigated the effects of each condition for accurate reconstruction of the corticothalamic projections by quantifying the similarities between the tractography and the tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA). Our results suggest that these parameters significantly affect tractography outcomes and our atlas can be used to investigate macroscopic structural connectivity in the mouse brain. Furthermore, tractography in mouse brain gray matter still face challenges and need improved imaging and tractography methods.
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Affiliation(s)
- Tanzil Mahmud Arefin
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States; Center for Neurotechnology in Mental Health Research, Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, United States
| | - Choong Heon Lee
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Zifei Liang
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Harikrishna Rallapalli
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Youssef Z Wadghiri
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Daniel H Turnbull
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Jiangyang Zhang
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States.
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17
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Jafari A, Dureux A, Zanini A, Menon RS, Gilbert KM, Everling S. A vocalization-processing network in marmosets. Cell Rep 2023; 42:112526. [PMID: 37195863 DOI: 10.1016/j.celrep.2023.112526] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/31/2023] [Accepted: 05/02/2023] [Indexed: 05/19/2023] Open
Abstract
Vocalizations play an important role in the daily life of primates and likely form the basis of human language. Functional imaging studies have demonstrated that listening to voices activates a fronto-temporal voice perception network in human participants. Here, we acquired whole-brain ultrahigh-field (9.4 T) fMRI in awake marmosets (Callithrix jacchus) and demonstrate that these small, highly vocal New World primates possess a similar fronto-temporal network, including subcortical regions, that is activated by the presentation of conspecific vocalizations. The findings suggest that the human voice perception network has evolved from an ancestral vocalization-processing network that predates the separation of New and Old World primates.
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Affiliation(s)
- Azadeh Jafari
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Audrey Dureux
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Alessandro Zanini
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Ravi S Menon
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Kyle M Gilbert
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Stefan Everling
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada; Department of Physiology and Pharmacology, University of Western Ontario, London, ON, Canada.
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18
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Zhu X, Yan H, Zhan Y, Feng F, Wei C, Yao YG, Liu C. An anatomical and connectivity atlas of the marmoset cerebellum. Cell Rep 2023; 42:112480. [PMID: 37163375 DOI: 10.1016/j.celrep.2023.112480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 02/01/2023] [Accepted: 04/20/2023] [Indexed: 05/12/2023] Open
Abstract
The cerebellum is essential for motor control and cognitive functioning, engaging in bidirectional communication with the cerebral cortex. The common marmoset, a small non-human primate, offers unique advantages for studying cerebello-cerebral circuits. However, the marmoset cerebellum is not well described in published resources. In this study, we present a comprehensive atlas of the marmoset cerebellum comprising (1) fine-detailed anatomical atlases and surface-analysis tools of the cerebellar cortex based on ultra-high-resolution ex vivo MRI, (2) functional connectivity and gradient patterns of the cerebellar cortex revealed by awake resting-state fMRI, and (3) structural-connectivity mapping of cerebellar nuclei using high-resolution diffusion MRI tractography. The atlas elucidates the anatomical details of the marmoset cerebellum, reveals distinct gradient patterns of intra-cerebellar and cerebello-cerebral functional connectivity, and maps the topological relationship of cerebellar nuclei in cerebello-cerebral circuits. As version 5 of the Marmoset Brain Mapping project, this atlas is publicly available at https://marmosetbrainmapping.org/MBMv5.html.
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Affiliation(s)
- Xiaojia Zhu
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, and KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), National Resource Center for Non-Human Primates, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China; Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haotian Yan
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yafeng Zhan
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Furui Feng
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chuanyao Wei
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yong-Gang Yao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, and KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), National Resource Center for Non-Human Primates, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650201, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Cirong Liu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China; Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China.
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19
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Charvet CJ. Mapping Human Brain Pathways: Challenges and Opportunities in the Integration of Scales. BRAIN, BEHAVIOR AND EVOLUTION 2023; 98:194-209. [PMID: 36972574 DOI: 10.1159/000530317] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/16/2023] [Indexed: 03/29/2023]
Abstract
The human brain is composed of a complex web of pathways. Diffusion magnetic resonance (MR) tractography is a neuroimaging technique that relies on the principle of diffusion to reconstruct brain pathways. Its tractography is broadly applicable to a range of problems as it is amenable for study in individuals of any age and from any species. However, it is well known that this technique can generate biologically implausible pathways, especially in regions of the brain where multiple fibers cross. This review highlights potential misconnections in two cortico-cortical association pathways with a focus on the aslant tract and inferior frontal occipital fasciculus. The lack of alternative methods to validate observations from diffusion MR tractography means there is a need to develop new integrative approaches to trace human brain pathways. This review discusses integrative approaches in neuroimaging, anatomical, and transcriptional variation as having much potential to trace the evolution of human brain pathways.
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Affiliation(s)
- Christine J Charvet
- Department of Anatomy, Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, Alabama, USA
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20
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Alexander L, Wood CM, Roberts AC. The ventromedial prefrontal cortex and emotion regulation: lost in translation? J Physiol 2023; 601:37-50. [PMID: 35635793 PMCID: PMC10084434 DOI: 10.1113/jp282627] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 05/13/2022] [Indexed: 01/03/2023] Open
Abstract
Neuroimaging studies implicate the ventromedial prefrontal cortex (vmPFC) in a wide range of emotional and cognitive functions, and changes in activity within vmPFC have been linked to the aetiology and successful treatment of depression. However, this is a large, structurally heterogeneous region and the extent to which this structural heterogeneity reflects functional heterogeneity remains unclear. Causal studies in animals should help address this question but attempts to map findings from vmPFC studies in rodents onto human imaging studies highlight cross-species discrepancies between structural homology and functional analogy. Bridging this gap, recent studies in marmosets - a species of new world monkey in which the overall organization of vmPFC is more like humans than that of rodents - have revealed that over-activation of the caudal subcallosal region of vmPFC, area 25, but not neighbouring area 32, heightens reactivity to negatively valenced stimuli whilst blunting responsivity to positively valenced stimuli. These co-occurring states resemble those seen in depressed patients, which are associated with increased activity in caudal subcallosal regions. In contrast, only reward blunting but not heightening of threat reactivity is seen following over-activation of the structurally homologous region in rodents. To further advance understanding of the role of vmPFC in the aetiology and treatment of depression, future work should focus on the behaviourally specific networks by which vmPFC regions have their effects, together with characterization of cross-species similarities and differences in function.
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Affiliation(s)
- Laith Alexander
- St Thomas’ HospitalLondonUK
- Department of Psychological MedicineSchool of Academic PsychiatryInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Christian M. Wood
- Department of PhysiologyDevelopment and NeuroscienceUniversity of CambridgeCambridgeUK
- Behavioural and Clinical Neuroscience InstituteUniversity of CambridgeCambridgeUK
| | - Angela C. Roberts
- Department of PhysiologyDevelopment and NeuroscienceUniversity of CambridgeCambridgeUK
- Behavioural and Clinical Neuroscience InstituteUniversity of CambridgeCambridgeUK
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21
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Tian X, Chen Y, Majka P, Szczupak D, Perl YS, Yen CCC, Tong C, Feng F, Jiang H, Glen D, Deco G, Rosa MGP, Silva AC, Liang Z, Liu C. An integrated resource for functional and structural connectivity of the marmoset brain. Nat Commun 2022; 13:7416. [PMID: 36456558 PMCID: PMC9715556 DOI: 10.1038/s41467-022-35197-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 11/21/2022] [Indexed: 12/02/2022] Open
Abstract
Comprehensive integration of structural and functional connectivity data is required to model brain functions accurately. While resources for studying the structural connectivity of non-human primate brains already exist, their integration with functional connectivity data has remained unavailable. Here we present a comprehensive resource that integrates the most extensive awake marmoset resting-state fMRI data available to date (39 marmoset monkeys, 710 runs, 12117 mins) with previously published cellular-level neuronal tracing data (52 marmoset monkeys, 143 injections) and multi-resolution diffusion MRI datasets. The combination of these data allowed us to (1) map the fine-detailed functional brain networks and cortical parcellations, (2) develop a deep-learning-based parcellation generator that preserves the topographical organization of functional connectivity and reflects individual variabilities, and (3) investigate the structural basis underlying functional connectivity by computational modeling. This resource will enable modeling structure-function relationships and facilitate future comparative and translational studies of primate brains.
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Affiliation(s)
- Xiaoguang Tian
- grid.21925.3d0000 0004 1936 9000Department of Neurobiology, University of Pittsburgh Brain Institute, University of Pittsburgh, Pittsburgh, PA 15261 USA
| | - Yuyan Chen
- grid.9227.e0000000119573309Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China
| | - Piotr Majka
- grid.419305.a0000 0001 1943 2944Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, 02-093 Warsaw, Poland ,grid.1002.30000 0004 1936 7857Department of Physiology and Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800 Australia
| | - Diego Szczupak
- grid.21925.3d0000 0004 1936 9000Department of Neurobiology, University of Pittsburgh Brain Institute, University of Pittsburgh, Pittsburgh, PA 15261 USA
| | - Yonatan Sanz Perl
- grid.5612.00000 0001 2172 2676Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018 Spain ,grid.441741.30000 0001 2325 2241Universidad de San Andrés, Vito Dumas 284 (B1644BID), Buenos Aires, Argentina
| | - Cecil Chern-Chyi Yen
- grid.94365.3d0000 0001 2297 5165Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health (NINDS/NIH), Bethesda, MD 20892 USA
| | - Chuanjun Tong
- grid.9227.e0000000119573309Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China
| | - Furui Feng
- grid.9227.e0000000119573309Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China
| | - Haiteng Jiang
- grid.13402.340000 0004 1759 700XDepartment of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Zhe Jiang Sheng, China ,grid.13402.340000 0004 1759 700XMOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou, China
| | - Daniel Glen
- grid.94365.3d0000 0001 2297 5165Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health (NIMH/NIH), Bethesda, MD 20892 USA
| | - Gustavo Deco
- grid.5612.00000 0001 2172 2676Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018 Spain ,grid.425902.80000 0000 9601 989XInstitució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, 08010 Spain ,grid.419524.f0000 0001 0041 5028Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04103 Germany ,grid.1002.30000 0004 1936 7857School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC 3800 Australia
| | - Marcello G. P. Rosa
- grid.1002.30000 0004 1936 7857Department of Physiology and Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800 Australia
| | - Afonso C. Silva
- grid.21925.3d0000 0004 1936 9000Department of Neurobiology, University of Pittsburgh Brain Institute, University of Pittsburgh, Pittsburgh, PA 15261 USA
| | - Zhifeng Liang
- grid.9227.e0000000119573309Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China ,grid.511008.dShanghai Center for Brain Science and Brain-Inspired Intelligence Technology Shanghai, Shanghai, China
| | - Cirong Liu
- grid.9227.e0000000119573309Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China ,grid.511008.dShanghai Center for Brain Science and Brain-Inspired Intelligence Technology Shanghai, Shanghai, China ,Lingang Laboratory, Shanghai, 200031 China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, China
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22
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Multimodal analysis demonstrating the shaping of functional gradients in the marmoset brain. Nat Commun 2022; 13:6584. [PMID: 36329036 PMCID: PMC9633775 DOI: 10.1038/s41467-022-34371-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
Abstract
The discovery of functional gradients introduce a new perspective in understanding the cortical spectrum of intrinsic dynamics, as it captures major axes of functional connectivity in low-dimensional space. However, how functional gradients arise and dynamically vary remains poorly understood. In this study, we investigated the biological basis of functional gradients using awake resting-state fMRI, retrograde tracing and gene expression datasets in marmosets. We found functional gradients in marmosets showed a sensorimotor-to-visual principal gradient followed by a unimodal-to-multimodal gradient, resembling functional gradients in human children. Although strongly constrained by structural wirings, functional gradients were dynamically modulated by arousal levels. Utilizing a reduced model, we uncovered opposing effects on gradient dynamics by structural connectivity (inverted U-shape) and neuromodulatory input (U-shape) with arousal fluctuations, and dissected the contribution of individual neuromodulatory receptors. This study provides insights into biological basis of functional gradients by revealing the interaction between structural connectivity and ascending neuromodulatory system.
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23
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Quah SKL, McIver L, Bullmore ET, Roberts AC, Sawiak SJ. Higher-order brain regions show shifts in structural covariance in adolescent marmosets. Cereb Cortex 2022; 32:4128-4140. [PMID: 35029670 PMCID: PMC9476623 DOI: 10.1093/cercor/bhab470] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 11/18/2021] [Accepted: 11/19/2021] [Indexed: 11/13/2022] Open
Abstract
Substantial progress has been made studying morphological changes in brain regions during adolescence, but less is known of network-level changes in their relationship. Here, we compare covariance networks constructed from the correlation of morphometric volumes across 135 brain regions of marmoset monkeys in early adolescence and adulthood. Substantial shifts are identified in the topology of structural covariance networks in the prefrontal cortex (PFC) and temporal lobe. PFC regions become more structurally differentiated and segregated within their own local network, hypothesized to reflect increased specialization after maturation. In contrast, temporal regions show increased inter-hemispheric covariances that may underlie the establishment of distributed networks. Regionally selective coupling of structural and maturational covariance is revealed, with relatively weak coupling in transmodal association areas. The latter may be a consequence of continued maturation within adulthood, but also environmental factors, for example, family size, affecting brain morphology. Advancing our understanding of how morphological relationships within higher-order brain areas mature in adolescence deepens our knowledge of the developing brain's organizing principles.
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Affiliation(s)
- Shaun K L Quah
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 3EB, UK
| | - Lauren McIver
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 3EB, UK
| | - Edward T Bullmore
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
- Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge CB21 5EF, UK
| | - Angela C Roberts
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 3EB, UK
| | - Stephen J Sawiak
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 3EB, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
- Translational Neuroimaging Laboratory, University of Cambridge, Cambridge, CB2 3EB, UK
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24
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Samandra R, Haque ZZ, Rosa MGP, Mansouri FA. The marmoset as a model for investigating the neural basis of social cognition in health and disease. Neurosci Biobehav Rev 2022; 138:104692. [PMID: 35569579 DOI: 10.1016/j.neubiorev.2022.104692] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/05/2022] [Accepted: 05/09/2022] [Indexed: 01/23/2023]
Abstract
Social-cognitive processes facilitate the use of environmental cues to understand others, and to be understood by others. Animal models provide vital insights into the neural underpinning of social behaviours. To understand social cognition at even deeper behavioural, cognitive, neural, and molecular levels, we need to develop more representative study models, which allow testing of novel hypotheses using human-relevant cognitive tasks. Due to their cooperative breeding system and relatively small size, common marmosets (Callithrix jacchus) offer a promising translational model for such endeavours. In addition to having social behavioural patterns and group dynamics analogous to those of humans, marmosets have cortical brain areas relevant for the mechanistic analysis of human social cognition, albeit in simplified form. Thus, they are likely suitable animal models for deciphering the physiological processes, connectivity and molecular mechanisms supporting advanced cognitive functions. Here, we review findings emerging from marmoset social and behavioural studies, which have already provided significant insights into executive, motivational, social, and emotional dysfunction associated with neurological and psychiatric disorders.
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Affiliation(s)
- Ranshikha Samandra
- Cognitive Neuroscience Laboratory, Department of Physiology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Zakia Z Haque
- Cognitive Neuroscience Laboratory, Department of Physiology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Marcello G P Rosa
- Department of Physiology and Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia; ARC Centre for Integrative Brain Function, Monash University, Australia.
| | - Farshad Alizadeh Mansouri
- Cognitive Neuroscience Laboratory, Department of Physiology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia; ARC Centre for Integrative Brain Function, Monash University, Australia.
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25
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Garin CM, Garin M, Silenzi L, Jaffe R, Constantinidis C. Multilevel atlas comparisons reveal divergent evolution of the primate brain. Proc Natl Acad Sci U S A 2022; 119:e2202491119. [PMID: 35700361 PMCID: PMC9231627 DOI: 10.1073/pnas.2202491119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 04/25/2022] [Indexed: 01/08/2023] Open
Abstract
Whether the size of the prefrontal cortex (PFC) in humans is disproportionate when compared to other species is a persistent debate in evolutionary neuroscience. This question has left the study of over/under-expansion in other structures relatively unexplored. We therefore sought to address this gap by adapting anatomical areas from the digital atlases of 18 mammalian species, to create a common interspecies classification. Our approach used data-driven analysis based on phylogenetic generalized least squares to evaluate anatomical expansion covering the whole brain. Our main finding suggests a divergence in primate evolution, orienting the stereotypical mammalian cerebral proportion toward a frontal and parietal lobe expansion in catarrhini (primate parvorder comprising old world monkeys, apes, and humans). Cerebral lobe volumes slopes plotted for catarrhini species were ranked as parietal∼frontal > temporal > occipital, contrasting with the ranking of other mammalian species (occipital > temporal > frontal∼parietal). Frontal and parietal slopes were statistically different in catarrhini when compared to other species through bootstrap analysis. Within the catarrhini's frontal lobe, the prefrontal cortex was the principal driver of frontal expansion. Across all species, expansion of the frontal lobe appeared to be systematically linked to the parietal lobe. Our findings suggest that the human frontal and parietal lobes are not disproportionately enlarged when compared to other catarrhini. Nevertheless, humans remain unique in carrying the most relatively enlarged frontal and parietal lobes in an infraorder exhibiting a disproportionate expansion of these areas.
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Affiliation(s)
- Clément M. Garin
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235
| | - Marie Garin
- Département de Mathématiques, Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, F-91190 France
| | - Leonardo Silenzi
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston Salem, NC 27157
| | - Rye Jaffe
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston Salem, NC 27157
| | - Christos Constantinidis
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235
- Program in Neuroscience, Vanderbilt University, Nashville, TN 37235
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN 37232
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26
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Vinci-Booher S, Caron B, Bullock D, James K, Pestilli F. Development of white matter tracts between and within the dorsal and ventral streams. Brain Struct Funct 2022; 227:1457-1477. [PMID: 35267078 DOI: 10.1007/s00429-021-02414-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 10/12/2021] [Indexed: 01/11/2023]
Abstract
The degree of interaction between the ventral and dorsal visual streams has been discussed in multiple scientific domains for decades. Recently, several white matter tracts that directly connect cortical regions associated with the dorsal and ventral streams have become possible to study due to advancements in automated and reproducible methods. The developmental trajectory of this set of tracts, here referred to as the posterior vertical pathway (PVP), has yet to be described. We propose an input-driven model of white matter development and provide evidence for the model by focusing on the development of the PVP. We used reproducible, cloud-computing methods and diffusion imaging from adults and children (ages 5-8 years) to compare PVP development to that of tracts within the ventral and dorsal pathways. PVP microstructure was more adult-like than dorsal stream microstructure, but less adult-like than ventral stream microstructure. Additionally, PVP microstructure was more similar to the microstructure of the ventral than the dorsal stream and was predicted by performance on a perceptual task in children. Overall, results suggest a potential role for the PVP in the development of the dorsal visual stream that may be related to its ability to facilitate interactions between ventral and dorsal streams during learning. Our results are consistent with the proposed model, suggesting that the microstructural development of major white matter pathways is related, at least in part, to the propagation of sensory information within the visual system.
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Affiliation(s)
- S Vinci-Booher
- Indiana University, 1101 E. 10th Street, Bloomington, IN, 47405, USA.
| | - B Caron
- Indiana University, 1101 E. 10th Street, Bloomington, IN, 47405, USA
| | - D Bullock
- Indiana University, 1101 E. 10th Street, Bloomington, IN, 47405, USA
| | - K James
- Indiana University, 1101 E. 10th Street, Bloomington, IN, 47405, USA
| | - F Pestilli
- Indiana University, 1101 E. 10th Street, Bloomington, IN, 47405, USA.
- The University of Texas, 108 E Dean Keeton St, Austin, TX, 78712, USA.
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27
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Schaeffer DJ, Klassen LM, Hori Y, Tian X, Szczupak D, Yen CCC, Cléry JC, Gilbert KM, Gati JS, Menon RS, Liu C, Everling S, Silva AC. An open access resource for functional brain connectivity from fully awake marmosets. Neuroimage 2022; 252:119030. [PMID: 35217206 PMCID: PMC9048130 DOI: 10.1016/j.neuroimage.2022.119030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 01/19/2022] [Accepted: 02/21/2022] [Indexed: 12/27/2022] Open
Abstract
The common marmoset (Callithrix jacchus) is quickly gaining traction as a premier neuroscientific model. However, considerable progress is still needed in understanding the functional and structural organization of the marmoset brain to rival that documented in longstanding preclinical model species, like mice, rats, and Old World primates. To accelerate such progress, we present the Marmoset Functional Brain Connectivity Resource (marmosetbrainconnectome.org), currently consisting of over 70 h of resting-state fMRI (RS-fMRI) data acquired at 500 µm isotropic resolution from 31 fully awake marmosets in a common stereotactic space. Three-dimensional functional connectivity (FC) maps for every cortical and subcortical gray matter voxel are stored online. Users can instantaneously view, manipulate, and download any whole-brain functional connectivity (FC) topology (at the subject- or group-level) along with the raw datasets and preprocessing code. Importantly, researchers can use this resource to test hypotheses about FC directly - with no additional analyses required - yielding whole-brain correlations for any gray matter voxel on demand. We demonstrate the resource's utility for presurgical planning and comparison with tracer-based neuronal connectivity as proof of concept. Complementing existing structural connectivity resources for the marmoset brain, the Marmoset Functional Brain Connectivity Resource affords users the distinct advantage of exploring the connectivity of any voxel in the marmoset brain, not limited to injection sites nor constrained by regional atlases. With the entire raw database (RS-fMRI and structural images) and preprocessing code openly available for download and use, we expect this resource to be broadly valuable to test novel hypotheses about the functional organization of the marmoset brain.
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Affiliation(s)
- David J Schaeffer
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15261, United States.
| | - L Martyn Klassen
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Yuki Hori
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Xiaoguang Tian
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15261, United States
| | - Diego Szczupak
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15261, United States
| | - Cecil Chern-Chyi Yen
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Justine C Cléry
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Kyle M Gilbert
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Joseph S Gati
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Ravi S Menon
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada; Department of Physiology and Pharmacology, University of Western Ontario, London, Ontario, Canada
| | - CiRong Liu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Stefan Everling
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada; Department of Physiology and Pharmacology, University of Western Ontario, London, Ontario, Canada
| | - Afonso C Silva
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15261, United States
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28
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Kaneko T, Komatsu M, Yamamori T, Ichinohe N, Okano H. Cortical neural dynamics unveil the rhythm of natural visual behavior in marmosets. Commun Biol 2022; 5:108. [PMID: 35115680 PMCID: PMC8814246 DOI: 10.1038/s42003-022-03052-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 01/13/2022] [Indexed: 01/13/2023] Open
Abstract
Numerous studies have shown that the visual system consists of functionally distinct ventral and dorsal streams; however, its exact spatial-temporal dynamics during natural visual behavior remain to be investigated. Here, we report cerebral neural dynamics during active visual exploration recorded by an electrocorticographic array covering the entire lateral surface of the marmoset cortex. We found that the dorsal stream was activated before the primary visual cortex with saccades and followed by the alteration of suppression and activation signals along the ventral stream. Similarly, the signal that propagated from the dorsal to ventral visual areas was accompanied by a travelling wave of low frequency oscillations. Such signal dynamics occurred at an average of 220 ms after saccades, which corresponded to the timing when whole-brain activation returned to background levels. We also demonstrated that saccades could occur at any point of signal flow, indicating the parallel computation of motor commands. Overall, this study reveals the neural dynamics of active vision, which are efficiently linked to the natural rhythms of visual exploration.
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Affiliation(s)
- Takaaki Kaneko
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, Japan. .,Systems Neuroscience Section, Primate Research Institute, Kyoto University, Aichi, Japan.
| | - Misako Komatsu
- Laboratory for Molecular Analysis of Higher Brain Function, Center for Brain Science, RIKEN, Saitama, Japan
| | - Tetsuo Yamamori
- Laboratory for Molecular Analysis of Higher Brain Function, Center for Brain Science, RIKEN, Saitama, Japan
| | - Noritaka Ichinohe
- Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, Japan. .,Department of Physiology, Keio University School of Medicine, Tokyo, Japan.
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29
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Ose T, Autio JA, Ohno M, Frey S, Uematsu A, Kawasaki A, Takeda C, Hori Y, Nishigori K, Nakako T, Yokoyama C, Nagata H, Yamamori T, Van Essen DC, Glasser MF, Watabe H, Hayashi T. Anatomical variability, multi-modal coordinate systems, and precision targeting in the marmoset brain. Neuroimage 2022; 250:118965. [PMID: 35122965 PMCID: PMC8948178 DOI: 10.1016/j.neuroimage.2022.118965] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 01/31/2022] [Accepted: 02/01/2022] [Indexed: 01/02/2023] Open
Abstract
Localising accurate brain regions needs careful evaluation in each experimental species due to their individual variability. However, the function and connectivity of brain areas is commonly studied using a single-subject cranial landmark-based stereotactic atlas in animal neuroscience. Here, we address this issue in a small primate, the common marmoset, which is increasingly widely used in systems neuroscience. We developed a non-invasive multi-modal neuroimaging-based targeting pipeline, which accounts for intersubject anatomical variability in cranial and cortical landmarks in marmosets. This methodology allowed creation of multi-modal templates (MarmosetRIKEN20) including head CT and brain MR images, embedded in coordinate systems of anterior and posterior commissures (AC-PC) and CIFTI grayordinates. We found that the horizontal plane of the stereotactic coordinate was significantly rotated in pitch relative to the AC-PC coordinate system (10 degrees, frontal downwards), and had a significant bias and uncertainty due to positioning procedures. We also found that many common cranial and brain landmarks (e.g., bregma, intraparietal sulcus) vary in location across subjects and are substantial relative to average marmoset cortical area dimensions. Combining the neuroimaging-based targeting pipeline with robot-guided surgery enabled proof-of-concept targeting of deep brain structures with an accuracy of 0.2 mm. Altogether, our findings demonstrate substantial intersubject variability in marmoset brain and cranial landmarks, implying that subject-specific neuroimaging-based localization is needed for precision targeting in marmosets. The population-based templates and atlases in grayordinates, created for the first time in marmoset monkeys, should help bridging between macroscale and microscale analyses.
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Affiliation(s)
- Takayuki Ose
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan; Graduate School of Biomedical Engineering, Tohoku University, Sendai, Japan.
| | - Joonas A Autio
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
| | - Masahiro Ohno
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
| | | | - Akiko Uematsu
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
| | - Akihiro Kawasaki
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
| | - Chiho Takeda
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
| | - Yuki Hori
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan; Department of Functional Brain Imaging, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan.
| | - Kantaro Nishigori
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan; Sumitomo Dainippon Pharma Co., Ltd., Osaka, Japan.
| | - Tomokazu Nakako
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan; Sumitomo Dainippon Pharma Co., Ltd., Osaka, Japan.
| | - Chihiro Yokoyama
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan; Faculty of Human life and Environmental Science, Nara women's University, Nara, Japan.
| | | | - Tetsuo Yamamori
- Laboratory for Molecular Analysis of Higher Brain Function, RIKEN Center for Brain Science, Wako, Japan.
| | - David C Van Essen
- Department of Neuroscience, Washington University Medical School, St Louis, MO USA.
| | - Matthew F Glasser
- Department of Neuroscience, Washington University Medical School, St Louis, MO USA; Department of Radiology, Washington University Medical School, St Louis, MO USA.
| | - Hiroshi Watabe
- Graduate School of Biomedical Engineering, Tohoku University, Sendai, Japan.
| | - Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan; Department of Brain Connectomics, Kyoto University Graduate School of Medicine, Kyoto, Japan.
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30
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Xu R, Bichot NP, Takahashi A, Desimone R. The cortical connectome of primate lateral prefrontal cortex. Neuron 2022; 110:312-327.e7. [PMID: 34739817 PMCID: PMC8776613 DOI: 10.1016/j.neuron.2021.10.018] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/09/2021] [Accepted: 10/11/2021] [Indexed: 01/21/2023]
Abstract
The lateral prefrontal cortex (LPFC) of primates plays an important role in executive control, but how it interacts with the rest of the cortex remains unclear. To address this, we densely mapped the cortical connectome of LPFC, using electrical microstimulation combined with functional MRI (EM-fMRI). We found isomorphic mappings between LPFC and five major processing domains composing most of the cerebral cortex except early sensory and motor areas. An LPFC grid of ∼200 stimulation sites topographically mapped to separate grids of activation sites in the five domains, coarsely resembling how the visual cortex maps the retina. The temporal and parietal maps largely overlapped in LPFC, suggesting topographically organized convergence of the ventral and dorsal streams, and the other maps overlapped at least partially. Thus, the LPFC contains overlapping, millimeter-scale maps that mirror the organization of major cortical processing domains, supporting LPFC's role in coordinating activity within and across these domains.
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Affiliation(s)
- Rui Xu
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Narcisse P Bichot
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Atsushi Takahashi
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Robert Desimone
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
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31
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Ruthig P, Schönwiesner M. Common principles in the lateralisation of auditory cortex structure and function for vocal communication in primates and rodents. Eur J Neurosci 2022; 55:827-845. [PMID: 34984748 DOI: 10.1111/ejn.15590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 12/24/2021] [Indexed: 11/27/2022]
Abstract
This review summarises recent findings on the lateralisation of communicative sound processing in the auditory cortex (AC) of humans, non-human primates, and rodents. Functional imaging in humans has demonstrated a left hemispheric preference for some acoustic features of speech, but it is unclear to which degree this is caused by bottom-up acoustic feature selectivity or top-down modulation from language areas. Although non-human primates show a less pronounced functional lateralisation in AC, the properties of AC fields and behavioral asymmetries are qualitatively similar. Rodent studies demonstrate microstructural circuits that might underlie bottom-up acoustic feature selectivity in both hemispheres. Functionally, the left AC in the mouse appears to be specifically tuned to communication calls, whereas the right AC may have a more 'generalist' role. Rodents also show anatomical AC lateralisation, such as differences in size and connectivity. Several of these functional and anatomical characteristics are also lateralized in human AC. Thus, complex vocal communication processing shares common features among rodents and primates. We argue that a synthesis of results from humans, non-human primates, and rodents is necessary to identify the neural circuitry of vocal communication processing. However, data from different species and methods are often difficult to compare. Recent advances may enable better integration of methods across species. Efforts to standardise data formats and analysis tools would benefit comparative research and enable synergies between psychological and biological research in the area of vocal communication processing.
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Affiliation(s)
- Philip Ruthig
- Faculty of Life Sciences, Leipzig University, Leipzig, Sachsen.,Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig
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32
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van Albada SJ, Morales-Gregorio A, Dickscheid T, Goulas A, Bakker R, Bludau S, Palm G, Hilgetag CC, Diesmann M. Bringing Anatomical Information into Neuronal Network Models. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:201-234. [DOI: 10.1007/978-3-030-89439-9_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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33
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Kwan C, Kang MS, Nuara SG, Gourdon JC, Bédard D, Tardif CL, Hopewell R, Ross K, Bdair H, Hamadjida A, Massarweh G, Soucy JP, Luo W, Del Cid Pellitero E, Shlaifer I, Durcan TM, Fon EA, Rosa-Neto P, Frey S, Huot P. Co-registration of Imaging Modalities (MRI, CT and PET) to Perform Frameless Stereotaxic Robotic Injections in the Common Marmoset. Neuroscience 2021; 480:143-154. [PMID: 34774970 DOI: 10.1016/j.neuroscience.2021.11.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 11/02/2021] [Accepted: 11/03/2021] [Indexed: 11/17/2022]
Abstract
The common marmoset has emerged as a popular model in neuroscience research, in part due to its reproductive efficiency, genetic and neuroanatomical similarities to humans and the successful generation of transgenic lines. Stereotaxic procedures in marmosets are guided by 2D stereotaxic atlases, which are constructed with a limited number of animals and fail to account for inter-individual variability in skull and brain size. Here, we developed a frameless imaging-guided stereotaxic system that improves upon traditional approaches by using subject-specific registration of computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) data to identify a surgical target, namely the putamen, in two marmosets. The skull surface was laser-scanned to create a point cloud that was registered to the 3D reconstruction of the skull from CT. Reconstruction of the skull, as well as of the brain from MR images, was crucial for surgical planning. Localisation and injection into the putamen was done using a 6-axis robotic arm controlled by a surgical navigation software (Brainsight™). Integration of subject-specific registration and frameless stereotaxic navigation allowed target localisation specific to each animal. Injection of alpha-synuclein fibrils into the putamen triggered progressive neurodegeneration of the nigro-striatal system, a key feature of Parkinson's disease. Four months post-surgery, a PET scan found evidence of nigro-striatal denervation, supporting accurate targeting of the putamen during co-registration and subsequent surgery. Our results suggest that this approach, coupled with frameless stereotaxic neuronavigation, is accurate in localising surgical targets and can be used to assess endpoints for longitudinal studies.
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Affiliation(s)
- Cynthia Kwan
- Neurodegenerative Disease Group, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada
| | - Min Su Kang
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, QC, Canada
| | - Stephen G Nuara
- Comparative Medicine & Animal Resource Centre, McGill University, Montreal, QC, Canada
| | - Jim C Gourdon
- Comparative Medicine & Animal Resource Centre, McGill University, Montreal, QC, Canada
| | - Dominique Bédard
- Neurodegenerative Disease Group, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada
| | - Christine L Tardif
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; Department of Biomedical Engineering, McGill University, Montreal, QC, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada
| | - Robert Hopewell
- Neurodegenerative Disease Group, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada
| | - Karen Ross
- Neurodegenerative Disease Group, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada
| | - Hussein Bdair
- Neurodegenerative Disease Group, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada
| | - Adjia Hamadjida
- Neurodegenerative Disease Group, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada
| | - Gassan Massarweh
- Neurodegenerative Disease Group, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada
| | - Jean-Paul Soucy
- McConnell Brain Imaging Centre, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada
| | - Wen Luo
- Neurodegenerative Disease Group, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada; The Neuro's Early Drug Discovery Unit, McGill University, Montreal, QC, Canada
| | - Esther Del Cid Pellitero
- Neurodegenerative Disease Group, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; Movement Disorder Clinic, Division of Neurology, Department of Neuroscience, McGill University Health Centre, Montreal, QC, Canada
| | - Irina Shlaifer
- Neurodegenerative Disease Group, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada; The Neuro's Early Drug Discovery Unit, McGill University, Montreal, QC, Canada
| | - Thomas M Durcan
- Neurodegenerative Disease Group, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada; The Neuro's Early Drug Discovery Unit, McGill University, Montreal, QC, Canada
| | - Edward A Fon
- Neurodegenerative Disease Group, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; Movement Disorder Clinic, Division of Neurology, Department of Neuroscience, McGill University Health Centre, Montreal, QC, Canada
| | - Pedro Rosa-Neto
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada
| | | | - Philippe Huot
- Neurodegenerative Disease Group, Montreal Neurological Institute-Hospital (The Neuro), Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; Movement Disorder Clinic, Division of Neurology, Department of Neuroscience, McGill University Health Centre, Montreal, QC, Canada.
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34
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Kim JH, Ryu JR, Lee B, Chae U, Son JW, Park BH, Cho IJ, Sun W. Interpreting the Entire Connectivity of Individual Neurons in Micropatterned Neural Culture With an Integrated Connectome Analyzer of a Neuronal Network (iCANN). Front Neuroanat 2021; 15:746057. [PMID: 34744642 PMCID: PMC8564400 DOI: 10.3389/fnana.2021.746057] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 09/23/2021] [Indexed: 11/13/2022] Open
Abstract
The function of a neural circuit can be determined by the following: (1) characteristics of individual neurons composing the circuit, (2) their distinct connection structure, and (3) their neural circuit activity. However, prior research on correlations between these three factors revealed many limitations. In particular, profiling and modeling of the connectivity of complex neural circuits at the cellular level are highly challenging. To reduce the burden of the analysis, we suggest a new approach with simplification of the neural connection in an array of honeycomb patterns on 2D, using a microcontact printing technique. Through a series of guided neuronal growths in defined honeycomb patterns, a simplified neuronal circuit was achieved. Our approach allowed us to obtain the whole network connectivity at cellular resolution using a combination of stochastic multicolor labeling via viral transfection. Therefore, we were able to identify several types of hub neurons with distinct connectivity features. We also compared the structural differences between different circuits using three-node motif analysis. This new model system, iCANN, is the first experimental model of neural computation at the cellular level, providing neuronal circuit structures for the study of the relationship between anatomical structure and function of the neuronal network.
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Affiliation(s)
- June Hoan Kim
- Department of Anatomy, Korea University College of Medicine, Seoul, South Korea
| | - Jae Ryun Ryu
- Department of Anatomy, Korea University College of Medicine, Seoul, South Korea
| | - Boram Lee
- Department of Anatomy, Korea University College of Medicine, Seoul, South Korea
| | - Uikyu Chae
- Center for BioMicrosystems, Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, South Korea
| | - Jong Wan Son
- Division of Quantum Phases and Devices, Department of Physics, Konkuk University, Seoul, South Korea
| | - Bae Ho Park
- Division of Quantum Phases and Devices, Department of Physics, Konkuk University, Seoul, South Korea
| | - Il-Joo Cho
- Center for BioMicrosystems, Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, South Korea
| | - Woong Sun
- Department of Anatomy, Korea University College of Medicine, Seoul, South Korea
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35
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Du J, Buckner RL. Precision Estimates of Macroscale Network Organization in the Human and Their Relation to Anatomical Connectivity in the Marmoset Monkey. Curr Opin Behav Sci 2021; 40:144-152. [PMID: 34722833 DOI: 10.1016/j.cobeha.2021.04.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Precision estimates of network organization from functional connectivity MRI in the human and tract-tracing data in the marmoset monkey converge to reveal an orderly macroscale gradient of sequential networks across the cerebral cortex. Parallel networks begin with a sequence of multiple nested sensory-motor networks in both species progressing to more distributed association networks in rostral prefrontal and temporal association zones, which are expanded and differentiated in the human. From this perspective, the spatially-distributed motif encountered in association networks appears to be on a continuum with primary sensory-motor networks. Network motifs supporting sophisticated forms of human cognition may arise from specializations of distributed anatomical networks formed in an ancestor at least 45 million years ago.
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Affiliation(s)
- Jingnan Du
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
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36
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Scott JT, Bourne JA. Modelling behaviors relevant to brain disorders in the nonhuman primate: Are we there yet? Prog Neurobiol 2021; 208:102183. [PMID: 34728308 DOI: 10.1016/j.pneurobio.2021.102183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 10/27/2021] [Accepted: 10/27/2021] [Indexed: 12/30/2022]
Abstract
Recent years have seen a profound resurgence of activity with nonhuman primates (NHPs) to model human brain disorders. From marmosets to macaques, the study of NHP species offers a unique window into the function of primate-specific neural circuits that are impossible to examine in other models. Examining how these circuits manifest into the complex behaviors of primates, such as advanced cognitive and social functions, has provided enormous insights to date into the mechanisms underlying symptoms of numerous neurological and neuropsychiatric illnesses. With the recent optimization of modern techniques to manipulate and measure neural activity in vivo, such as optogenetics and calcium imaging, NHP research is more well-equipped than ever to probe the neural mechanisms underlying pathological behavior. However, methods for behavioral experimentation and analysis in NHPs have noticeably failed to keep pace with these advances. As behavior ultimately lies at the junction between preclinical findings and its translation to clinical outcomes for brain disorders, approaches to improve the integrity, reproducibility, and translatability of behavioral experiments in NHPs requires critical evaluation. In this review, we provide a unifying account of existing brain disorder models using NHPs, and provide insights into the present and emerging contributions of behavioral studies to the field.
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Affiliation(s)
- Jack T Scott
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC, Australia
| | - James A Bourne
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC, Australia.
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37
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Visual Cortical Area MT Is Required for Development of the Dorsal Stream and Associated Visuomotor Behaviors. J Neurosci 2021; 41:8197-8209. [PMID: 34417331 DOI: 10.1523/jneurosci.0824-21.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 07/07/2021] [Accepted: 08/09/2021] [Indexed: 01/11/2023] Open
Abstract
The middle temporal (MT) area of the extrastriate visual cortex has long been studied in adulthood for its distinctive physiological properties and function as a part of the dorsal stream, yet interestingly it possesses a similar maturation profile as the primary visual cortex (V1). Here, we examined whether an early-life lesion in MT of marmoset monkeys (six female, two male) altered the dorsal stream development and the behavioral precision of reaching-to-grasp sequences. We observed permanent changes in the anatomy of cortices associated with both reaching (parietal and medial intraparietal areas) and grasping (anterior intraparietal area), as well as in reaching-and-grasping behaviors. In addition, we observed a significant impact on the anatomy of V1 and the direction sensitivity of V1 neurons in the lesion projection zone. These findings indicate that area MT is a crucial node in the development of primate vision, affecting both V1 and areas in the dorsal visual pathway known to mediate visually guided manual behaviors.SIGNIFICANCE STATEMENT Previous studies have identified a role for the MT area of the visual cortex in perceiving motion, yet none have examined its central role in the development of the visual cortex and in the establishment of visuomotor behaviors. To address this, we used a unilateral MT lesion model in neonatal marmosets before examining the anatomic, physiological, and behavioral consequences. In adulthood, we observed perturbations in goal-orientated reach-and-grasp behavior, altered direction selectivity of V1 neurons, and changes in the cytoarchitecture throughout dorsal stream areas. This study highlights the importance of MT as a central node in visual system development and consequential visuomotor activity.
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38
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Watanabe S, Kurotani T, Oga T, Noguchi J, Isoda R, Nakagami A, Sakai K, Nakagaki K, Sumida K, Hoshino K, Saito K, Miyawaki I, Sekiguchi M, Wada K, Minamimoto T, Ichinohe N. Functional and molecular characterization of a non-human primate model of autism spectrum disorder shows similarity with the human disease. Nat Commun 2021; 12:5388. [PMID: 34526497 PMCID: PMC8443557 DOI: 10.1038/s41467-021-25487-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 08/12/2021] [Indexed: 02/08/2023] Open
Abstract
Autism spectrum disorder (ASD) is a multifactorial disorder with characteristic synaptic and gene expression changes. Early intervention during childhood is thought to benefit prognosis. Here, we examined the changes in cortical synaptogenesis, synaptic function, and gene expression from birth to the juvenile stage in a marmoset model of ASD induced by valproic acid (VPA) treatment. Early postnatally, synaptogenesis was reduced in this model, while juvenile-age VPA-treated marmosets showed increased synaptogenesis, similar to observations in human tissue. During infancy, synaptic plasticity transiently increased and was associated with altered vocalization. Synaptogenesis-related genes were downregulated early postnatally. At three months of age, the differentially expressed genes were associated with circuit remodeling, similar to the expression changes observed in humans. In summary, we provide a functional and molecular characterization of a non-human primate model of ASD, highlighting its similarity to features observed in human ASD.
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Affiliation(s)
- Satoshi Watanabe
- grid.419280.60000 0004 1763 8916Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo Japan
| | - Tohru Kurotani
- grid.419280.60000 0004 1763 8916Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo Japan
| | - Tomofumi Oga
- grid.419280.60000 0004 1763 8916Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo Japan
| | - Jun Noguchi
- grid.419280.60000 0004 1763 8916Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo Japan
| | - Risa Isoda
- grid.419280.60000 0004 1763 8916Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo Japan
| | - Akiko Nakagami
- grid.419280.60000 0004 1763 8916Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo Japan ,grid.411827.90000 0001 2230 656XDepartment of Psychology, Japan Women’s University, Kawasaki, Kanagawa Japan
| | - Kazuhisa Sakai
- grid.419280.60000 0004 1763 8916Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo Japan
| | - Keiko Nakagaki
- grid.419280.60000 0004 1763 8916Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo Japan
| | - Kayo Sumida
- grid.459996.e0000 0004 0376 2692Environmental Health Science Laboratory, Sumitomo Chemical Co., Ltd., Konohana-ku, Osaka, Japan
| | - Kohei Hoshino
- grid.417741.00000 0004 1797 168XPreclinical Research Laboratories, Sumitomo Dainippon Pharma Co., Ltd., Konohana-ku, Osaka, Japan
| | - Koichi Saito
- grid.459996.e0000 0004 0376 2692Environmental Health Science Laboratory, Sumitomo Chemical Co., Ltd., Konohana-ku, Osaka, Japan
| | - Izuru Miyawaki
- grid.417741.00000 0004 1797 168XPreclinical Research Laboratories, Sumitomo Dainippon Pharma Co., Ltd., Konohana-ku, Osaka, Japan
| | - Masayuki Sekiguchi
- grid.419280.60000 0004 1763 8916Department of Degenerative Neurological Diseases, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo Japan
| | - Keiji Wada
- grid.419280.60000 0004 1763 8916Department of Degenerative Neurological Diseases, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo Japan
| | - Takafumi Minamimoto
- grid.482503.80000 0004 5900 003XDepartment of Functional Brain Imaging, National Institutes for Quantum and Radiological Science and Technology, Chiba, Chiba, Japan
| | - Noritaka Ichinohe
- grid.419280.60000 0004 1763 8916Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo Japan
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D'Souza JF, Price NSC, Hagan MA. Marmosets: a promising model for probing the neural mechanisms underlying complex visual networks such as the frontal-parietal network. Brain Struct Funct 2021; 226:3007-3022. [PMID: 34518902 PMCID: PMC8541938 DOI: 10.1007/s00429-021-02367-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 08/23/2021] [Indexed: 01/02/2023]
Abstract
The technology, methodology and models used by visual neuroscientists have provided great insights into the structure and function of individual brain areas. However, complex cognitive functions arise in the brain due to networks comprising multiple interacting cortical areas that are wired together with precise anatomical connections. A prime example of this phenomenon is the frontal–parietal network and two key regions within it: the frontal eye fields (FEF) and lateral intraparietal area (area LIP). Activity in these cortical areas has independently been tied to oculomotor control, motor preparation, visual attention and decision-making. Strong, bidirectional anatomical connections have also been traced between FEF and area LIP, suggesting that the aforementioned visual functions depend on these inter-area interactions. However, advancements in our knowledge about the interactions between area LIP and FEF are limited with the main animal model, the rhesus macaque, because these key regions are buried in the sulci of the brain. In this review, we propose that the common marmoset is the ideal model for investigating how anatomical connections give rise to functionally-complex cognitive visual behaviours, such as those modulated by the frontal–parietal network, because of the homology of their cortical networks with humans and macaques, amenability to transgenic technology, and rich behavioural repertoire. Furthermore, the lissencephalic structure of the marmoset brain enables application of powerful techniques, such as array-based electrophysiology and optogenetics, which are critical to bridge the gaps in our knowledge about structure and function in the brain.
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Affiliation(s)
- Joanita F D'Souza
- Department of Physiology and Neuroscience Program, Biomedicine Discovery Institute, Monash University, 26 Innovation Walk, Clayton, VIC, 3800, Australia.,Australian Research Council, Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, VIC, 3800, Australia
| | - Nicholas S C Price
- Department of Physiology and Neuroscience Program, Biomedicine Discovery Institute, Monash University, 26 Innovation Walk, Clayton, VIC, 3800, Australia.,Australian Research Council, Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, VIC, 3800, Australia
| | - Maureen A Hagan
- Department of Physiology and Neuroscience Program, Biomedicine Discovery Institute, Monash University, 26 Innovation Walk, Clayton, VIC, 3800, Australia. .,Australian Research Council, Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, VIC, 3800, Australia.
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40
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Hori Y, Cléry JC, Schaeffer DJ, Menon RS, Everling S. Functional Organization of Frontoparietal Cortex in the Marmoset Investigated with Awake Resting-State fMRI. Cereb Cortex 2021; 32:1965-1977. [PMID: 34515315 DOI: 10.1093/cercor/bhab328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 08/13/2021] [Accepted: 08/14/2021] [Indexed: 11/12/2022] Open
Abstract
Frontoparietal networks contribute to complex cognitive functions in humans and macaques, such as working memory, attention, task-switching, response suppression, grasping, reaching, and eye movement control. However, there has been no comprehensive examination of the functional organization of frontoparietal networks using functional magnetic resonance imaging in the New World common marmoset monkey (Callithrix jacchus), which is now widely recognized as a powerful nonhuman primate experimental animal. In this study, we employed hierarchical clustering of interareal blood oxygen level-dependent signals to investigate the hypothesis that the organization of the frontoparietal cortex in the marmoset follows the organizational principles of the macaque frontoparietal system. We found that the posterior part of the lateral frontal cortex (premotor regions) was functionally connected to the anterior parietal areas, while more anterior frontal regions (frontal eye field [FEF]) were connected to more posterior parietal areas (the region around the lateral intraparietal area [LIP]). These overarching patterns of interareal organization are consistent with a recent macaque study. These findings demonstrate parallel frontoparietal processing streams in marmosets and support the functional similarities of FEF-LIP and premotor-anterior parietal pathways between marmoset and macaque.
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Affiliation(s)
- Yuki Hori
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario N6A 5B7, Canada
| | - Justine C Cléry
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario N6A 5B7, Canada
| | - David J Schaeffer
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Ravi S Menon
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario N6A 5B7, Canada
| | - Stefan Everling
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario N6A 5B7, Canada.,Department of Physiology and Pharmacology, The University of Western Ontario, London, Ontario N6A 5C1, Canada
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41
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Goulas A, Damicelli F, Hilgetag CC. Bio-instantiated recurrent neural networks: Integrating neurobiology-based network topology in artificial networks. Neural Netw 2021; 142:608-618. [PMID: 34391175 DOI: 10.1016/j.neunet.2021.07.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 06/21/2021] [Accepted: 07/08/2021] [Indexed: 11/19/2022]
Abstract
Biological neuronal networks (BNNs) are a source of inspiration and analogy making for researchers that focus on artificial neuronal networks (ANNs). Moreover, neuroscientists increasingly use ANNs as a model for the brain. Despite certain similarities between these two types of networks, important differences can be discerned. First, biological neural networks are sculpted by evolution and the constraints that it entails, whereas artificial neural networks are engineered to solve particular tasks. Second, the network topology of these systems, apart from some analogies that can be drawn, exhibits pronounced differences. Here, we examine strategies to construct recurrent neural networks (RNNs) that instantiate the network topology of brains of different species. We refer to such RNNs as bio-instantiated. We investigate the performance of bio-instantiated RNNs in terms of: (i) the prediction performance itself, that is, the capacity of the network to minimize the cost function at hand in test data, and (ii) speed of training, that is, how fast during training the network reaches its optimal performance. We examine bio-instantiated RNNs in working memory tasks where task-relevant information must be tracked as a sequence of events unfolds in time. We highlight the strategies that can be used to construct RNNs with the network topology found in BNNs, without sacrificing performance. Despite that we observe no enhancement of performance when compared to randomly wired RNNs, our approach demonstrates how empirical neural network data can be used for constructing RNNs, thus, facilitating further experimentation with biologically realistic network topologies, in contexts where such aspect is desired.
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Affiliation(s)
- Alexandros Goulas
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistr 52, 20246 Hamburg, Germany.
| | - Fabrizio Damicelli
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistr 52, 20246 Hamburg, Germany
| | - Claus C Hilgetag
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistr 52, 20246 Hamburg, Germany; Health Sciences Department, Boston University, Boston, MA 02215, USA
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42
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Retrograde Transgene Expression via Neuron-Specific Lentiviral Vector Depends on Both Species and Input Projections. Viruses 2021; 13:v13071387. [PMID: 34372593 PMCID: PMC8310113 DOI: 10.3390/v13071387] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/12/2021] [Accepted: 07/14/2021] [Indexed: 12/22/2022] Open
Abstract
For achieving retrograde gene transfer, we have so far developed two types of lentiviral vectors pseudotyped with fusion envelope glycoprotein, termed HiRet vector and NeuRet vector, consisting of distinct combinations of rabies virus and vesicular stomatitis virus glycoproteins. In the present study, we compared the patterns of retrograde transgene expression for the HiRet vs. NeuRet vectors by testing the cortical input system. These vectors were injected into the motor cortex in rats, marmosets, and macaques, and the distributions of retrograde labels were investigated in the cortex and thalamus. Our histological analysis revealed that the NeuRet vector generally exhibits a higher efficiency of retrograde gene transfer than the HiRet vector, though its capacity of retrograde transgene expression in the macaque brain is unexpectedly low, especially in terms of the intracortical connections, as compared to the rat and marmoset brains. It was also demonstrated that the NeuRet but not the HiRet vector displays sufficiently high neuron specificity and causes no marked inflammatory/immune responses at the vector injection sites in the primate (marmoset and macaque) brains. The present results indicate that the retrograde transgene efficiency of the NeuRet vector varies depending not only on the species but also on the input projections.
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43
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Bakola S, Burman KJ, Bednarek S, Chan JM, Jermakow N, Worthy KH, Majka P, Rosa MGP. Afferent Connections of Cytoarchitectural Area 6M and Surrounding Cortex in the Marmoset: Putative Homologues of the Supplementary and Pre-supplementary Motor Areas. Cereb Cortex 2021; 32:41-62. [PMID: 34255833 DOI: 10.1093/cercor/bhab193] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 06/07/2021] [Accepted: 06/07/2021] [Indexed: 01/02/2023] Open
Abstract
Cortical projections to the caudomedial frontal cortex were studied using retrograde tracers in marmosets. We tested the hypothesis that cytoarchitectural area 6M includes homologues of the supplementary and pre-supplementary motor areas (SMA and pre-SMA) of other primates. We found that, irrespective of the injection sites' location within 6M, over half of the labeled neurons were located in motor and premotor areas. Other connections originated in prefrontal area 8b, ventral anterior and posterior cingulate areas, somatosensory areas (3a and 1-2), and areas on the rostral aspect of the dorsal posterior parietal cortex. Although the origin of afferents was similar, injections in rostral 6M received higher percentages of prefrontal afferents, and fewer somatosensory afferents, compared to caudal injections, compatible with differentiation into SMA and pre-SMA. Injections rostral to 6M (area 8b) revealed a very different set of connections, with increased emphasis on prefrontal and posterior cingulate afferents, and fewer parietal afferents. The connections of 6M were also quantitatively different from those of the primary motor cortex, dorsal premotor areas, and cingulate motor area 24d. These results show that the cortical motor control circuit is conserved in simian primates, indicating that marmosets can be valuable models for studying movement planning and control.
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Affiliation(s)
- Sophia Bakola
- Department of Physiology and Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia.,Monash University Node, ARC Centre of Excellence for Integrative Brain Function, Monash University, Clayton, VIC 3800, Australia
| | - Kathleen J Burman
- Department of Physiology and Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia.,Monash University Node, ARC Centre of Excellence for Integrative Brain Function, Monash University, Clayton, VIC 3800, Australia
| | - Sylwia Bednarek
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, 02-093 Warsaw, Poland
| | - Jonathan M Chan
- Department of Physiology and Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia.,Monash University Node, ARC Centre of Excellence for Integrative Brain Function, Monash University, Clayton, VIC 3800, Australia
| | - Natalia Jermakow
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, 02-093 Warsaw, Poland
| | - Katrina H Worthy
- Department of Physiology and Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Piotr Majka
- Monash University Node, ARC Centre of Excellence for Integrative Brain Function, Monash University, Clayton, VIC 3800, Australia.,Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, 02-093 Warsaw, Poland
| | - Marcello G P Rosa
- Department of Physiology and Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia.,Monash University Node, ARC Centre of Excellence for Integrative Brain Function, Monash University, Clayton, VIC 3800, Australia
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44
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Duan LY, Horst NK, Cranmore SAW, Horiguchi N, Cardinal RN, Roberts AC, Robbins TW. Controlling one's world: Identification of sub-regions of primate PFC underlying goal-directed behavior. Neuron 2021; 109:2485-2498.e5. [PMID: 34171290 PMCID: PMC8346232 DOI: 10.1016/j.neuron.2021.06.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 04/13/2021] [Accepted: 06/02/2021] [Indexed: 12/30/2022]
Abstract
Impaired detection of causal relationships between actions and their outcomes can lead to maladaptive behavior. However, causal roles of specific prefrontal cortex (PFC) sub-regions and the caudate nucleus in mediating such relationships in primates are unclear. We inactivated and overactivated five PFC sub-regions, reversibly and pharmacologically: areas 24 (perigenual anterior cingulate cortex), 32 (medial PFC), 11 (anterior orbitofrontal cortex, OFC), 14 (rostral ventromedial PFC/medial OFC), and 14-25 (caudal ventromedial PFC) and the anteromedial caudate to examine their role in expressing learned action-outcome contingencies using a contingency degradation paradigm in marmoset monkeys. Area 24 or caudate inactivation impaired the response to contingency change, while area 11 inactivation enhanced it, and inactivation of areas 14, 32, or 14-25 had no effect. Overactivation of areas 11 and 24 impaired this response. These findings demonstrate the distinct roles of PFC sub-regions in goal-directed behavior and illuminate the candidate neurobehavioral substrates of psychiatric disorders, including obsessive-compulsive disorder. Monkey pgACC-24 is necessary for detecting causal control of actions over outcomes Its projection target in the caudate nucleus is also implicated Three other subregions of the ventromedial prefrontal cortex are not necessary Anterior OFC-11 may mediate Pavlovian influences on goal-directed behavior
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Affiliation(s)
- Lisa Y Duan
- Department of Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, UK; Behavioural and Clinical Neuroscience Institute, Downing Street, University of Cambridge, Cambridge CB2 3EB, UK.
| | - Nicole K Horst
- Department of Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, UK; Behavioural and Clinical Neuroscience Institute, Downing Street, University of Cambridge, Cambridge CB2 3EB, UK
| | - Stacey A W Cranmore
- Department of Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, UK; Behavioural and Clinical Neuroscience Institute, Downing Street, University of Cambridge, Cambridge CB2 3EB, UK
| | - Naotaka Horiguchi
- Department of Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, UK; Behavioural and Clinical Neuroscience Institute, Downing Street, University of Cambridge, Cambridge CB2 3EB, UK
| | - Rudolf N Cardinal
- Department of Psychiatry, University of Cambridge, Herchel Smith Building for Brain & Mind Sciences, Forvie Site, Robinson Way, Cambridge CB2 0SZ, UK; Behavioural and Clinical Neuroscience Institute, Downing Street, University of Cambridge, Cambridge CB2 3EB, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Liaison Psychiatry Service, Box 190, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Angela C Roberts
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK; Behavioural and Clinical Neuroscience Institute, Downing Street, University of Cambridge, Cambridge CB2 3EB, UK
| | - Trevor W Robbins
- Department of Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, UK; Behavioural and Clinical Neuroscience Institute, Downing Street, University of Cambridge, Cambridge CB2 3EB, UK
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45
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Doble PA, de Vega RG, Bishop DP, Hare DJ, Clases D. Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry Imaging in Biology. Chem Rev 2021; 121:11769-11822. [PMID: 34019411 DOI: 10.1021/acs.chemrev.0c01219] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Elemental imaging gives insight into the fundamental chemical makeup of living organisms. Every cell on Earth is comprised of a complex and dynamic mixture of the chemical elements that define structure and function. Many disease states feature a disturbance in elemental homeostasis, and understanding how, and most importantly where, has driven the development of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) as the principal elemental imaging technique for biologists. This review provides an outline of ICP-MS technology, laser ablation cell designs, imaging workflows, and methods of quantification. Detailed examples of imaging applications including analyses of cancers, elemental uptake and accumulation, plant bioimaging, nanomaterials in the environment, and exposure science and neuroscience are presented and discussed. Recent incorporation of immunohistochemical workflows for imaging biomolecules, complementary and multimodal imaging techniques, and image processing methods is also reviewed.
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Affiliation(s)
- Philip A Doble
- Atomic Medicine Initiative, University of Technology Sydney, Broadway, New South Wales 2007, Australia
| | - Raquel Gonzalez de Vega
- Atomic Medicine Initiative, University of Technology Sydney, Broadway, New South Wales 2007, Australia
| | - David P Bishop
- Atomic Medicine Initiative, University of Technology Sydney, Broadway, New South Wales 2007, Australia
| | - Dominic J Hare
- Atomic Medicine Initiative, University of Technology Sydney, Broadway, New South Wales 2007, Australia.,School of BioSciences, University of Melbourne, Parkville, Victoria 3052, Australia
| | - David Clases
- Atomic Medicine Initiative, University of Technology Sydney, Broadway, New South Wales 2007, Australia
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46
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Hayashi T, Hou Y, Glasser MF, Autio JA, Knoblauch K, Inoue-Murayama M, Coalson T, Yacoub E, Smith S, Kennedy H, Van Essen DC. The nonhuman primate neuroimaging and neuroanatomy project. Neuroimage 2021; 229:117726. [PMID: 33484849 PMCID: PMC8079967 DOI: 10.1016/j.neuroimage.2021.117726] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 12/13/2020] [Accepted: 01/02/2021] [Indexed: 11/29/2022] Open
Abstract
Multi-modal neuroimaging projects such as the Human Connectome Project (HCP) and UK Biobank are advancing our understanding of human brain architecture, function, connectivity, and their variability across individuals using high-quality non-invasive data from many subjects. Such efforts depend upon the accuracy of non-invasive brain imaging measures. However, 'ground truth' validation of connectivity using invasive tracers is not feasible in humans. Studies using nonhuman primates (NHPs) enable comparisons between invasive and non-invasive measures, including exploration of how "functional connectivity" from fMRI and "tractographic connectivity" from diffusion MRI compare with long-distance connections measured using tract tracing. Our NonHuman Primate Neuroimaging & Neuroanatomy Project (NHP_NNP) is an international effort (6 laboratories in 5 countries) to: (i) acquire and analyze high-quality multi-modal brain imaging data of macaque and marmoset monkeys using protocols and methods adapted from the HCP; (ii) acquire quantitative invasive tract-tracing data for cortical and subcortical projections to cortical areas; and (iii) map the distributions of different brain cell types with immunocytochemical stains to better define brain areal boundaries. We are acquiring high-resolution structural, functional, and diffusion MRI data together with behavioral measures from over 100 individual macaques and marmosets in order to generate non-invasive measures of brain architecture such as myelin and cortical thickness maps, as well as functional and diffusion tractography-based connectomes. We are using classical and next-generation anatomical tracers to generate quantitative connectivity maps based on brain-wide counting of labeled cortical and subcortical neurons, providing ground truth measures of connectivity. Advanced statistical modeling techniques address the consistency of both kinds of data across individuals, allowing comparison of tracer-based and non-invasive MRI-based connectivity measures. We aim to develop improved cortical and subcortical areal atlases by combining histological and imaging methods. Finally, we are collecting genetic and sociality-associated behavioral data in all animals in an effort to understand how genetic variation shapes the connectome and behavior.
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Affiliation(s)
- Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, 6-7-3 MI R&D Center 3F, Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan; Department of Neurobiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yujie Hou
- Inserm, Stem Cell and Brain Research Institute U1208, Univ Lyon, Université Claude Bernard Lyon 1, Bron, France
| | - Matthew F Glasser
- Department of Neuroscience, Washington University Medical School, St Louis, MO USA; Department of Neuroscience and Radiology, Washington University Medical School, St Louis, MO USA
| | - Joonas A Autio
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, 6-7-3 MI R&D Center 3F, Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan
| | - Kenneth Knoblauch
- Inserm, Stem Cell and Brain Research Institute U1208, Univ Lyon, Université Claude Bernard Lyon 1, Bron, France
| | | | - Tim Coalson
- Department of Neuroscience, Washington University Medical School, St Louis, MO USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, USA
| | - Stephen Smith
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, UK
| | - Henry Kennedy
- Inserm, Stem Cell and Brain Research Institute U1208, Univ Lyon, Université Claude Bernard Lyon 1, Bron, France; Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences (CAS) Key Laboratory of Primate Neurobiology, CAS, Shanghai, China
| | - David C Van Essen
- Department of Neuroscience, Washington University Medical School, St Louis, MO USA
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47
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Atapour N, Worthy KH, Rosa MGP. Neurochemical changes in the primate lateral geniculate nucleus following lesions of striate cortex in infancy and adulthood: implications for residual vision and blindsight. Brain Struct Funct 2021; 226:2763-2775. [PMID: 33743077 DOI: 10.1007/s00429-021-02257-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/10/2021] [Indexed: 12/16/2022]
Abstract
Following lesions of the primary visual cortex (V1), the lateral geniculate nucleus (LGN) undergoes substantial cell loss due to retrograde degeneration. However, visually responsive neurons remain in the degenerated sector of LGN, and these have been implicated in mediation of residual visual capacities that remain within the affected sectors of the visual field. Using immunohistochemistry, we compared the neurochemical characteristics of LGN neurons in V1-lesioned marmoset monkeys (Callithrix jacchus) with those of non-lesioned control animals. We found that GABAergic neurons form approximately 6.5% of the neuronal population in the normal LGN, where most of these cells express the calcium-binding protein parvalbumin. Following long-term V1 lesions in adult monkeys, we observed a marked increase (~ sevenfold) in the proportion of GABA-expressing neurons in the degenerated sector of the LGN, indicating that GABAergic cells are less affected by retrograde degeneration in comparison with magno- and parvocellular projection neurons. In addition, following early postnatal V1 lesions and survival into adulthood, we found widespread expression of GABA in putative projection neurons, even outside the degenerated sectors (lesion projection zones). Our findings show that changes in the ratio of GABAergic neurons in LGN need to be taken into account in the interpretation of the mechanisms of visual abilities that survive V1 lesions in primates.
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Affiliation(s)
- Nafiseh Atapour
- Neuroscience Program, Biomedicine Discovery Institute and Department of Physiology, Monash University, Melbourne, VIC, 3800, Australia. .,Australian Research Council, Centre of Excellence for Integrative Brain Function, Monash University Node, Melbourne, VIC, Australia.
| | - Katrina H Worthy
- Neuroscience Program, Biomedicine Discovery Institute and Department of Physiology, Monash University, Melbourne, VIC, 3800, Australia
| | - Marcello G P Rosa
- Neuroscience Program, Biomedicine Discovery Institute and Department of Physiology, Monash University, Melbourne, VIC, 3800, Australia.,Australian Research Council, Centre of Excellence for Integrative Brain Function, Monash University Node, Melbourne, VIC, Australia
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48
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Schaeffer DJ, Liu C, Silva AC, Everling S. Magnetic Resonance Imaging of Marmoset Monkeys. ILAR J 2021; 61:274-285. [PMID: 33631015 PMCID: PMC8918195 DOI: 10.1093/ilar/ilaa029] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 09/22/2020] [Accepted: 10/23/2020] [Indexed: 11/12/2022] Open
Abstract
The use of the common marmoset monkey (Callithrix jacchus) for neuroscientific research has grown markedly in the last decade. Magnetic resonance imaging (MRI) has played a significant role in establishing the extent of comparability of marmoset brain architecture with the human brain and brains of other preclinical species (eg, macaques and rodents). As a non-invasive technique, MRI allows for the flexible acquisition of the same sequences across different species in vivo, including imaging of whole-brain functional topologies not possible with more invasive techniques. Being one of the smallest New World primates, the marmoset may be an ideal nonhuman primate species to study with MRI. As primates, marmosets have an elaborated frontal cortex with features analogous to the human brain, while also having a small enough body size to fit into powerful small-bore MRI systems typically employed for rodent imaging; these systems offer superior signal strength and resolution. Further, marmosets have a rich behavioral repertoire uniquely paired with a lissencephalic cortex (like rodents). This smooth cortical surface lends itself well to MRI and also other invasive methodologies. With the advent of transgenic modification techniques, marmosets have gained significant traction as a powerful complement to canonical mammalian modelling species. Marmosets are poised to make major contributions to preclinical investigations of the pathophysiology of human brain disorders as well as more basic mechanistic explorations of the brain. The goal of this article is to provide an overview of the practical aspects of implementing MRI and fMRI in marmosets (both under anesthesia and fully awake) and discuss the development of resources recently made available for marmoset imaging.
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Affiliation(s)
- David J Schaeffer
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - CiRong Liu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Afonso C Silva
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Stefan Everling
- Department of Physiology and Pharmacology, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
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49
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Yang Y, Chen K, Rosa MGP, Yu HH, Kuang LR, Yang J. Visual response characteristics of neurons in the second visual area of marmosets. Neural Regen Res 2021; 16:1871-1876. [PMID: 33510095 PMCID: PMC8328785 DOI: 10.4103/1673-5374.303043] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
The physiological characteristics of the marmoset second visual area (V2) are poorly understood compared with those of the primary visual area (V1). In this study, we observed the physiological response characteristics of V2 neurons in four healthy adult marmosets using intracortical tungsten microelectrodes. We recorded 110 neurons in area V2, with receptive fields located between 8° and 15° eccentricity. Most (88.2%) of these neurons were orientation selective, with half-bandwidths typically ranging between 10° and 30°. A significant proportion of neurons (28.2%) with direction selectivity had a direction index greater than 0.5. The vast majority of V2 neurons had separable spatial frequency and temporal frequency curves and, according to this criterion, they were not speed selective. The basic functional response characteristics of neurons in area V2 resemble those found in area V1. Our findings show that area V2 together with V1 are important in primate visual processing, especially in locating objects in space and in detecting an object’s direction of motion. The methods used in this study were approved by the Monash University Animal Ethics Committee, Australia (MARP 2009-2011) in 2009.
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Affiliation(s)
- Yin Yang
- Department of Ophthalmology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital; College of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China
| | - Ke Chen
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China
| | - Marcello G P Rosa
- Department of Physiology, Monash University, Melbourne, VIC, Australia
| | - Hsin-Hao Yu
- Department of Physiology, Monash University, Melbourne, VIC, Australia
| | - Li-Rong Kuang
- Chengdu Medical College, Chengdu, Sichuan Province, China
| | - Jie Yang
- College of Medicine, University of Electronic Science and Technology of China; Department of Neurology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, Sichuan Province, China
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50
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Majka P, Bednarek S, Chan JM, Jermakow N, Liu C, Saworska G, Worthy KH, Silva AC, Wójcik DK, Rosa MGP. Histology-Based Average Template of the Marmoset Cortex With Probabilistic Localization of Cytoarchitectural Areas. Neuroimage 2020; 226:117625. [PMID: 33301940 DOI: 10.1016/j.neuroimage.2020.117625] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 11/19/2020] [Accepted: 12/01/2020] [Indexed: 12/25/2022] Open
Abstract
The rapid adoption of marmosets in neuroscience has created a demand for three dimensional (3D) atlases of the brain of this species to facilitate data integration in a common reference space. We report on a new open access template of the marmoset cortex (the Nencki-Monash, or NM template), representing a morphological average of 20 brains of young adult individuals, obtained by 3D reconstructions generated from Nissl-stained serial sections. The method used to generate the template takes into account morphological features of the individual brains, as well as the borders of clearly defined cytoarchitectural areas. This has resulted in a resource which allows direct estimates of the most likely coordinates of each cortical area, as well as quantification of the margins of error involved in assigning voxels to areas, and preserves quantitative information about the laminar structure of the cortex. We provide spatial transformations between the NM and other available marmoset brain templates, thus enabling integration with magnetic resonance imaging (MRI) and tracer-based connectivity data. The NM template combines some of the main advantages of histology-based atlases (e.g. information about the cytoarchitectural structure) with features more commonly associated with MRI-based templates (isotropic nature of the dataset, and probabilistic analyses). The underlying workflow may be found useful in the future development of 3D brain atlases that incorporate information about the variability of areas in species for which it may be impractical to ensure homogeneity of the sample in terms of age, sex and genetic background.
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Affiliation(s)
- Piotr Majka
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, 02-093 Warsaw, Poland; Australian Research Council, Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, VIC 3800, Australia; Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC 3800, Australia.
| | - Sylwia Bednarek
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, 02-093 Warsaw, Poland
| | - Jonathan M Chan
- Australian Research Council, Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, VIC 3800, Australia; Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC 3800, Australia
| | - Natalia Jermakow
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, 02-093 Warsaw, Poland
| | - Cirong Liu
- Department of Neurobiology, University of Pittsburgh Brain Institute, Pittsburgh, PA, USA
| | - Gabriela Saworska
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, 02-093 Warsaw, Poland
| | - Katrina H Worthy
- Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC 3800, Australia
| | - Afonso C Silva
- Department of Neurobiology, University of Pittsburgh Brain Institute, Pittsburgh, PA, USA
| | - Daniel K Wójcik
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, 02-093 Warsaw, Poland; Institute of Applied Psychology, Faculty of Management and Social Communication, Jagiellonian University, 30-348 Cracow, Poland
| | - Marcello G P Rosa
- Australian Research Council, Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, VIC 3800, Australia; Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC 3800, Australia
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