1
|
Pandurangan K, Jayakumar J, Savoia S, Nanda R, Lata S, Kumar EH, S S, Vasudevan S, Srinivasan C, Joseph J, Sivaprakasam M, Verma R. Systematic development of immunohistochemistry protocol for large cryosections-specific to non-perfused fetal brain. J Neurosci Methods 2024; 405:110085. [PMID: 38387804 DOI: 10.1016/j.jneumeth.2024.110085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 02/01/2024] [Accepted: 02/18/2024] [Indexed: 02/24/2024]
Abstract
BACKGROUND Immunohistochemistry (IHC) is an important technique in understanding the expression of neurochemical molecules in the developing human brain. Despite its routine application in the research and clinical setup, the IHC protocol specific for soft fragile fetal brains that are fixed using the non-perfusion method is still limited in studying the whole brain. NEW METHOD This study shows that the IHC protocols, using a chromogenic detection system, used in animals and adult humans are not optimal in the fetal brains. We have optimized key steps from Antigen retrieval (AR) to chromogen visualization for formalin-fixed whole-brain cryosections (20 µm) mounted on glass slides. RESULTS We show the results from six validated, commonly used antibodies to study the fetal brain. We achieved optimal antigen retrieval with 0.1 M Boric Acid, pH 9.0 at 70°C for 20 minutes. We also present the optimal incubation duration and temperature for protein blocking and the primary antibody that results in specific antigen labeling with minimal tissue damage. COMPARISON WITH EXISTING METHODS The IHC protocol commonly used for adult human and animal brains results in significant tissue damage in the fetal brains with little or suboptimal antigen expression. Our new method with important modifications including the temperature, duration, and choice of the alkaline buffer for AR addresses these pitfalls and provides high-quality results. CONCLUSION The optimized IHC protocol for the developing human brain (13-22 GW) provides a high-quality, repeatable, and reliable method for studying chemoarchitecture in neurotypical and pathological conditions across different gestational ages.
Collapse
Affiliation(s)
- Karthika Pandurangan
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
| | - Jaikishan Jayakumar
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; Center for Computational Brain Research, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
| | | | - Reetuparna Nanda
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
| | - S Lata
- Mediscan Systems, Chennai, Tamil Nadu, India.
| | | | - Suresh S
- Mediscan Systems, Chennai, Tamil Nadu, India.
| | - Sudha Vasudevan
- Department of Obstetrics & Gynaecology, Saveetha Medical College, Thandalam, Chennai, Tamil Nadu, India.
| | - Chitra Srinivasan
- Department of Pathology, Saveetha Medical College, Thandalam, Chennai, Tamil Nadu, India.
| | - Jayaraj Joseph
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; Department of Electrical Engineering, Indian Institute of Technology, Madras, Chennai, Tamil Nadu, India.
| | - Mohanasankar Sivaprakasam
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; Department of Electrical Engineering, Indian Institute of Technology, Madras, Chennai, Tamil Nadu, India.
| | - Richa Verma
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
| |
Collapse
|
2
|
Verma R, Jayakumar J, Folkerth R, Manger PR, Bota M, Majumder M, Pandurangan K, Savoia S, Karthik S, Kumarasami R, Joseph J, Rohini G, Vasudevan S, Srinivasan C, Lata S, Kumar EH, Rangasami R, Kumutha J, Suresh S, Šimić G, Mitra PP, Sivaprakasam M. Histological characterization and development of mesial surface sulci in the human brain at 13-15 gestational weeks through high-resolution histology. J Comp Neurol 2024; 532:e25612. [PMID: 38591638 DOI: 10.1002/cne.25612] [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: 10/06/2023] [Revised: 03/06/2024] [Accepted: 03/24/2024] [Indexed: 04/10/2024]
Abstract
Cellular-level anatomical data from early fetal brain are sparse yet critical to the understanding of neurodevelopmental disorders. We characterize the organization of the human cerebral cortex between 13 and 15 gestational weeks using high-resolution whole-brain histological data sets complimented with multimodal imaging. We observed the heretofore underrecognized, reproducible presence of infolds on the mesial surface of the cerebral hemispheres. Of note at this stage, when most of the cerebrum is occupied by lateral ventricles and the corpus callosum is incompletely developed, we postulate that these mesial infolds represent the primordial stage of cingulate, callosal, and calcarine sulci, features of mesial cortical development. Our observations are based on the multimodal approach and further include histological three-dimensional reconstruction that highlights the importance of the plane of sectioning. We describe the laminar organization of the developing cortical mantle, including these infolds from the marginal to ventricular zone, with Nissl, hematoxylin and eosin, and glial fibrillary acidic protein (GFAP) immunohistochemistry. Despite the absence of major sulci on the dorsal surface, the boundaries among the orbital, frontal, parietal, and occipital cortex were very well demarcated, primarily by the cytoarchitecture differences in the organization of the subplate (SP) and intermediate zone (IZ) in these locations. The parietal region has the thickest cortical plate (CP), SP, and IZ, whereas the orbital region shows the thinnest CP and reveals an extra cell-sparse layer above the bilaminar SP. The subcortical structures show intensely GFAP-immunolabeled soma, absent in the cerebral mantle. Our findings establish a normative neurodevelopment baseline at the early stage.
Collapse
Affiliation(s)
- Richa Verma
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Jaikishan Jayakumar
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Center for Computational Brain Research, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Rebecca Folkerth
- Department of Forensic Medicine, NYU Grossman School of Medicine, New York, New York, USA
| | - Paul R Manger
- School of Anatomical Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Mihail Bota
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Moitrayee Majumder
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Karthika Pandurangan
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | | | - Srinivasa Karthik
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Ramdayalan Kumarasami
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Jayaraj Joseph
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Department of Electrical Engineering, Indian Institute of Technology, Madras, Chennai, Tamil Nadu, India
| | - G Rohini
- Department of Obstetrics & Gynaecology, Saveetha Medical College, Thandalam, Chennai, Tamil Nadu, India
| | - Sudha Vasudevan
- Department of Pathology, Saveetha Medical College, Thandalam, Chennai, Tamil Nadu, India
| | - Chitra Srinivasan
- Department of Pathology, Saveetha Medical College, Thandalam, Chennai, Tamil Nadu, India
| | - S Lata
- Mediscan Systems, Chennai, Tamil Nadu, India
| | | | - Rajeswaran Rangasami
- Department of Radiology, Sri Ramachandra Institute of Higher Education and Research, Chennai, Tamil Nadu, India
| | - Jayaraman Kumutha
- Department of Neonatology, Saveetha Medical College, Thandalam, Chennai, Tamil Nadu, India
| | - S Suresh
- Mediscan Systems, Chennai, Tamil Nadu, India
| | - Goran Šimić
- Department of Neuroscience, Croatian Institute for Brain Research, University of Zagreb Medical School, Zagreb, Hrvatska, Croatia
| | - Partha P Mitra
- Center for Computational Brain Research, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Cold Spring Harbor Laboratory, New York, New York, USA
| | - Mohanasankar Sivaprakasam
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Department of Electrical Engineering, Indian Institute of Technology, Madras, Chennai, Tamil Nadu, India
| |
Collapse
|
3
|
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.
Collapse
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.
| |
Collapse
|
4
|
Parks TV, Szczupak D, Choi SH, Schaeffer DJ. Noninvasive focal transgene delivery with viral neuronal tracers in the marmoset monkey. CELL REPORTS METHODS 2024; 4:100709. [PMID: 38359822 PMCID: PMC10921014 DOI: 10.1016/j.crmeth.2024.100709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 12/14/2023] [Accepted: 01/23/2024] [Indexed: 02/17/2024]
Abstract
We establish a reliable method for selectively delivering adeno-associated viral vectors (AAVs) across the blood-brain barrier (BBB) in the marmoset without the need for neurosurgical injection. We focally perturbed the BBB (∼1 × 2 mm) in area 8aD of the frontal cortex in four adult marmoset monkeys using low-intensity transcranial focused ultrasound aided by microbubbles. Within an hour of opening the BBB, either AAV2 or AAV9 was delivered systemically via tail-vein injection. In all four marmosets, fluorescence-encoded neurons were observed at the site of BBB perturbation, with AAV2 showing a sparse distribution of transduced neurons when compared to AAV9. The results are compared to direct intracortical injections of anterograde tracers into area 8aD and similar (albeit sparser) long-range connectivity was observed. With evidence of transduced neurons specific to the region of BBB opening as well as long-distance tracing, we establish a framework for focal noninvasive transgene delivery to the marmoset brain.
Collapse
Affiliation(s)
- T Vincenza Parks
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Diego Szczupak
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Sang-Ho Choi
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - David J Schaeffer
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA.
| |
Collapse
|
5
|
Meisner OC, Fagan NA, Greenwood J, Jadi MP, Nandy AS, Chang SWC. Development of a Marmoset Apparatus for Automated Pulling (MarmoAAP) to Study Cooperative Behaviors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.16.579531. [PMID: 38405744 PMCID: PMC10889019 DOI: 10.1101/2024.02.16.579531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
In recent years, the field of neuroscience has increasingly recognized the importance of studying animal behaviors in naturalistic environments to gain deeper insights into ethologically relevant behavioral processes and neural mechanisms. The common marmoset (Callithrix jacchus), due to its small size, prosocial nature, and genetic proximity to humans, has emerged as a pivotal model toward this effort. However, traditional research methodologies often fail to fully capture the nuances of marmoset social interactions and cooperative behaviors. To address this critical gap, we developed the Marmoset Apparatus for Automated Pulling (MarmoAAP), a novel behavioral apparatus designed for studying cooperative behaviors in common marmosets. MarmoAAP addresses the limitations of traditional behavioral research methods by enabling high-throughput, detailed behavior outputs that can be integrated with video and audio recordings, allowing for more nuanced and comprehensive analyses even in a naturalistic setting. We also highlight the flexibility of MarmoAAP in task parameter manipulation which accommodates a wide range of behaviors and individual animal capabilities. Furthermore, MarmoAAP provides a platform to perform investigations of neural activity underlying naturalistic social behaviors. MarmoAAP is a versatile and robust tool for advancing our understanding of primate behavior and related cognitive processes. This new apparatus bridges the gap between ethologically relevant animal behavior studies and neural investigations, paving the way for future research in cognitive and social neuroscience using marmosets as a model organism.
Collapse
Affiliation(s)
- Olivia C Meisner
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06510, USA
- Department of Psychology, Yale University, New Haven, CT 06520, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Nicholas A Fagan
- Department of Psychology, Yale University, New Haven, CT 06520, USA
| | - Joel Greenwood
- Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Monika P Jadi
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06510, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06510, USA
- Department of Psychiatry, Yale University, New Haven, CT 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06510, USA
| | - Anirvan S Nandy
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06510, USA
- Department of Psychology, Yale University, New Haven, CT 06520, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06510, USA
- Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT 06510, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06510, USA
| | - Steve W C Chang
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06510, USA
- Department of Psychology, Yale University, New Haven, CT 06520, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06510, USA
- Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT 06510, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06510, USA
| |
Collapse
|
6
|
Jiang T, Gong H, Yuan J. Whole-brain Optical Imaging: A Powerful Tool for Precise Brain Mapping at the Mesoscopic Level. Neurosci Bull 2023; 39:1840-1858. [PMID: 37715920 PMCID: PMC10661546 DOI: 10.1007/s12264-023-01112-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 05/08/2023] [Indexed: 09/18/2023] Open
Abstract
The mammalian brain is a highly complex network that consists of millions to billions of densely-interconnected neurons. Precise dissection of neural circuits at the mesoscopic level can provide important structural information for understanding the brain. Optical approaches can achieve submicron lateral resolution and achieve "optical sectioning" by a variety of means, which has the natural advantage of allowing the observation of neural circuits at the mesoscopic level. Automated whole-brain optical imaging methods based on tissue clearing or histological sectioning surpass the limitation of optical imaging depth in biological tissues and can provide delicate structural information in a large volume of tissues. Combined with various fluorescent labeling techniques, whole-brain optical imaging methods have shown great potential in the brain-wide quantitative profiling of cells, circuits, and blood vessels. In this review, we summarize the principles and implementations of various whole-brain optical imaging methods and provide some concepts regarding their future development.
Collapse
Affiliation(s)
- Tao Jiang
- Huazhong University of Science and Technology-Suzhou Institute for Brainsmatics, Jiangsu Industrial Technology Research Institute, Suzhou, 215123, China
| | - Hui Gong
- Huazhong University of Science and Technology-Suzhou Institute for Brainsmatics, Jiangsu Industrial Technology Research Institute, Suzhou, 215123, China
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Jing Yuan
- Huazhong University of Science and Technology-Suzhou Institute for Brainsmatics, Jiangsu Industrial Technology Research Institute, Suzhou, 215123, China.
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
| |
Collapse
|
7
|
James RI, Verma R, Johnson LR, Manesh A, Jayakumar J, Sen M, Joseph J, Kumarasami R, Mitra PP, Sivaprakasam M, Varghese GM. A Standardized Protocol for the Safe Retrieval of Infectious Postmortem Human Brain for Studying Whole-Brain Pathology. Am J Forensic Med Pathol 2023; 44:303-310. [PMID: 37490584 PMCID: PMC10662599 DOI: 10.1097/paf.0000000000000871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
ABSTRACT We describe a safe and standardized perfusion protocol for studying brain pathology in high-risk autopsies using a custom-designed low-cost infection containment chamber and high-resolution histology. The output quality was studied using the histological data from the whole cerebellum and brain stem processed using a high-resolution cryohistology pipeline at 0.5 μm per pixel, in-plane resolution with serial sections at 20-μm thickness. To understand the pathophysiology of highly infectious diseases, it is necessary to have a safe and cost-effective method of performing high-risk autopsies and a standardized perfusion protocol for preparing high-quality tissues. Using the low-cost infection containment chamber, we detail the cranial autopsy protocol and ex situ perfusion-fixation of 4 highly infectious adult human brains. The digitized high-resolution histology images of the Nissl-stained series reveal that most of the sections were free of processing artifacts, such as fixation damage, freezing artifacts, and osmotic shock, at the macrocellular and microcellular level. The quality of our protocol was also tested with the highly sensitive immunohistochemistry staining for specific protein markers. Our protocol provides a safe and effective method in high-risk autopsies that allows for the evaluation of pathogen-host interaction, the underlying pathophysiology, and the extent of the infection across the whole brain at microscopic resolutions.
Collapse
Affiliation(s)
- Ranjit Immanuel James
- From the Department of Forensic Medicine and Toxicology, Christian Medical College, Vellore
| | - Richa Verma
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai
| | - Latif Rajesh Johnson
- From the Department of Forensic Medicine and Toxicology, Christian Medical College, Vellore
| | - Abi Manesh
- Department of Infectious Diseases, Christian Medical College, Vellore
| | - Jaikishan Jayakumar
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai
- Center for Computational Brain Research
| | - Mousumi Sen
- From the Department of Forensic Medicine and Toxicology, Christian Medical College, Vellore
| | - Jayaraj Joseph
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai
- Department of Electrical Engineering
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, India
| | - Ramdayalan Kumarasami
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, India
| | - Partha P. Mitra
- Center for Computational Brain Research
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
| | - Mohanasankar Sivaprakasam
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai
- Department of Electrical Engineering
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, India
| | | |
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
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.
Collapse
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.
| |
Collapse
|
10
|
Kaiser M. Connectomes: from a sparsity of networks to large-scale databases. Front Neuroinform 2023; 17:1170337. [PMID: 37377946 PMCID: PMC10291062 DOI: 10.3389/fninf.2023.1170337] [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: 02/20/2023] [Accepted: 05/26/2023] [Indexed: 06/29/2023] Open
Abstract
The analysis of whole brain networks started in the 1980s when only a handful of connectomes were available. In these early days, information about the human connectome was absent and one could only dream about having information about connectivity in a single human subject. Thanks to non-invasive methods such as diffusion imaging, we now know about connectivity in many species and, for some species, in many individuals. To illustrate the rapid change in availability of connectome data, the UK Biobank is on track to record structural and functional connectivity in 100,000 human subjects. Moreover, connectome data from a range of species is now available: from Caenorhabditis elegans and the fruit fly to pigeons, rodents, cats, non-human primates, and humans. This review will give a brief overview of what structural connectivity data is now available, how connectomes are organized, and how their organization shows common features across species. Finally, I will outline some of the current challenges and potential future work in making use of connectome information.
Collapse
Affiliation(s)
- Marcus Kaiser
- NIHR Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| |
Collapse
|
11
|
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.
Collapse
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.
| |
Collapse
|
12
|
Liu X, Gao T, Lu T, Bao Y, Schumann G, Lu L. China Brain Project: from bench to bedside. Sci Bull (Beijing) 2023; 68:444-447. [PMID: 36822910 DOI: 10.1016/j.scib.2023.02.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Affiliation(s)
- Xiaoxing Liu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit, Peking University, Beijing 100191, China
| | - Teng Gao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit, Peking University, Beijing 100191, China
| | - Tangsheng Lu
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing 100191, China
| | - Yanping Bao
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing 100191, China; School of Public Health, Peking University, Beijing 100191, China
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; PONS Centre, Department of Psychiatry and Psychotherapy, Campus Charite Mitte (CCM), Charite Universitaetsmedizin Berlin, Berlin 10117, Germany
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit, Peking University, Beijing 100191, China; National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing 100191, China; Peking-Tsinghua Centre for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100081, China.
| |
Collapse
|
13
|
Santana NNM, Silva EHA, dos Santos SF, Costa MSMO, Nascimento Junior ES, Engelberth RCJG, Cavalcante JS. Retinorecipient areas in the common marmoset ( Callithrix jacchus): An image-forming and non-image forming circuitry. Front Neural Circuits 2023; 17:1088686. [PMID: 36817647 PMCID: PMC9932520 DOI: 10.3389/fncir.2023.1088686] [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: 11/03/2022] [Accepted: 01/10/2023] [Indexed: 02/05/2023] Open
Abstract
The mammalian retina captures a multitude of diverse features from the external environment and conveys them via the optic nerve to a myriad of retinorecipient nuclei. Understanding how retinal signals act in distinct brain functions is one of the most central and established goals of neuroscience. Using the common marmoset (Callithrix jacchus), a monkey from Northeastern Brazil, as an animal model for parsing how retinal innervation works in the brain, started decades ago due to their marmoset's small bodies, rapid reproduction rate, and brain features. In the course of that research, a large amount of new and sophisticated neuroanatomical techniques was developed and employed to explain retinal connectivity. As a consequence, image and non-image-forming regions, functions, and pathways, as well as retinal cell types were described. Image-forming circuits give rise directly to vision, while the non-image-forming territories support circadian physiological processes, although part of their functional significance is uncertain. Here, we reviewed the current state of knowledge concerning retinal circuitry in marmosets from neuroanatomical investigations. We have also highlighted the aspects of marmoset retinal circuitry that remain obscure, in addition, to identify what further research is needed to better understand the connections and functions of retinorecipient structures.
Collapse
Affiliation(s)
- Nelyane Nayara M. Santana
- Laboratory of Neurochemical Studies, Department of Physiology and Behavior, Bioscience Center, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Eryck H. A. Silva
- Laboratory of Neurochemical Studies, Department of Physiology and Behavior, Bioscience Center, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Sâmarah F. dos Santos
- Laboratory of Neurochemical Studies, Department of Physiology and Behavior, Bioscience Center, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Miriam S. M. O. Costa
- Laboratory of Neuroanatomy, Department of Morphology, Bioscience Center, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Expedito S. Nascimento Junior
- Laboratory of Neuroanatomy, Department of Morphology, Bioscience Center, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Rovena Clara J. G. Engelberth
- Laboratory of Neurochemical Studies, Department of Physiology and Behavior, Bioscience Center, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Jeferson S. Cavalcante
- Laboratory of Neurochemical Studies, Department of Physiology and Behavior, Bioscience Center, Federal University of Rio Grande do Norte, Natal, Brazil,*Correspondence: Jeferson S. Cavalcante,
| |
Collapse
|
14
|
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.
Collapse
|
15
|
Wlodkowic D, Bownik A, Leitner C, Stengel D, Braunbeck T. Beyond the behavioural phenotype: Uncovering mechanistic foundations in aquatic eco-neurotoxicology. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 829:154584. [PMID: 35306067 DOI: 10.1016/j.scitotenv.2022.154584] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 03/09/2022] [Accepted: 03/11/2022] [Indexed: 06/14/2023]
Abstract
During the last decade, there has been an increase in awareness of how anthropogenic pollution can alter behavioural traits of diverse aquatic organisms. Apart from understanding profound ecological implications, alterations in neuro-behavioural indices have emerged as sensitive and physiologically integrative endpoints in chemical risk assessment. Accordingly, behavioural ecotoxicology and broader eco-neurotoxicology are becoming increasingly popular fields of research that span a plethora of fundamental laboratory experimentations as well as applied field-based studies. Despite mounting interest in aquatic behavioural ecotoxicology studies, there is, however, a considerable paucity in deciphering the mechanistic foundations underlying behavioural alterations upon exposure to pollutants. The behavioural phenotype is indeed the highest-level integrative neurobiological phenomenon, but at its core lie myriads of intertwined biochemical, cellular, and physiological processes. Therefore, the mechanisms that underlie changes in behavioural phenotypes can stem among others from dysregulation of neurotransmitter pathways, electrical signalling, and cell death of discrete cell populations in the central and peripheral nervous systems. They can, however, also be a result of toxicity to sensory organs and even metabolic dysfunctions. In this critical review, we outline why behavioural phenotyping should be the starting point that leads to actual discovery of fundamental mechanisms underlying actions of neurotoxic and neuromodulating contaminants. We highlight potential applications of the currently existing and emerging neurobiology and neurophysiology analytical strategies that should be embraced and more broadly adopted in behavioural ecotoxicology. Such strategies can provide new mechanistic discoveries instead of only observing the end sum phenotypic effects.
Collapse
Affiliation(s)
- Donald Wlodkowic
- The Neurotox Laboratory, School of Science, RMIT University, Melbourne, Australia.
| | - Adam Bownik
- Department of Hydrobiology and Protection of Ecosystems, Faculty of Environmental Biology, University of Life Sciences, Lublin, Poland
| | - Carola Leitner
- Aquatic Ecology and Toxicology, Centre for Organismal Studies, University of Heidelberg, Im Neuenheimer Feld 504, D-69120 Heidelberg, Germany
| | - Daniel Stengel
- Aquatic Ecology and Toxicology, Centre for Organismal Studies, University of Heidelberg, Im Neuenheimer Feld 504, D-69120 Heidelberg, Germany
| | - Thomas Braunbeck
- Aquatic Ecology and Toxicology, Centre for Organismal Studies, University of Heidelberg, Im Neuenheimer Feld 504, D-69120 Heidelberg, Germany
| |
Collapse
|
16
|
Guo S, Xue J, Liu J, Ye X, Guo Y, Liu D, Zhao X, Xiong F, Han X, Peng H. Smart imaging to empower brain-wide neuroscience at single-cell levels. Brain Inform 2022; 9:10. [PMID: 35543774 PMCID: PMC9095808 DOI: 10.1186/s40708-022-00158-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/12/2022] [Indexed: 11/10/2022] Open
Abstract
A deep understanding of the neuronal connectivity and networks with detailed cell typing across brain regions is necessary to unravel the mechanisms behind the emotional and memorial functions as well as to find the treatment of brain impairment. Brain-wide imaging with single-cell resolution provides unique advantages to access morphological features of a neuron and to investigate the connectivity of neuron networks, which has led to exciting discoveries over the past years based on animal models, such as rodents. Nonetheless, high-throughput systems are in urgent demand to support studies of neural morphologies at larger scale and more detailed level, as well as to enable research on non-human primates (NHP) and human brains. The advances in artificial intelligence (AI) and computational resources bring great opportunity to 'smart' imaging systems, i.e., to automate, speed up, optimize and upgrade the imaging systems with AI and computational strategies. In this light, we review the important computational techniques that can support smart systems in brain-wide imaging at single-cell resolution.
Collapse
Affiliation(s)
- Shuxia Guo
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China.
| | - Jie Xue
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China
| | - Jian Liu
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China
| | - Xiangqiao Ye
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China
| | - Yichen Guo
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China
| | - Di Liu
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China
| | - Xuan Zhao
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China
| | - Feng Xiong
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China
| | - Xiaofeng Han
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China.
| | - Hanchuan Peng
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China
| |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
Newmaster KT, Kronman FA, Wu YT, Kim Y. Seeing the Forest and Its Trees Together: Implementing 3D Light Microscopy Pipelines for Cell Type Mapping in the Mouse Brain. Front Neuroanat 2022; 15:787601. [PMID: 35095432 PMCID: PMC8794814 DOI: 10.3389/fnana.2021.787601] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 12/02/2021] [Indexed: 12/14/2022] Open
Abstract
The brain is composed of diverse neuronal and non-neuronal cell types with complex regional connectivity patterns that create the anatomical infrastructure underlying cognition. Remarkable advances in neuroscience techniques enable labeling and imaging of these individual cell types and their interactions throughout intact mammalian brains at a cellular resolution allowing neuroscientists to examine microscopic details in macroscopic brain circuits. Nevertheless, implementing these tools is fraught with many technical and analytical challenges with a need for high-level data analysis. Here we review key technical considerations for implementing a brain mapping pipeline using the mouse brain as a primary model system. Specifically, we provide practical details for choosing methods including cell type specific labeling, sample preparation (e.g., tissue clearing), microscopy modalities, image processing, and data analysis (e.g., image registration to standard atlases). We also highlight the need to develop better 3D atlases with standardized anatomical labels and nomenclature across species and developmental time points to extend the mapping to other species including humans and to facilitate data sharing, confederation, and integrative analysis. In summary, this review provides key elements and currently available resources to consider while developing and implementing high-resolution mapping methods.
Collapse
Affiliation(s)
- Kyra T Newmaster
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| | - Fae A Kronman
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| | - Yuan-Ting Wu
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| |
Collapse
|
19
|
Xu F, Shen Y, Ding L, Yang CY, Tan H, Wang H, Zhu Q, Xu R, Wu F, Xiao Y, Xu C, Li Q, Su P, Zhang LI, Dong HW, Desimone R, Xu F, Hu X, Lau PM, Bi GQ. High-throughput mapping of a whole rhesus monkey brain at micrometer resolution. Nat Biotechnol 2021; 39:1521-1528. [PMID: 34312500 DOI: 10.1038/s41587-021-00986-5] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 06/14/2021] [Indexed: 02/06/2023]
Abstract
Whole-brain mesoscale mapping in primates has been hindered by large brain sizes and the relatively low throughput of available microscopy methods. Here, we present an approach that combines primate-optimized tissue sectioning and clearing with ultrahigh-speed fluorescence microscopy implementing improved volumetric imaging with synchronized on-the-fly-scan and readout technique, and is capable of completing whole-brain imaging of a rhesus monkey at 1 × 1 × 2.5 µm3 voxel resolution within 100 h. We also developed a highly efficient method for long-range tracing of sparse axonal fibers in datasets numbering hundreds of terabytes. This pipeline, which we call serial sectioning and clearing, three-dimensional microscopy with semiautomated reconstruction and tracing (SMART), enables effective connectome-scale mapping of large primate brains. With SMART, we were able to construct a cortical projection map of the mediodorsal nucleus of the thalamus and identify distinct turning and routing patterns of individual axons in the cortical folds while approaching their arborization destinations.
Collapse
Affiliation(s)
- Fang Xu
- Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, China.,CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Yan Shen
- CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Lufeng Ding
- CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Chao-Yu Yang
- CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Heng Tan
- Key Laboratory of Animal Models and Human Disease Mechanism, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,Department of Pathology and Pathophysiology, Kunming Medical University, Kunming, China
| | - Hao Wang
- Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, China.,Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
| | - Qingyuan Zhu
- Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Rui Xu
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Fengyi Wu
- CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Yanyang Xiao
- CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Cheng Xu
- Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Qianwei Li
- Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Peng Su
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China
| | - Li I Zhang
- Zilkha Neurogenetic Institute, Center for Neural Circuits & Sensory Processing Disorders, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hong-Wei Dong
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Robert Desimone
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Fuqiang Xu
- CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China.,State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Key Laboratory of Magnetic Resonance in Biological Systems, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China
| | - Xintian Hu
- Key Laboratory of Animal Models and Human Disease Mechanism, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China
| | - Pak-Ming Lau
- Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, China. .,CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China. .,CAS Key Laboratory of Brain Function and Disease, and School of Life Sciences, University of Science and Technology of China, Hefei, China. .,Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China.
| | - Guo-Qiang Bi
- Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, China. .,CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China. .,Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China. .,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China.
| |
Collapse
|
20
|
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.
Collapse
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.
| |
Collapse
|
21
|
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.
Collapse
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
| |
Collapse
|
22
|
Liu C, Yen CCC, Szczupak D, Tian X, Glen D, Silva AC. Marmoset Brain Mapping V3: Population multi-modal standard volumetric and surface-based templates. Neuroimage 2020; 226:117620. [PMID: 33307224 PMCID: PMC7908070 DOI: 10.1016/j.neuroimage.2020.117620] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 10/27/2020] [Accepted: 11/28/2020] [Indexed: 12/14/2022] Open
Abstract
The standard anatomical brain template provides a common space and coordinate system for visualizing and analyzing neuroimaging data from large cohorts of subjects. Previous templates and atlases for the common marmoset brain were either based on data from a single individual or lacked essential functionalities for neuroimaging analysis. Here, we present new population-based in-vivo standard templates and tools derived from multi-modal data of 27 marmosets, including multiple types of T1w and T2w contrast images, DTI contrasts, and large field-of-view MRI and CT images. We performed multi-atlas labeling of anatomical structures on the new templates and constructed highly accurate tissue-type segmentation maps to facilitate volumetric studies. We built fully featured brain surfaces and cortical flat maps to facilitate 3D visualization and surface-based analyses, which are compatible with most surface analyzing tools, including FreeSurfer, AFNI/SUMA, and the Connectome Workbench. Analysis of the MRI and CT datasets revealed significant variations in brain shapes, sizes, and regional volumes of brain structures, highlighting substantial individual variabilities in the marmoset population. Thus, our population-based template and associated tools provide a versatile analysis platform and standard coordinate system for a wide range of MRI and connectome studies of common marmosets. These new template tools comprise version 3 of our Marmoset Brain Mapping Project and are publicly available via marmosetbrainmapping.org/v3.html.
Collapse
Affiliation(s)
- Cirong Liu
- Department of Neurobiology, University of Pittsburgh Brain Institute, 3501 Fifth Avenue, 6065 Biomedical Science Tower 3, Pittsburgh PA, USA; Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Cecil Chern-Chyi Yen
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Diego Szczupak
- Department of Neurobiology, University of Pittsburgh Brain Institute, 3501 Fifth Avenue, 6065 Biomedical Science Tower 3, Pittsburgh PA, USA; Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Xiaoguang Tian
- Department of Neurobiology, University of Pittsburgh Brain Institute, 3501 Fifth Avenue, 6065 Biomedical Science Tower 3, Pittsburgh PA, USA; Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Daniel Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health (NIMH/NIH), Bethesda, MD, USA
| | - Afonso C Silva
- Department of Neurobiology, University of Pittsburgh Brain Institute, 3501 Fifth Avenue, 6065 Biomedical Science Tower 3, Pittsburgh PA, USA; Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
| |
Collapse
|
23
|
Abstract
The common marmoset (Callithrix jacchus), a small New World primate, is receiving substantial attention in the neuroscience and biomedical science fields because its anatomical features, functional and behavioral characteristics, and reproductive features and its amenability to available genetic modification technologies make it an attractive experimental subject. In this review, I outline the progress of marmoset neuroscience research and summarize both the current status (opportunities and limitations) of and the future perspectives on the application of marmosets in neuroscience and disease modeling.
Collapse
Affiliation(s)
- Hideyuki Okano
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo 160-8582, Japan; .,Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako-shi, Saitama 351-0198, Japan
| |
Collapse
|
24
|
Luo T, Deng L, Li A, Zhou C, Shao S, Sun Q, Gong H, Yang X, Li X. Scalable Resin Embedding Method for Large-Volume Brain Tissues with High Fluorescence Preservation Capacity. iScience 2020; 23:101717. [PMID: 33196032 PMCID: PMC7645060 DOI: 10.1016/j.isci.2020.101717] [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: 08/12/2020] [Revised: 10/06/2020] [Accepted: 10/16/2020] [Indexed: 01/15/2023] Open
Abstract
Resin embedding is widely used to dissect the fine structure of bio-tissue with electron and optical microscopy. However, it is difficult to embed large-volume tissues with resin. Here, we modified the formula of LR-White resin to prevent the sample cracking during polymerization process and applied this method to the intact brains of mouse, ferret, and macaque. Meanwhile, we increased the fluorescence preservation rate for green fluorescent protein (GFP) from 73 ± 4.0% to 126 ± 3.0% and tdTomato from 60 ± 3.3% to 117 ± 2.8%. Combined with the whole-brain imaging system, we acquired the cytoarchitectonic information and the circuit information such as individual axon and boutons which were labeled with multiple fluorescent proteins. This method shows great potential in the study of continuous fine microstructure information in large-volume tissues from different species, which can facilitate the neuroscience research and help the understanding of the structure-function relationship in complex bio-tissues. Modified LR-White resin embedding was proposed to embed large-volume tissues Retarder α-methyl-styrene was added to prevent cracking during polymerization Resin formula was modified to preserve multiple fluorescent proteins Microstructure information was acquired from the brains of different species
Collapse
Affiliation(s)
- Ting Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, #1037, Luoyu Road, Wuhan, Hubei 430074, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Lei Deng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, #1037, Luoyu Road, Wuhan, Hubei 430074, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, #1037, Luoyu Road, Wuhan, Hubei 430074, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.,HUST-Suzhou Institute for Brainsmatics, Suzhou 215125, China
| | - Can Zhou
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, #1037, Luoyu Road, Wuhan, Hubei 430074, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Shuai Shao
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, #1037, Luoyu Road, Wuhan, Hubei 430074, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Qingtao Sun
- HUST-Suzhou Institute for Brainsmatics, Suzhou 215125, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, #1037, Luoyu Road, Wuhan, Hubei 430074, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.,HUST-Suzhou Institute for Brainsmatics, Suzhou 215125, China
| | - Xiaoquan Yang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, #1037, Luoyu Road, Wuhan, Hubei 430074, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.,HUST-Suzhou Institute for Brainsmatics, Suzhou 215125, China
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, #1037, Luoyu Road, Wuhan, Hubei 430074, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.,HUST-Suzhou Institute for Brainsmatics, Suzhou 215125, China
| |
Collapse
|
25
|
Common marmoset as a model primate for study of the motor control system. Curr Opin Neurobiol 2020; 64:103-110. [DOI: 10.1016/j.conb.2020.02.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 02/24/2020] [Accepted: 02/25/2020] [Indexed: 02/08/2023]
|
26
|
Banerjee S, Magee L, Wang D, Li X, Huo BX, Jayakumar J, Matho K, Lin MK, Ram K, Sivaprakasam M, Huang J, Wang Y, Mitra PP. Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks. NAT MACH INTELL 2020; 2:585-594. [PMID: 34604701 PMCID: PMC8486300 DOI: 10.1038/s42256-020-0227-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 08/09/2020] [Indexed: 11/09/2022]
Abstract
Understanding of neuronal circuitry at cellular resolution within the brain has relied on neuron tracing methods which involve careful observation and interpretation by experienced neuroscientists. With recent developments in imaging and digitization, this approach is no longer feasible with the large scale (terabyte to petabyte range) images. Machine learning based techniques, using deep networks, provide an efficient alternative to the problem. However, these methods rely on very large volumes of annotated images for training and have error rates that are too high for scientific data analysis, and thus requires a significant volume of human-in-the-loop proofreading. Here we introduce a hybrid architecture combining prior structure in the form of topological data analysis methods, based on discrete Morse theory, with the best-in-class deep-net architectures for the neuronal connectivity analysis. We show significant performance gains using our hybrid architecture on detection of topological structure (e.g. connectivity of neuronal processes and local intensity maxima on axons corresponding to synaptic swellings) with precision/recall close to 90% compared with human observers. We have adapted our architecture to a high performance pipeline capable of semantic segmentation of light microscopic whole-brain image data into a hierarchy of neuronal compartments. We expect that the hybrid architecture incorporating discrete Morse techniques into deep nets will generalize to other data domains.
Collapse
Affiliation(s)
| | - Lucas Magee
- Computer Science and Engineering Department, The Ohio State University, Columbus, OH, USA 43210
| | - Dingkang Wang
- Computer Science and Engineering Department, The Ohio State University, Columbus, OH, USA 43210
| | - Xu Li
- Cold Spring Harbor Laboratory, NY, USA 11724
| | | | - Jaikishan Jayakumar
- Center for Computational Brain Research, Indian Institute of Technology, Chennai, Tamil Nadu, India 600036
| | | | | | - Keerthi Ram
- Center for Computational Brain Research, Indian Institute of Technology, Chennai, Tamil Nadu, India 600036
| | - Mohanasankar Sivaprakasam
- Center for Computational Brain Research, Indian Institute of Technology, Chennai, Tamil Nadu, India 600036
| | - Josh Huang
- Cold Spring Harbor Laboratory, NY, USA 11724
| | - Yusu Wang
- Computer Science and Engineering Department, The Ohio State University, Columbus, OH, USA 43210
| | | |
Collapse
|
27
|
Mancini M, Casamitjana A, Peter L, Robinson E, Crampsie S, Thomas DL, Holton JL, Jaunmuktane Z, Iglesias JE. A multimodal computational pipeline for 3D histology of the human brain. Sci Rep 2020; 10:13839. [PMID: 32796937 PMCID: PMC7429828 DOI: 10.1038/s41598-020-69163-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 06/30/2020] [Indexed: 12/14/2022] Open
Abstract
Ex vivo imaging enables analysis of the human brain at a level of detail that is not possible in vivo with MRI. In particular, histology can be used to study brain tissue at the microscopic level, using a wide array of different stains that highlight different microanatomical features. Complementing MRI with histology has important applications in ex vivo atlas building and in modeling the link between microstructure and macroscopic MR signal. However, histology requires sectioning tissue, hence distorting its 3D structure, particularly in larger human samples. Here, we present an open-source computational pipeline to produce 3D consistent histology reconstructions of the human brain. The pipeline relies on a volumetric MRI scan that serves as undistorted reference, and on an intermediate imaging modality (blockface photography) that bridges the gap between MRI and histology. We present results on 3D histology reconstruction of whole human hemispheres from two donors.
Collapse
Affiliation(s)
- Matteo Mancini
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK.
- CUBRIC, Cardiff University, Cardiff, UK.
- NeuroPoly Lab, Polytechnique Montreal, Montreal, Canada.
| | - Adrià Casamitjana
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Loic Peter
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Eleanor Robinson
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Shauna Crampsie
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - David L Thomas
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
- Leonard Wolfson Experimental Neurology Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Janice L Holton
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Zane Jaunmuktane
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Juan Eugenio Iglesias
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA.
| |
Collapse
|
28
|
Lee BC, Lin MK, Fu Y, Hata J, Miller MI, Mitra PP. Multimodal cross-registration and quantification of metric distortions in marmoset whole brain histology using diffeomorphic mappings. J Comp Neurol 2020; 529:281-295. [PMID: 32406083 DOI: 10.1002/cne.24946] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 03/23/2020] [Accepted: 04/30/2020] [Indexed: 11/08/2022]
Abstract
Whole brain neuroanatomy using tera-voxel light-microscopic data sets is of much current interest. A fundamental problem in this field is the mapping of individual brain data sets to a reference space. Previous work has not rigorously quantified in-vivo to ex-vivo distortions in brain geometry from tissue processing. Further, existing approaches focus on registering unimodal volumetric data; however, given the increasing interest in the marmoset model for neuroscience research and the importance of addressing individual brain architecture variations, new algorithms are necessary to cross-register multimodal data sets including MRIs and multiple histological series. Here we present a computational approach for same-subject multimodal MRI-guided reconstruction of a series of consecutive histological sections, jointly with diffeomorphic mapping to a reference atlas. We quantify the scale change during different stages of brain histological processing using the Jacobian determinant of the diffeomorphic transformations involved. By mapping the final image stacks to the ex-vivo post-fixation MRI, we show that (a) tape-transfer assisted histological sections can be reassembled accurately into 3D volumes with a local scale change of 2.0 ± 0.4% per axis dimension; in contrast, (b) tissue perfusion/fixation as assessed by mapping the in-vivo MRIs to the ex-vivo post fixation MRIs shows a larger median absolute scale change of 6.9 ± 2.1% per axis dimension. This is the first systematic quantification of local metric distortions associated with whole-brain histological processing, and we expect that the results will generalize to other species. These local scale changes will be important for computing local properties to create reference brain maps.
Collapse
Affiliation(s)
- Brian C Lee
- Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Meng K Lin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA
| | - Yan Fu
- Shanghai Jiaotong University, Shanghai, China
| | | | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Partha P Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA
| |
Collapse
|
29
|
Woodward A, Gong R, Abe H, Nakae K, Hata J, Skibbe H, Yamaguchi Y, Ishii S, Okano H, Yamamori T, Ichinohe N. The NanoZoomer artificial intelligence connectomics pipeline for tracer injection studies of the marmoset brain. Brain Struct Funct 2020; 225:1225-1243. [PMID: 32367264 DOI: 10.1007/s00429-020-02073-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 04/13/2020] [Indexed: 11/28/2022]
Abstract
We describe our connectomics pipeline for processing anterograde tracer injection data for the brain of the common marmoset (Callithrix jacchus). Brain sections were imaged using a batch slide scanner (NanoZoomer 2.0-HT) and we used artificial intelligence to precisely segment the tracer signal from the background in the fluorescence images. The shape of each brain was reconstructed by reference to a block-face and all data were mapped into a common 3D brain space with atlas and 2D cortical flat map. To overcome the effect of using a single template atlas to specify cortical boundaries, brains were cyto- and myelo-architectonically annotated to create individual 3D atlases. Registration between the individual and common brain cortical boundaries in the flat map space was done to absorb the variation of each brain and precisely map all tracer injection data into one cortical brain space. We describe the methodology of our pipeline and analyze the accuracy of our tracer segmentation and brain registration approaches. Results show our pipeline can successfully process and normalize tracer injection experiments into a common space, making it suitable for large-scale connectomics studies with a focus on the cerebral cortex.
Collapse
Affiliation(s)
- Alexander Woodward
- Connectome Analysis Unit, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan.
| | - Rui Gong
- Connectome Analysis Unit, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Hiroshi Abe
- Laboratory for Molecular Analysis of Higher Brain Function, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Ken Nakae
- Integrated Systems Biology Laboratory, Department of Systems Science, Graduate School of Informatics, Kyoto University, 36-1 Yoshida-Honmachi, Sakyo, Kyoto, 606-8501, Japan
| | - Junichi Hata
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Henrik Skibbe
- Brain Image Analysis Unit, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Yoko Yamaguchi
- Laboratory for Cognitive Brain Mapping, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Shin Ishii
- Integrated Systems Biology Laboratory, Department of Systems Science, Graduate School of Informatics, Kyoto University, 36-1 Yoshida-Honmachi, Sakyo, Kyoto, 606-8501, Japan
| | - Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Tetsuo Yamamori
- Laboratory for Molecular Analysis of Higher Brain Function, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Noritaka Ichinohe
- Laboratory for Molecular Analysis of Higher Brain Function, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| |
Collapse
|
30
|
Long B, Jiang T, Zhang J, Chen S, Jia X, Xu X, Luo Q, Gong H, Li A, Li X. Mapping the Architecture of Ferret Brains at Single-Cell Resolution. Front Neurosci 2020; 14:322. [PMID: 32351352 PMCID: PMC7174703 DOI: 10.3389/fnins.2020.00322] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 03/19/2020] [Indexed: 12/11/2022] Open
Abstract
Mapping the cytoarchitecture of the whole brain can reveal the organizational logic of neural systems. However, this remains a significant challenge, especially for gyrencephalic brains with a large volume. Here we propose an integrated pipeline for generating a cytoarchitectonic atlas with single-cell resolution of the whole brain. To analyze a large-volume brain, we used a modified en-bloc Nissl staining protocol to achieve uniform staining of large-scale brain specimens from ferret (Mustela putorius furo). By combining whole-brain imaging and big data processing, we established strategies for parsing cytoarchitectural information at a voxel resolution of 0.33 μm × 0.33 μm × 1 μm and terabyte-scale data analysis. Using the cytoarchitectonic datasets for adult ferret brain, we identified giant pyramidal neurons in ferret brains and provide the first report of their morphological diversity, neurochemical phenotype, and distribution patterns in the whole brain in three dimensions. This pipeline will facilitate studies on the organization and development of the mammalian brains, from that of rodents to the gyrencephalic brains of ferret and even primates.
Collapse
Affiliation(s)
- Ben Long
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Tao Jiang
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Jianmin Zhang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Siqi Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Xueyan Jia
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Xiaofeng Xu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| |
Collapse
|
31
|
Open access resource for cellular-resolution analyses of corticocortical connectivity in the marmoset monkey. Nat Commun 2020; 11:1133. [PMID: 32111833 PMCID: PMC7048793 DOI: 10.1038/s41467-020-14858-0] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 02/03/2020] [Indexed: 12/25/2022] Open
Abstract
Understanding the principles of neuronal connectivity requires tools for efficient quantification and visualization of large datasets. The primate cortex is particularly challenging due to its complex mosaic of areas, which in many cases lack clear boundaries. Here, we introduce a resource that allows exploration of results of 143 retrograde tracer injections in the marmoset neocortex. Data obtained in different animals are registered to a common stereotaxic space using an algorithm guided by expert delineation of histological borders, allowing accurate assignment of connections to areas despite interindividual variability. The resource incorporates tools for analyses relative to cytoarchitectural areas, including statistical properties such as the fraction of labeled neurons and the percentage of supragranular neurons. It also provides purely spatial (parcellation-free) data, based on the stereotaxic coordinates of 2 million labeled neurons. This resource helps bridge the gap between high-density cellular connectivity studies in rodents and imaging-based analyses of human brains. Understanding principles of neuronal connectivity requires tools for quantification and visualization of large datasets. Here, the authors introduce an online resource encompassing the coordinates of two million neurons labelled by tracer injections in the marmoset cortex, and analysis tools.
Collapse
|
32
|
Němec P, Osten P. The evolution of brain structure captured in stereotyped cell count and cell type distributions. Curr Opin Neurobiol 2020; 60:176-183. [PMID: 31945723 PMCID: PMC7191610 DOI: 10.1016/j.conb.2019.12.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 12/23/2019] [Accepted: 12/24/2019] [Indexed: 12/13/2022]
Abstract
The stereotyped features of brain structure, such as the distribution, morphology and connectivity of neuronal cell types across brain areas, are those most likely to explain the remarkable capacity of the brain to process information and govern behaviors. Recent advances in anatomical methods, including the simple but versatile isotropic fractionator and several whole-brain labeling, clearing and microscopy methods, have opened the door to an exciting new era in comparative brain anatomy, one that has the potential to transform our understanding of the brain structure-function relationship by representing the evolution of brain complexity in quantitative anatomical features shared across species and species-specific or clade-specific. Here we discuss these methods and their application to mapping brain cell count and cell type distributions-two particularly powerful neural correlates of vertebrate cognitive and behavioral capabilities.
Collapse
Affiliation(s)
- Pavel Němec
- Department of Zoology, Faculty of Science, Charles University, Vinicna 7, 12844 Prague, Czech Republic.
| | - Pavel Osten
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11743, USA.
| |
Collapse
|
33
|
Liu C, Ye FQ, Newman JD, Szczupak D, Tian X, Yen CCC, Majka P, Glen D, Rosa MGP, Leopold DA, Silva AC. A resource for the detailed 3D mapping of white matter pathways in the marmoset brain. Nat Neurosci 2020; 23:271-280. [PMID: 31932765 PMCID: PMC7007400 DOI: 10.1038/s41593-019-0575-0] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 12/10/2019] [Indexed: 12/19/2022]
Abstract
While the fundamental importance of the white matter in supporting neuronal communication is well known, existing publications of primate brains do not feature a detailed description of its complex anatomy. The main barrier to achieving this is that existing primate neuroimaging data have insufficient spatial resolution to resolve white matter pathways fully. Here we present a resource that allows detailed descriptions of white matter structures and trajectories of fiber pathways in the marmoset brain. The resource includes: (1) the highest-resolution diffusion-weighted MRI data available to date, which reveal white matter features not previously described; (2) a comprehensive three-dimensional white matter atlas depicting fiber pathways that were either omitted or misidentified in previous atlases; and (3) comprehensive fiber pathway maps of cortical connections combining diffusion-weighted MRI tractography and neuronal tracing data. The resource, which can be downloaded from marmosetbrainmapping.org, will facilitate studies of brain connectivity and the development of tractography algorithms in the primate brain.
Collapse
Affiliation(s)
- Cirong Liu
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
- Department of Neurobiology, University of Pittsburgh Brain Institute, Pittsburgh, PA, USA.
| | - Frank Q Ye
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, and National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - John D Newman
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
- Section on Quantitative Imaging and Tissue Sciences, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Diego Szczupak
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
- Department of Neurobiology, University of Pittsburgh Brain Institute, Pittsburgh, PA, USA
| | - Xiaoguang Tian
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
- Department of Neurobiology, University of Pittsburgh Brain Institute, Pittsburgh, PA, USA
| | - Cecil Chern-Chyi Yen
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Piotr Majka
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
- ARC Centre of Excellence for Integrative Brain Function, Clayton, Melbourne, Victoria, Australia
| | - Daniel Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health (NIMH/NIH), Bethesda, MD, USA
| | - Marcello G P Rosa
- ARC Centre of Excellence for Integrative Brain Function, Clayton, Melbourne, Victoria, Australia
- Neuroscience Program, Monash Biomedicine Discovery Institute, Clayton, Melbourne, Victoria, Australia
| | - David A Leopold
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, and National Eye Institute, National Institutes of Health, Bethesda, MD, USA
- Section on Cognitive Neurophysiology and Imaging, Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Afonso C Silva
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
- Department of Neurobiology, University of Pittsburgh Brain Institute, Pittsburgh, PA, USA.
| |
Collapse
|
34
|
Seiriki K. Development of a Whole-brain Imaging System at Subcellular Resolution for Analysis of Animal Models of Neuropsychiatric Disorders. YAKUGAKU ZASSHI 2019; 139:1501-1507. [DOI: 10.1248/yakushi.19-00169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Kaoru Seiriki
- Institute for Academic Initiatives, Osaka University
- Laboratory of Molecular Neuropharmacology, Graduate School of Pharmaceutical Sciences, Osaka University
| |
Collapse
|
35
|
Chon U, Vanselow DJ, Cheng KC, Kim Y. Enhanced and unified anatomical labeling for a common mouse brain atlas. Nat Commun 2019; 10:5067. [PMID: 31699990 PMCID: PMC6838086 DOI: 10.1038/s41467-019-13057-w] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 10/17/2019] [Indexed: 01/13/2023] Open
Abstract
Anatomical atlases in standard coordinates are necessary for the interpretation and integration of research findings in a common spatial context. However, the two most-used mouse brain atlases, the Franklin-Paxinos (FP) and the common coordinate framework (CCF) from the Allen Institute for Brain Science, have accumulated inconsistencies in anatomical delineations and nomenclature, creating confusion among neuroscientists. To overcome these issues, we adopt here the FP labels into the CCF to merge the labels in the single atlas framework. We use cell type-specific transgenic mice and an MRI atlas to adjust and further segment our labels. Moreover, detailed segmentations are added to the dorsal striatum using cortico-striatal connectivity data. Lastly, we digitize our anatomical labels based on the Allen ontology, create a web-interface for visualization, and provide tools for comprehensive comparisons between the CCF and FP labels. Our open-source labels signify a key step towards a unified mouse brain atlas.
Collapse
Affiliation(s)
- Uree Chon
- Department of Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, USA
| | - Daniel J Vanselow
- Department of Pathology, College of Medicine, Penn State University, Hershey, PA, USA
| | - Keith C Cheng
- Department of Pathology, College of Medicine, Penn State University, Hershey, PA, USA
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, USA.
| |
Collapse
|
36
|
Huo BX, Zeater N, Lin MK, Takahashi YS, Hanada M, Nagashima J, Lee BC, Hata J, Zaheer A, Grünert U, Miller MI, Rosa MGP, Okano H, Martin PR, Mitra PP. Relation of koniocellular layers of dorsal lateral geniculate to inferior pulvinar nuclei in common marmosets. Eur J Neurosci 2019; 50:4004-4017. [PMID: 31344282 DOI: 10.1111/ejn.14529] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 07/11/2019] [Accepted: 07/15/2019] [Indexed: 11/27/2022]
Abstract
Traditionally, the dorsal lateral geniculate nucleus (LGN) and the inferior pulvinar (IPul) nucleus are considered as anatomically and functionally distinct thalamic nuclei. However, in several primate species it has also been established that the koniocellular (K) layers of LGN and parts of the IPul have a shared pattern of immunoreactivity for the calcium-binding protein calbindin. These calbindin-rich cells constitute a thalamic matrix system which is implicated in thalamocortical synchronisation. Further, the K layers and IPul are both involved in visual processing and have similar connections with retina and superior colliculus. Here, we confirmed the continuity between calbindin-rich cells in LGN K layers and the central lateral division of IPul (IPulCL) in marmoset monkeys. By employing a high-throughput neuronal tracing method, we found that both the K layers and IPulCL form comparable patterns of connections with striate and extrastriate cortices; these connections are largely different to those of the parvocellular and magnocellular laminae of LGN. Retrograde tracer-labelled cells and anterograde tracer-labelled axon terminals merged seamlessly from IPulCL into LGN K layers. These results support continuity between LGN K layers and IPulCL, providing an anatomical basis for functional congruity of this region of the dorsal thalamic matrix and calling into question the traditional segregation between LGN and the inferior pulvinar nucleus.
Collapse
Affiliation(s)
- Bing-Xing Huo
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan.,Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Natalie Zeater
- Faculty of Medicine and Health, Save Sight Institute, The University of Sydney, Sydney, NSW, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Sydney University Node, Sydney, NSW, Australia.,Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Meng Kuan Lin
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan.,Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Yeonsook S Takahashi
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan.,Integra Life Science Japan, Minato-Ku, Akasaka, Japan
| | - Mitsutoshi Hanada
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan.,Systems Neuroscience Institute and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jaimi Nagashima
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan.,Systems Neuroscience Institute and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Brian C Lee
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Junichi Hata
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan
| | - Afsah Zaheer
- Faculty of Medicine and Health, Save Sight Institute, The University of Sydney, Sydney, NSW, Australia.,Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Ulrike Grünert
- Faculty of Medicine and Health, Save Sight Institute, The University of Sydney, Sydney, NSW, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Sydney University Node, Sydney, NSW, Australia.,Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Michael I Miller
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Marcello G P Rosa
- Department of Physiology and Biomedicine Research Institute, Monash University, Clayton, Vic., Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, Vic., Australia
| | - Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan.,Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Paul R Martin
- Faculty of Medicine and Health, Save Sight Institute, The University of Sydney, Sydney, NSW, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Sydney University Node, Sydney, NSW, Australia.,Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Partha P Mitra
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan.,Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| |
Collapse
|
37
|
Majka P, Rosa MGP, Bai S, Chan JM, Huo BX, Jermakow N, Lin MK, Takahashi YS, Wolkowicz IH, Worthy KH, Rajan R, Reser DH, Wójcik DK, Okano H, Mitra PP. Unidirectional monosynaptic connections from auditory areas to the primary visual cortex in the marmoset monkey. Brain Struct Funct 2018; 224:111-131. [PMID: 30288557 PMCID: PMC6373361 DOI: 10.1007/s00429-018-1764-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 09/27/2018] [Indexed: 11/26/2022]
Abstract
Until the late twentieth century, it was believed that different sensory modalities were processed by largely independent pathways in the primate cortex, with cross-modal integration only occurring in specialized polysensory areas. This model was challenged by the finding that the peripheral representation of the primary visual cortex (V1) receives monosynaptic connections from areas of the auditory cortex in the macaque. However, auditory projections to V1 have not been reported in other primates. We investigated the existence of direct interconnections between V1 and auditory areas in the marmoset, a New World monkey. Labelled neurons in auditory cortex were observed following 4 out of 10 retrograde tracer injections involving V1. These projections to V1 originated in the caudal subdivisions of auditory cortex (primary auditory cortex, caudal belt and parabelt areas), and targeted parts of V1 that represent parafoveal and peripheral vision. Injections near the representation of the vertical meridian of the visual field labelled few or no cells in auditory cortex. We also placed 8 retrograde tracer injections involving core, belt and parabelt auditory areas, none of which revealed direct projections from V1. These results confirm the existence of a direct, nonreciprocal projection from auditory areas to V1 in a different primate species, which has evolved separately from the macaque for over 30 million years. The essential similarity of these observations between marmoset and macaque indicate that early-stage audiovisual integration is a shared characteristic of primate sensory processing.
Collapse
Affiliation(s)
- Piotr Majka
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 02-093, Warsaw, Poland
- Monash University Node, Australian Research Council, Centre of Excellence for Integrative Brain Function, Clayton, VIC, 3800, Australia
| | - Marcello G P Rosa
- Monash University Node, Australian Research Council, Centre of Excellence for Integrative Brain Function, Clayton, VIC, 3800, Australia.
- Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC, 3800, Australia.
| | - Shi Bai
- Monash University Node, Australian Research Council, Centre of Excellence for Integrative Brain Function, Clayton, VIC, 3800, Australia
- Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC, 3800, Australia
| | - Jonathan M Chan
- Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC, 3800, Australia
| | - Bing-Xing Huo
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, 351-0106, Japan
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Natalia Jermakow
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 02-093, Warsaw, Poland
| | - Meng K Lin
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, 351-0106, Japan
| | - Yeonsook S Takahashi
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, 351-0106, Japan
| | - Ianina H Wolkowicz
- Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC, 3800, Australia
| | - Katrina H Worthy
- Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC, 3800, Australia
| | - Ramesh Rajan
- Monash University Node, Australian Research Council, Centre of Excellence for Integrative Brain Function, Clayton, VIC, 3800, Australia
- Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC, 3800, Australia
| | - David H Reser
- School of Rural Health, Monash University, Churchill, VIC, 3842, Australia
| | - Daniel K Wójcik
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 02-093, Warsaw, Poland
| | - Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, 351-0106, Japan
- Department of Physiology, Keio University School of Medicine, Tokyo, 160-8582, Japan
| | - Partha P Mitra
- Monash University Node, Australian Research Council, Centre of Excellence for Integrative Brain Function, Clayton, VIC, 3800, Australia.
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, 351-0106, Japan.
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA.
| |
Collapse
|