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Ross G, Radtke-Schuller S, Frohlich F. Ferret as a model system for studying the anatomy and function of the prefrontal cortex: A systematic review. Neurosci Biobehav Rev 2024; 162:105701. [PMID: 38718987 PMCID: PMC11162921 DOI: 10.1016/j.neubiorev.2024.105701] [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: 10/30/2023] [Revised: 04/12/2024] [Accepted: 05/01/2024] [Indexed: 05/19/2024]
Abstract
There is a lack of consensus on anatomical nomenclature, standards of documentation, and functional equivalence of the frontal cortex between species. There remains a major gap between human prefrontal function and interpretation of findings in the mouse brain that appears to lack several key prefrontal areas involved in cognition and psychiatric illnesses. The ferret is an emerging model organism that has gained traction as an intermediate model species for the study of top-down cognitive control and other higher-order brain functions. However, this research has yet to benefit from synthesis. Here, we provide a summary of all published research pertaining to the frontal and/or prefrontal cortex of the ferret across research scales. The targeted location within the ferret brain is summarized visually for each experiment, and the anatomical terminology used at time of publishing is compared to what would be the appropriate term to use presently. By doing so, we hope to improve clarity in the interpretation of both previous and future publications on the comparative study of frontal cortex.
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Affiliation(s)
- Grace Ross
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Neuroscience Center, University of North Carolina, Chapel Hill, NC, USA
| | - Susanne Radtke-Schuller
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Flavio Frohlich
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Neuroscience Center, University of North Carolina, Chapel Hill, NC, USA; Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC, USA; Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC, USA; Department of Neurology, University of North Carolina, Chapel Hill, NC, USA.
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2
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Pramod RK, Atul PK, Pandey M, Anbazhagan S, Mhaske ST, Barathidasan R. Care, management, and use of ferrets in biomedical research. Lab Anim Res 2024; 40:10. [PMID: 38532510 DOI: 10.1186/s42826-024-00197-4] [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: 11/27/2023] [Revised: 03/02/2024] [Accepted: 03/14/2024] [Indexed: 03/28/2024] Open
Abstract
The ferret (Mustela putorius furo) is a small domesticated species of the family Mustelidae within the order Carnivora. The present article reviews and discusses the current state of knowledge about housing, care, breeding, and biomedical uses of ferrets. The management and breeding procedures of ferrets resemble those used for other carnivores. Understanding its behavior helps in the use of environmental enrichment and social housing, which promote behaviors typical of the species. Ferrets have been used in research since the beginning of the twentieth century. It is a suitable non-rodent model in biomedical research because of its hardy nature, social behavior, diet and other habits, small size, and thus the requirement of a relatively low amount of test compounds and early sexual maturity compared with dogs and non-human primates. Ferrets and humans have numerous similar anatomical, metabolic, and physiological characteristics, including the endocrine, respiratory, auditory, gastrointestinal, and immunological systems. It is one of the emerging animal models used in studies such as influenza and other infectious respiratory diseases, cystic fibrosis, lung cancer, cardiac research, gastrointestinal disorders, neuroscience, and toxicological studies. Ferrets are vulnerable to many human pathogenic organisms, like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), because air transmission of this virus between them has been observed in the laboratory. Ferrets draw the attention of the medical community compared to rodents because they occupy a distinct niche in biomedical studies, although they possess a small representation in laboratory research.
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Affiliation(s)
- Ravindran Kumar Pramod
- ICMR-National Animal Resource Facility for Biomedical Research, Genome Valley, Hyderabad, Telangana, 500101, India.
| | - Pravin Kumar Atul
- ICMR-National Animal Resource Facility for Biomedical Research, Genome Valley, Hyderabad, Telangana, 500101, India
| | - Mamta Pandey
- ICMR-National Animal Resource Facility for Biomedical Research, Genome Valley, Hyderabad, Telangana, 500101, India
| | - S Anbazhagan
- ICMR-National Animal Resource Facility for Biomedical Research, Genome Valley, Hyderabad, Telangana, 500101, India
| | - Suhas T Mhaske
- ICMR-National Animal Resource Facility for Biomedical Research, Genome Valley, Hyderabad, Telangana, 500101, India
| | - R Barathidasan
- ICMR-National Animal Resource Facility for Biomedical Research, Genome Valley, Hyderabad, Telangana, 500101, India
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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.
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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
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4
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The brainstem connectome database. Sci Data 2022; 9:168. [PMID: 35414055 PMCID: PMC9005652 DOI: 10.1038/s41597-022-01219-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 02/25/2022] [Indexed: 11/29/2022] Open
Abstract
Connectivity data of the nervous system and subdivisions, such as the brainstem, cerebral cortex and subcortical nuclei, are necessary to understand connectional structures, predict effects of connectional disorders and simulate network dynamics. For that purpose, a database was built and analyzed which comprises all known directed and weighted connections within the rat brainstem. A longterm metastudy of original research publications describing tract tracing results form the foundation of the brainstem connectome (BC) database which can be analyzed directly in the framework neuroVIISAS. The BC database can be accessed directly by connectivity tables, a web-based tool and the framework. Analysis of global and local network properties, a motif analysis, and a community analysis of the brainstem connectome provides insight into its network organization. For example, we found that BC is a scale-free network with a small-world connectivity. The Louvain modularity and weighted stochastic block matching resulted in partially matching of functions and connectivity. BC modeling was performed to demonstrate signal propagation through the somatosensory pathway which is affected in Multiple sclerosis. Measurement(s) | brainstem | Technology Type(s) | tract tracing metastudy | Factor Type(s) | brain region | Sample Characteristic - Organism | Rattus rattus | Sample Characteristic - Environment | Experimental setup | Sample Characteristic - Location | Germany |
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Schmitt O, Eipert P, Schwanke S, Lessmann F, Meinhardt J, Beier J, Kadir K, Karnitzki A, Sellner L, Klünker AC, Ruß F, Jenssen J. Connectome verification: inter-rater and connection reliability of tract-tracing-based intrinsic hypothalamic connectivity. Brief Bioinform 2019; 20:1944-1955. [PMID: 29897426 DOI: 10.1093/bib/bby048] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 05/09/2018] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Structural connectomics supports understanding aspects of neuronal dynamics and brain functions. Conducting metastudies of tract-tracing publications is one option to generate connectome databases by collating neuronal connectivity data. Meanwhile, it is a common practice that the neuronal connections and their attributes of such retrospective data collations are extracted from tract-tracing publications manually by experts. As the description of tract-tracing results is often not clear-cut and the documentation of interregional connections is not standardized, the extraction of connectivity data from tract-tracing publications could be complex. This might entail that different experts interpret such non-standardized descriptions of neuronal connections from the same publication in variable ways. Hitherto, no investigation is available that determines the variability of extracted connectivity information from original tract-tracing publications. A relatively large variability of connectivity information could produce significant misconstructions of adjacency matrices with faults in network and graph analyzes. The objective of this study is to investigate the inter-rater and inter-observation variability of tract-tracing-based documentations of neuronal connections. To demonstrate the variability of neuronal connections, data of 16 publications which describe neuronal connections of subregions of the hypothalamus have been assessed by way of example. RESULTS A workflow is proposed that allows detecting variability of connectivity at different steps of data processing in connectome metastudies. Variability between three blinded experts was found by comparing the connection information in a sample of 16 publications that describe tract-tracing-based neuronal connections in the hypothalamus. Furthermore, observation scores, matrix visualizations of discrepant connections and weight variations in adjacency matrices are analyzed. AVAILABILITY The resulting data and software are available at http://neuroviisas.med.uni-rostock.de/neuroviisas.shtml.
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Affiliation(s)
- Oliver Schmitt
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Peter Eipert
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Sebastian Schwanke
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Felix Lessmann
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Jennifer Meinhardt
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Julia Beier
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Kanar Kadir
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Adrian Karnitzki
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Linda Sellner
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Ann-Christin Klünker
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Frauke Ruß
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Jörg Jenssen
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
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Schwanke S, Jenssen J, Eipert P, Schmitt O. Towards Differential Connectomics with NeuroVIISAS. Neuroinformatics 2019; 17:163-179. [PMID: 30014279 DOI: 10.1007/s12021-018-9389-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The comparison of connectomes is an essential step to identify changes in structural and functional neuronal networks. However, the connectomes themselves as well as the comparisons of connectomes could be manifold. In most applications, comparisons of connectomes are applied to specific sets of data. In many studies collections of scripts are applied optimized for certain species (non-generic approaches) or diseases (control versus disease group connectomes). These collections of scripts have a limited functionality which do not support functional and topographic mappings of connectomes (hemispherical asymmetries, peripheral nervous system). The platform-independent and generic neuroVIISAS framework is built to circumvent limitations that come with variants of nomenclatures, connectivity lists and connectional hierarchies as well as restrictions to structural connectome analyses. A new analytical module is introduced into the framework to compare different types of connectomes and different representations of the same connectome within a unique software environment. As an example a differential analysis of the partial connectome of the laboratory rat that is based on virus tract tracing with the same regions of non-virus tract tracing has been performed. A relatively large connectional coherence between the two different techniques was found. However, some detected connections are described by virus tract-tracing only.
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Affiliation(s)
- Sebastian Schwanke
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057, Rostock, Germany
| | - Jörg Jenssen
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057, Rostock, Germany
| | - Peter Eipert
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057, Rostock, Germany
| | - Oliver Schmitt
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057, Rostock, Germany.
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Dell L, Innocenti GM, Hilgetag CC, Manger PR. Cortical and thalamic connectivity of occipital visual cortical areas 17, 18, 19, and 21 of the domestic ferret (
Mustela putorius furo
). J Comp Neurol 2019; 527:1293-1314. [DOI: 10.1002/cne.24631] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 12/19/2018] [Accepted: 01/02/2019] [Indexed: 12/21/2022]
Affiliation(s)
- Leigh‐Anne Dell
- Institute of Computational Neuroscience, University Medical Center Hamburg‐Eppendorf Hamburg Germany
| | - Giorgio M. Innocenti
- Department of NeuroscienceKarolinska Institute Stockholm Sweden
- Brain and Mind InstituteÉcole Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Claus C. Hilgetag
- Institute of Computational Neuroscience, University Medical Center Hamburg‐Eppendorf Hamburg Germany
- Department of Health SciencesBoston University Boston Massachusetts
| | - Paul R. Manger
- School of Anatomical Sciences, Faculty of Health Sciences, University of the Witwatersrand Johannesburg South Africa
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8
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Dell L, Innocenti GM, Hilgetag CC, Manger PR. Cortical and thalamic connectivity of posterior parietal visual cortical areas PPc and PPr of the domestic ferret (
Mustela putorius furo
). J Comp Neurol 2019; 527:1315-1332. [DOI: 10.1002/cne.24630] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 12/19/2018] [Accepted: 01/02/2019] [Indexed: 11/06/2022]
Affiliation(s)
- Leigh‐Anne Dell
- Institute of Computational NeuroscienceUniversity Medical Center Hamburg‐Eppendorf Hamburg Germany
| | - Giorgio M. Innocenti
- Department of NeuroscienceKarolinska Institute Stockholm Sweden
- Brain and Mind InstituteÉcole Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Claus C. Hilgetag
- Institute of Computational NeuroscienceUniversity Medical Center Hamburg‐Eppendorf Hamburg Germany
- Department of Health SciencesBoston University Boston Massachusetts
| | - Paul R. Manger
- School of Anatomical Sciences, Faculty of Health SciencesUniversity of the Witwatersrand Johannesburg South Africa
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9
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Dell L, Innocenti GM, Hilgetag CC, Manger PR. Cortical and thalamic connectivity of temporal visual cortical areas 20a and 20b of the domestic ferret (
Mustela putorius furo
). J Comp Neurol 2019; 527:1333-1347. [DOI: 10.1002/cne.24632] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 12/19/2018] [Accepted: 01/02/2019] [Indexed: 11/07/2022]
Affiliation(s)
- Leigh‐Anne Dell
- Institute of Computational Neuroscience, University Medical Center Hamburg‐Eppendorf Hamburg Germany
| | - Giorgio M. Innocenti
- Department of NeuroscienceKarolinska Institute Stockholm Sweden
- Brain and Mind Institute, École Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Claus C. Hilgetag
- Institute of Computational Neuroscience, University Medical Center Hamburg‐Eppendorf Hamburg Germany
- Department of Health SciencesBoston University Boston Massachusetts
| | - Paul R. Manger
- School of Anatomical Sciences, Faculty of Health SciencesUniversity of the Witwatersrand Johannesburg South Africa
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10
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Griffa A, Van den Heuvel MP. Rich-club neurocircuitry: function, evolution, and vulnerability. DIALOGUES IN CLINICAL NEUROSCIENCE 2018. [PMID: 30250389 PMCID: PMC6136122 DOI: 10.31887/dcns.2018.20.2/agriffa] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Over the past decades, network neuroscience has played a fundamental role in the understanding of large-scale brain connectivity architecture. Brains, and more generally nervous systems, can be modeled as sets of elements (neurons, assemblies, or cortical chunks) that dynamically interact through a highly structured and adaptive neurocircuitry. An interesting property of neural networks is that elements rich in connections are central to the network organization and tend to interconnect strongly with each other, forming so-called rich clubs. The ubiquity of rich-club organization across different species and scales of investigation suggests that this topology could be a distinctive feature of biological systems with information processing capabilities. This review surveys recent neuroimaging, computational, and cross-species comparative literature to offer an insight into the function and origin of rich-club architecture in nervous systems, discussing its relevance to human cognition and behavior, and vulnerability to brain disorders.
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Affiliation(s)
- Alessandra Griffa
- Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, The Netherlands
| | - Martijn P Van den Heuvel
- Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, The Netherlands; Department of Clinical Genetics, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
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Abstract
Since the initial report in 1911, the domestic ferret has become an invaluable biomedical research model. While widely recognized for its utility in influenza virus research, ferrets are used for a variety of infectious and noninfectious disease models due to the anatomical, metabolic, and physiological features they share with humans and their susceptibility to many human pathogens. However, there are limitations to the model that must be overcome for maximal utility for the scientific community. Here, we describe important recent advances that will accelerate biomedical research with this animal model.
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Griffa A. Rich-club neurocircuitry: function, evolution, and vulnerability. DIALOGUES IN CLINICAL NEUROSCIENCE 2018; 20:121-132. [PMID: 30250389 PMCID: PMC6136122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Over the past decades, network neuroscience has played a fundamental role in the understanding of large-scale brain connectivity architecture. Brains, and more generally nervous systems, can be modeled as sets of elements (neurons, assemblies, or cortical chunks) that dynamically interact through a highly structured and adaptive neurocircuitry. An interesting property of neural networks is that elements rich in connections are central to the network organization and tend to interconnect strongly with each other, forming so-called rich clubs. The ubiquity of rich-club organization across different species and scales of investigation suggests that this topology could be a distinctive feature of biological systems with information processing capabilities. This review surveys recent neuroimaging, computational, and cross-species comparative literature to offer an insight into the function and origin of rich-club architecture in nervous systems, discussing its relevance to human cognition and behavior, and vulnerability to brain disorders.
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Affiliation(s)
- Alessandra Griffa
- Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, The Netherlands
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