Article ; Online: Network models to enhance the translational impact of cross-species studies.
2023 Volume 24, Issue 9, Page(s) 575–588
Abstract: Neuroscience studies are often carried out in animal models for the purpose of understanding specific aspects of the human condition. However, the translation of findings across species remains a substantial challenge. Network science approaches can ... ...
Abstract | Neuroscience studies are often carried out in animal models for the purpose of understanding specific aspects of the human condition. However, the translation of findings across species remains a substantial challenge. Network science approaches can enhance the translational impact of cross-species studies by providing a means of mapping small-scale cellular processes identified in animal model studies to larger-scale inter-regional circuits observed in humans. In this Review, we highlight the contributions of network science approaches to the development of cross-species translational research in neuroscience. We lay the foundation for our discussion by exploring the objectives of cross-species translational models. We then discuss how the development of new tools that enable the acquisition of whole-brain data in animal models with cellular resolution provides unprecedented opportunity for cross-species applications of network science approaches for understanding large-scale brain networks. We describe how these tools may support the translation of findings across species and imaging modalities and highlight future opportunities. Our overarching goal is to illustrate how the application of network science tools across human and animal model studies could deepen insight into the neurobiology that underlies phenomena observed with non-invasive neuroimaging methods and could simultaneously further our ability to translate findings across species. |
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MeSH term(s) | Animals ; Humans ; Brain ; Neuroimaging ; Translational Research, Biomedical/methods ; Neurosciences ; Neurobiology |
Language | English |
Publishing date | 2023-07-31 |
Publishing country | England |
Document type | Journal Article ; Review ; Research Support, N.I.H., Extramural |
ZDB-ID | 2034150-7 |
ISSN | 1471-0048 ; 1471-0048 ; 1471-003X |
ISSN (online) | 1471-0048 |
ISSN | 1471-0048 ; 1471-003X |
DOI | 10.1038/s41583-023-00720-x |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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