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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This work was supported by National Institutes of Health (NIH) grant F32AA030475-01A1 to J.K.B.
The authors declare no competing interests.
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Nature Reviews Neuroscience thanks D. Batalle, who co-reviewed with G. De Alteriis; A. Raj; and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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- Average path lengths
The average number of edges that connects each pair of nodes in a network.
- Control energy
The magnitude of input required to drive the brain from one activity state to another while accounting for its structural topology, time and the number of nodes into which input is given.
The sum of connections to a given node.
- Global efficiency
A measure of the efficiency of long-range communication in a network.
A measure of how readily a network can be partitioned into subgroups of nodes that are more strongly connected to one another than to the rest of the network.
- Multilayer network
A graph structure in which nodes are organized into multiple layers; intra-layer edges represent relations between nodes within a layer, and inter-layer edges represent relations between nodes in different layers.
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Brynildsen, J.K., Rajan, K., Henderson, M.X. et al. Network models to enhance the translational impact of cross-species studies. Nat. Rev. Neurosci. 24, 575–588 (2023). https://doi.org/10.1038/s41583-023-00720-x