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On the nature and use of models in network neuroscience

Abstract

Network theory provides an intuitively appealing framework for studying relationships among interconnected brain mechanisms and their relevance to behaviour. As the space of its applications grows, so does the diversity of meanings of the term network model. This diversity can cause confusion, complicate efforts to assess model validity and efficacy, and hamper interdisciplinary collaboration. In this Review, we examine the field of network neuroscience, focusing on organizing principles that can help overcome these challenges. First, we describe the fundamental goals in constructing network models. Second, we review the most common forms of network models, which can be described parsimoniously along the following three primary dimensions: from data representations to first-principles theory; from biophysical realism to functional phenomenology; and from elementary descriptions to coarse-grained approximations. Third, we draw on biology, philosophy and other disciplines to establish validation principles for these models. We close with a discussion of opportunities to bridge model types and point to exciting frontiers for future pursuits.

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Fig. 1: Schematic of network models used in neuroscience.
Fig. 2: Three dimensions of network model types.
Fig. 3: Illustrations of different categories of model assessment.
Fig. 4: Bridging model types in network neuroscience.

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Acknowledgements

The authors thank Blevmore Laboratories and A. E. Sizemore for efforts in graphic design. The authors also thank D. Lydon-Staley, A. E. Sizemore, E. J. Cornblath and D. Zhou for helpful comments on an earlier version of this manuscript. D.S.B. acknowledges support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the ISI Foundation, the Paul Allen Foundation, the US Army Research Laboratory (W911NF-10-2-0022), the US Army Research Office (Bassett-W911NF-14-1-0679, Grafton-W911NF-16-1-0474 and DCIST-W911NF-17-2-0181), the US Office of Naval Research, the US National Institute of Mental Health (2-R01-DC-009209-11, R01-MH112847, R01-MH107235 and R21-M MH-106799), the US National Institute of Child Health and Human Development (1R01HD086888-01), the US National Institute of Neurological Disorders and Stroke (R01-NS099348) and the US National Science Foundation (BCS-1441502, BCS-1430087, NSF PHY-1554488 and BCS-1631550). J.I.G. acknowledges support from the US National Science Foundation (NSF NCS-1533623), the US National Eye Institute (R01-EY015260) and the US National Institute of Mental Health (R01-MH115557). The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.

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Nature Reviews Neuroscience thanks M. Kramer, L. Uddin and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Glossary

Network

In its simplest form, a set of nodes (or vertices) that are linked in pairs by so-called edges.

Graph

A set of objects, some pairs of which are related.

Hypergraphs

Extensions of graphs in which a single edge can connect more than two nodes.

Simplicial complexes

Comprise a set of vertices V and a collection of subsets of V that is closed under taking subsets.

Structural connectivity

A measure of the physical link between two cells, ensembles or areas and is not equivalent to either functional connectivity or effective connectivity.

Functional connectivity

A statistical similarity in time series, which can be estimated using a plethora of similarity measures and is not equivalent to either effective connectivity or structural connectivity.

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Bassett, D.S., Zurn, P. & Gold, J.I. On the nature and use of models in network neuroscience. Nat Rev Neurosci 19, 566–578 (2018). https://doi.org/10.1038/s41583-018-0038-8

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