Null models in network neuroscience

TY - JOUR

T1 - Null models in network neuroscience

AU - Váša, František

AU - Mišić, Bratislav

N1 - Funding Information: The authors thank A. Goulas for stimulating discussions during the conceptualizing of this work, and E. Suárez, A. Luppi, V. Bazinet, G. Shafiei, J. Hansen, Z.-Q. Liu, O. Sherwood and R. Moran for constructive comments on the manuscript. F.V. acknowledges support from the Data to Early Diagnosis and Precision Medicine Industrial Strategy Challenge Fund, UK Research and Innovation (UKRI) and the Bill & Melinda Gates Foundation. B.M. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canadian Institutes of Health Research (CIHR), the Brain Canada Foundation Future Leaders Fund, the Canada Research Chairs Program and the Healthy Brains for Healthy Lives initiative. Publisher Copyright: © 2022, Springer Nature Limited.

PY - 2022/8

Y1 - 2022/8

N2 - Recent advances in imaging and tracing technology provide increasingly detailed reconstructions of brain connectomes. Concomitant analytic advances enable rigorous identification and quantification of functionally important features of brain network architecture. Null models are a flexible tool to statistically benchmark the presence or magnitude of features of interest, by selectively preserving specific architectural properties of brain networks while systematically randomizing others. Here we describe the logic, implementation and interpretation of null models of connectomes. We introduce randomization and generative approaches to constructing null networks, and outline a taxonomy of network methods for statistical inference. We highlight the spectrum of null models - from liberal models that control few network properties, to conservative models that recapitulate multiple properties of empirical networks - that allow us to operationalize and test detailed hypotheses about the structure and function of brain networks. We review emerging scenarios for the application of null models in network neuroscience, including for spatially embedded networks, annotated networks and correlation-derived networks. Finally, we consider the limits of null models, as well as outstanding questions for the field.

AB - Recent advances in imaging and tracing technology provide increasingly detailed reconstructions of brain connectomes. Concomitant analytic advances enable rigorous identification and quantification of functionally important features of brain network architecture. Null models are a flexible tool to statistically benchmark the presence or magnitude of features of interest, by selectively preserving specific architectural properties of brain networks while systematically randomizing others. Here we describe the logic, implementation and interpretation of null models of connectomes. We introduce randomization and generative approaches to constructing null networks, and outline a taxonomy of network methods for statistical inference. We highlight the spectrum of null models - from liberal models that control few network properties, to conservative models that recapitulate multiple properties of empirical networks - that allow us to operationalize and test detailed hypotheses about the structure and function of brain networks. We review emerging scenarios for the application of null models in network neuroscience, including for spatially embedded networks, annotated networks and correlation-derived networks. Finally, we consider the limits of null models, as well as outstanding questions for the field.

UR - http://www.scopus.com/inward/record.url?scp=85131089927&partnerID=8YFLogxK

U2 - 10.1038/s41583-022-00601-9

DO - 10.1038/s41583-022-00601-9

M3 - Review article

C2 - 35641793

SN - 1471-003X

VL - 23

SP - 493

EP - 504

JO - Nature Reviews Neuroscience

JF - Nature Reviews Neuroscience

IS - 8

ER -