Statistical Connectomics

The data science of networks is a rapidly developing field with myriad applications. In neuroscience, the brain is commonly modeled as a connectome, a network of nodes connected by edges. While there have been thousands of papers on connectomics, the statistics of networks remains limited and poorly...

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Bibliographic Details
Published in:Annual review of statistics and its application 2021-03, Vol.8 (1), p.463-492
Main Authors: Chung, Jaewon, Bridgeford, Eric, Arroyo, Jesús, Pedigo, Benjamin D, Saad-Eldin, Ali, Gopalakrishnan, Vivek, Xiang, Liang, Priebe, Carey E, Vogelstein, Joshua T
Format: Article
Language:English
Subjects:
connectomics
graphs
networks
statistical models
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Summary:The data science of networks is a rapidly developing field with myriad applications. In neuroscience, the brain is commonly modeled as a connectome, a network of nodes connected by edges. While there have been thousands of papers on connectomics, the statistics of networks remains limited and poorly understood. Here, we provide an overview from the perspective of statistical network science of the kinds of models, assumptions, problems, and applications that are theoretically and empirically justified for analysis of connectome data. We hope this review spurs further development and application of statistically grounded methods in connectomics.
ISSN:2326-8298
2326-831X
DOI:10.1146/annurev-statistics-042720-023234