Statistical Connectomics

Jaewon Chung, Eric Bridgeford, Jesus Arroyo, Benjamin D. Pedigo, Ali Saad-Eldin, Vivek Gopalakrishnan, Liang Xiang, Carey E. Priebe and Joshua T. Vogelstein

No ek4n3, OSF Preprints from Center for Open Science

Abstract: 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.

Date: 2020-08-12
New Economics Papers: this item is included in nep-net
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:ek4n3

DOI: 10.31219/osf.io/ek4n3

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