Skip to main navigation Skip to search Skip to main content

Statistical connectomics

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

Research output: Contribution to journal › Review article › peer-review

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.

Original languageEnglish (US)
Pages (from-to)463-492
Number of pages30
JournalAnnual Review of Statistics and Its Application
Volume8
DOIs
StatePublished - Mar 7 2021

Keywords

  • connectomics
  • graphs
  • networks
  • statistical models

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint

Dive into the research topics of 'Statistical connectomics'. Together they form a unique fingerprint.

Cite this