Using Statistical Distribution to Identify the Influence Connections in Brain Networks

Haodi Mao, Xing Wu, Jianjia Wang*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

Functional connectivity is a widely used method to explore the connections among different regions in the brain. However, it is still a challenge to extract structures from multiple similar brain networks that can characterize the different groups of patients in functional connectivity. In this paper, we commence from the binomial distribution and then extend to double gamma distributions to binarize brain network. This produces an optimal threshold in the process of network construction. We introduce a community-based nodal finding algorithm to handle the influence of the rich effect on finding important nodes. The experiments present an effective classification result on different brain network structures.

Original languageEnglish
Title of host publicationProceedings - 2023 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages259-262
Number of pages4
ISBN (Electronic)9798350308693
DOIs
Publication statusPublished - 2023
Event15th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023 - Jiangsu, China
Duration: 2 Nov 20234 Nov 2023

Publication series

NameProceedings - 2023 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023

Conference

Conference15th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023
Country/TerritoryChina
CityJiangsu
Period2/11/234/11/23

Keywords

  • binomial distribution
  • community important nodes algorithm
  • Complex network
  • double gamma distribution

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