Sparse Bayesian network for brain connectivity modeling of Alzheimer's disease

Shuai Huang, Jing Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent studies have shown that Alzheimer's disease (AD) is related to the alteration in the brain connectivity network. One type of connectivity, called effective connectivity, defined as the causal influence between distinct brain regions, is essential to the brain's functional process. However, little research has been done to model the effective connectivity of AD and characterize its difference from normal controls (NC). We propose sparse Bayesian network (SBN) for effective connectivity modeling. Specifically, we propose a novel formulation in the learning of SBN. Theoretical analysis and simulation studies are performed, both implying that the learning under the proposed formulation is more accurate and efficient than many existing algorithms. We apply the proposed method to the neuroimaging PET data of 42 AD and 67 NC subjects, and identify and compare the effective connectivity models for AD and NC. Our study reveals several connectivity patterns distinctly different between AD and NC, which are consistent with literature findings. New patterns are also discovered which may help the knowledge discovery of AD.

Original languageEnglish (US)
Title of host publication61st Annual IIE Conference and Expo Proceedings
PublisherInstitute of Industrial Engineers
StatePublished - 2011
Event61st Annual Conference and Expo of the Institute of Industrial Engineers - Reno, NV, United States
Duration: May 21 2011May 25 2011

Other

Other61st Annual Conference and Expo of the Institute of Industrial Engineers
CountryUnited States
CityReno, NV
Period5/21/115/25/11

Keywords

  • Alzheimer's disease
  • Bayesian network
  • High-dimensional data
  • L1-norm
  • Structural learning

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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