FMRI data analysis with dynamic causal modeling and Bayesian networks

T. N. Mane, M. B. Nagori, S. A. Agrawal

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

Abstract

Brain, an amazing organ of human body comprises electrical signal that can be helpful for interaction between various brain regions. Functional Magnetic resonance Imaging (fMRI) is a specialized type of Magnetic Resonance Imaging scan. Though nature of fMRI data posses various challenges in the analysis. But, even after all challenges too, it is used as an effective method to diagnose various disease and the relationships between various brain regions. In this paper, we have proposed a model that will result a better fMRI data analysis. The effective interactions among the brain regions can be explored using dynamic causal modeling (DCM) that will help us to understand the functionality of brain up to some extent. Bayesian networks can be used for causal discovery purpose in support with markov blanket which can be evaluated with the help of evaluation metrics.

Original languageEnglish
Title of host publicationMaterials Science and Information Technology, MSIT2011
Pages5303-5307
Number of pages5
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2011 International Conference on Material Science and Information Technology, MSIT2011 - Singapore, Singapore
Duration: 16 Sep 201118 Sep 2011

Publication series

NameAdvanced Materials Research
Volume433-440
ISSN (Print)1022-6680

Conference

Conference2011 International Conference on Material Science and Information Technology, MSIT2011
Country/TerritorySingapore
CitySingapore
Period16/09/1118/09/11

Keywords

  • Dynamic causal modeling
  • Feature selection
  • FMRI
  • Functional integration
  • Markov blanket

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