MD-LMS Algorithm Based Brain Functional Connectivity Analysis in Resting State fMRI

This paper proposes a new method to analyze resting state fMRI images for brain functional connectivity extraction. The proposed method regards the brain areas as nodes and the brain functional connectivity as a diffusion network, and then applies multitask diffusion LMS (MD-LMS) algorithm and hiera...

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Bibliographic Details
Published in2018 World Automation Congress (WAC) pp. 1 - 5
Main Authors Marui, Wataru, Kan, Shigenobu, Nii, Manabu, Shibata, Masahiko, Kobashi, Syoji
Format Conference Proceeding
LanguageEnglish
Japanese
Published TSI Press 01.06.2018
Subjects
Conferences
Functional connectivity
Functional magnetic resonance imaging
Hierarchical clustering
Labeling
MD-LMS
Presses
Resting state fMRI
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DOI10.23919/WAC.2018.8430471

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Summary:This paper proposes a new method to analyze resting state fMRI images for brain functional connectivity extraction. The proposed method regards the brain areas as nodes and the brain functional connectivity as a diffusion network, and then applies multitask diffusion LMS (MD-LMS) algorithm and hierarchical clustering to extract the valid networks. MD-LMS algorithm is a signal analysis method in a diffusion network. The proposed method was applied to three healthy subjects, and extracted multiple brain functional connectivity networks. The proposal method examined the brain hierarchical functional structure, and extracted temporal change of the brain functional connectivity. The novelty of the method is that the proposed method can extract transition of brain functional connectivity networks.
DOI:10.23919/WAC.2018.8430471