Proceedings of the 2016 International Conference on Biological Sciences and Technology

Application of Graph Theory Features for the Objective Diagnosis of Depressive Patients with or without Anxiety: an Rs-fMRI Study

Authors
Xiang-Yu Shen, Jing-Yu Zhu, Mao-Bin Wei, Jiao-Long Qin, Rui Yan, Qiu-Xiang Wei, Zhi-Jian Yao, Qing Lu
Corresponding Author
Xiang-Yu Shen
Available Online January 2016.
DOI
https://doi.org/10.2991/bst-16.2016.41How to use a DOI?
Keywords
Rs-fMRI, MDD, Anxiety, Graph theory, Machine learning.
Abstract
Purposes: To probe abnormality that may lead to anxiety in depressive patients. Procedures: This study investigated the graph theory features ahead of machine learning feature selection procedure. Classification methods were applied afterwards. Methods: Graph theory, statistical analysis and forward sequential feature selection were combined to find features. SVM classifier was also involved. Results: 1 global and 22 local features were found correlated with clinical anxiety factor. Conclusions: Anxiety is correlated with emotion and cognitive loop and other regions.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
The International Conference on Biological Sciences and Technology
Part of series
Advances in Biological Sciences Research
Publication Date
January 2016
ISBN
978-94-6252-161-2
ISSN
2468-5747
DOI
https://doi.org/10.2991/bst-16.2016.41How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xiang-Yu Shen
AU  - Jing-Yu Zhu
AU  - Mao-Bin Wei
AU  - Jiao-Long Qin
AU  - Rui Yan
AU  - Qiu-Xiang Wei
AU  - Zhi-Jian Yao
AU  - Qing Lu
PY  - 2016/01
DA  - 2016/01
TI  - Application of Graph Theory Features for the Objective Diagnosis of Depressive Patients with or without Anxiety: an Rs-fMRI Study
BT  - The International Conference on Biological Sciences and Technology
PB  - Atlantis Press
SN  - 2468-5747
UR  - https://doi.org/10.2991/bst-16.2016.41
DO  - https://doi.org/10.2991/bst-16.2016.41
ID  - Shen2016/01
ER  -