Application of Graph Theory Features for the Objective Diagnosis of Depressive Patients with or without Anxiety: an Rs-fMRI Study
Xiang-Yu Shen, Jing-Yu Zhu, Mao-Bin Wei, Jiao-Long Qin, Rui Yan, Qiu-Xiang Wei, Zhi-Jian Yao, Qing Lu
Available Online January 2016.
- https://doi.org/10.2991/bst-16.2016.41How to use a DOI?
- Rs-fMRI, MDD, Anxiety, Graph theory, Machine learning.
- 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.
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 SP - 278 EP - 283 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 -