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Published in: Cognitive Neurodynamics 6/2023

03-12-2022 | Research Article

Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI

Authors: Huan Wang, Rongxin Zhu, Shui Tian, Junneng Shao, Zhongpeng Dai, Li Xue, Yurong Sun, Zhilu Chen, Zhijian Yao, Qing Lu

Published in: Cognitive Neurodynamics | Issue 6/2023

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Abstract

The diagnosis of bipolar disorders (BD) mainly depends on the clinical history and behavior observation, while only using clinical tools often limits the diagnosis accuracy. The study aimed to create a novel BD diagnosis framework using multilayer modularity in the dynamic minimum spanning tree (MST). We collected 45 un-medicated BD patients and 47 healthy controls (HC). The sliding window approach was utilized to construct dynamic MST via resting-state functional magnetic resonance imaging (fMRI) data. Firstly, we used three null models to explore the effectiveness of multilayer modularity in dynamic MST. Furthermore, the module allegiance exacted from dynamic MST was applied to train a classifier to discriminate BD patients. Finally, we explored the influence of the FC estimator and MST scale on the performance of the model. The findings indicated that multilayer modularity in the dynamic MST was not a random process in the human brain. And the model achieved an accuracy of 83.70% for identifying BD patients. In addition, we found the default mode network, subcortical network (SubC), and attention network played a key role in the classification. These findings suggested that the multilayer modularity in dynamic MST could highlight the difference between HC and BD patients, which opened up a new diagnostic tool for BD patients.

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Appendix
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Metadata
Title
Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI
Authors
Huan Wang
Rongxin Zhu
Shui Tian
Junneng Shao
Zhongpeng Dai
Li Xue
Yurong Sun
Zhilu Chen
Zhijian Yao
Qing Lu
Publication date
03-12-2022
Publisher
Springer Netherlands
Published in
Cognitive Neurodynamics / Issue 6/2023
Print ISSN: 1871-4080
Electronic ISSN: 1871-4099
DOI
https://doi.org/10.1007/s11571-022-09907-x

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