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

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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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Acknowledgements

We express our sincere gratitude to the Department of Psychiatry and the Department of Radiology at the Affiliated Brain Hospital of Nanjing Medical University. We are grateful for the generous support of all participants and their families.

Funding

This work was supported by the National Natural Science Foundation of China (81871066); Jiangsu Provincial Key Research and Development Program (BE2018609 and BE2019675); Jiangsu Provincial Medical Innovation Team of the Project of Invigorating Health Care through Science, Technology and Education (CXTDC2016004); Key Project supported by Medical Science and Technology Development Foundation, Jiangsu Commission of Health (K2019011); Key Project supported by Medical Science and Technology Development Foundation, Nanjing Department of Health (YKK16146 and ZKX18034).

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Authors and Affiliations

  1. School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096, Jiangsu Province, China

    Huan Wang, Shui Tian, Junneng Shao, Zhongpeng Dai, Li Xue, Yurong Sun, Zhijian Yao & Qing Lu

  2. Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China

    Huan Wang, Shui Tian, Junneng Shao, Zhongpeng Dai, Li Xue, Yurong Sun & Qing Lu

  3. Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China

    Rongxin Zhu, Zhilu Chen & Zhijian Yao

  4. Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China

    Zhijian Yao

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  1. Huan Wang
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Wang, H., Zhu, R., Tian, S. et al. Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI. Cogn Neurodyn 17, 1609–1619 (2023). https://doi.org/10.1007/s11571-022-09907-x

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