DE-NNs: Brain Network Analysis Algorithm Based on Dynamic Evidence Neural Networks

Hou Tao; Ding Weiping; Huang Jiashuang; Ju Hengrong

doi:10.7544/issn1000-1239.202330883

Journal of Computer Research and Development > 2025 > Corrected proof > DOI: 10.7544/issn1000-1239.202330883 CSTR: 32373.14.issn1000-1239.202330883

Hou Tao, Ding Weiping, Huang Jiashuang, Ju Hengrong. DE-NNs: Brain Network Analysis Algorithm Based on Dynamic Evidence Neural Networks[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202330883

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DE-NNs: Brain Network Analysis Algorithm Based on Dynamic Evidence Neural Networks

Hou Tao^{1, 2,},
Ding Weiping^1, ,,
Huang Jiashuang^{1, 2},
Ju Hengrong¹

1.
School of Artificial Intelligence and Computer Science, Nantong University, Nantong, Jiangsu 226019
2.
MIIT Key Laboratory of Pattern Analysis and Machine Intelligence (Nanjing University of Aeronautics and Astronautics), Nanjing 211106

Funds: This work was supported by the National Natural Science Foundation of China (61976120, 62006128, 62102199), the Natural Science Foundation of Jiangsu Province (BK20231337), the Double-Creation Doctoral Program of Jiangsu Province, the Natural Science Key Foundation of Higher Education of Jiangsu Province (21KJA510004), the Fundamental Research Funds for the Central Universities (NJ2022028), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX23_1781).

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Author Bio:
Hou Tao: born in 2000. Master candidate. His main research interests include deep learning and brain network analysis

Ding Weiping: born in 1979. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include data mining, deep learning, machine learning, granular computing, evolutionary computing, and big data analytics

Huang Jiashuang: born in 1988. PhD, associate professor. His main research interests include brain network analysis and deep learning

Ju Hengrong: born in 1989. PhD, associate professor. His main research interests include granular computing, rough sets, machine learning, and knowledge discovery
Received Date: October 30, 2023
Revised Date: August 15, 2024
Accepted Date: September 02, 2024
Available Online: December 11, 2024

Graphical Abstract

Abstract

Abstract

Dynamic functional connections (dFCs) can be regarded as a process of dynamic changes in multiple time windows to explore the changes in functional connections of the brain in different time periods. It has been widely used in resting state functional magnetic resonance imaging (rs-fMRI) analysis, providing a new perspective and strategy for the diagnosis of brain diseases. However, the common dynamic brain network analysis methods can not effectively use the potential correlation and timing between dynamic data, and ignore the uncertainty factors caused by the inconsistent data quality of each window. Therefore, we propose a brain network analysis algorithm based on dynamic evidence neural networks (DE-NNs). This algorithm designs a multi-view evidence acquisition module of dynamic brain network, which treats each time window of dynamic brain network as a view. Three different convolution filters are used to extract the feature maps of each time window of the dynamic brain network, and the evidence of the dynamic level is fully obtained. A dynamic evidence fusion mechanism is designed to make full use of dynamic evidence. The dynamic trust function is constructed according to the time sequence of dFC data based on the evidence theory synthesis rules. The evidence generated by multiple windows is fused at the decision level of classification, the uncertainty information is fully considered, and the classification performance is significantly improved. Experiments are conducted on three schizophrenia datasets compared with existing advanced algorithms in order to verify the effectiveness of the proposed DE-NNs. The results show that the accuracy and F1 scores of DE-NNs on the three brain disease diagnosis tasks are significantly improved.
- evidence fusion,
- uncertainty,
- dynamic functional connections,
- brain network analysis,
- brain disease diagnosis

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References (62)

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