[1]DAI Wenhao,DING Weiping,YIN Tao,et al.Brain network analysis algorithm based on trusted multiview association fusion[J].CAAI Transactions on Intelligent Systems,2026,21(2):553-564.[doi:10.11992/tis.202507026]
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Brain network analysis algorithm based on trusted multiview association fusion

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 2 Page number: 553-564 Column: 学术论文—人工智能基础 Public date: 2026-03-05
Title:
Brain network analysis algorithm based on trusted multiview association fusion
Author(s):
DAI Wenhao1;  DING Weiping1;  YIN Tao2;  HOU Tao1;  CHEN Yuepeng2
1. School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China;
2. School of Information Science and Technology, Nantong University, Nantong 226019, China
Keywords:
multiview fusion; evidence theory; mutual information; uncertainty; dynamic functional connectivity; brain network analysis; brain disease diagnosis; multiview learning
CLC:
TP18
DOI:
10.11992/tis.202507026
Abstract:
Dynamic functional connectivity combined with multiview learning usually assumes that the quality of multiview data is highly consistent, ignoring the heterogeneity and untrustworthiness of multiview data in the real world. To address the above problems, a brain network analysis algorithm based on trusted multiview association fusion is proposed. First, a local-global convolutional filter is utilized to extract multiview brain network evidence; second, a multiview association fusion network is constructed to measure the degree of information sharing through cross-view correlation, and then a selective strategy is used to retain the views containing the most shared information and dynamically reject low-quality data. To smooth the strength of evidence, a dynamic trust assessment mechanism is designed to quantify trustworthiness and neutralize it with evidence weighting by jointly modeling evidence uncertainty and category probability. Multiple sets of experiments are conducted on two real schizophrenia datasets, and the results validate the effectiveness and sophistication of the algorithms, which significantly improve the classification performance of the brain disease diagnosis task.
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