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Related Experiment Video

Updated: Jun 13, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

TAFNet: Trusted Multiview Associative Fusion Neural Networks for Analyzing Dynamic Brain Networks.

Weiping Ding, Wenhao Dai, Tao Hou

    IEEE Transactions on Neural Networks and Learning Systems
    |June 11, 2026
    PubMed
    Summary
    This summary is machine-generated.

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    This study introduces a novel framework for analyzing dynamic brain networks, improving diagnostic accuracy in schizophrenia by at least 2.15%. The method addresses data heterogeneity in dynamic functional connectivity analysis using trusted multiview associative fusion neural networks (TAFNet).

    Area of Science:

    • Neuroscience
    • Machine Learning
    • Biomedical Engineering

    Background:

    • Dynamic functional connectivity (DFC) is vital for understanding brain network temporal dynamics.
    • Existing DFC methods often overlook data heterogeneity in clinical settings, assuming uniform quality across time windows.
    • This limitation hinders accurate analysis of complex brain states.

    Purpose of the Study:

    • To propose a novel framework, TAFNet, for robust dynamic brain network analysis.
    • To address data heterogeneity and improve the reliability of DFC analysis in clinical environments.
    • To enhance diagnostic accuracy for neurological disorders using advanced machine learning.

    Main Methods:

    • Developed a trusted multiview associative fusion neural network (TAFNet) framework.

    Related Experiment Videos

    Last Updated: Jun 13, 2026

    Modeling the Functional Network for Spatial Navigation in the Human Brain
    05:55

    Modeling the Functional Network for Spatial Navigation in the Human Brain

    Published on: October 13, 2023

  • Employed a local-global convolutional filtering module for evidence extraction from independent temporal window views.
  • Integrated a multiview associative fusion mechanism using mutual information and a top-k view selection strategy.
  • Incorporated a dynamic trust assessment (DTA) module and Dempster combination rule for reliable fusion and confidence quantification.

    • Main Results:

    • TAFNet demonstrated superior performance compared to state-of-the-art methods on three schizophrenia datasets.
    • The framework achieved a diagnostic accuracy improvement of at least 2.15%.
    • The dynamic trust assessment module effectively quantified predictive confidence by aligning evidential beliefs with probabilistic predictions.

    Conclusions:

    • The proposed TAFNet framework offers a significant advancement in analyzing dynamic brain networks, particularly in heterogeneous clinical data.
    • TAFNet enhances diagnostic accuracy and provides reliable confidence estimations, crucial for clinical applications.
    • This approach paves the way for more accurate and trustworthy neuroimaging-based diagnostics.