An explainable autoencoder with multi-paradigm fMRI fusion for identifying differences in dynamic functional connectivity during brain development
DOI10.1016/j.neunet.2022.12.007zbMath1528.92012OpenAlexW4312081739MaRDI QIDQ6053366
Julia M. Stephen, Yu Ping Wang, Vince D. Calhoun, Huiyu Zhou, Faming Xu, Tony W. Wilson, Chen Qiao
Publication date: 18 October 2023
Published in: Neural Networks (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.neunet.2022.12.007
feature fusionexplainabilitybrain developmentdynamic functional connectivityhypergraph regularizationmulti-paradigm learning
Artificial neural networks and deep learning (68T07) Biomedical imaging and signal processing (92C55) Neural networks for/in biological studies, artificial life and related topics (92B20)
Cites Work
- NP-hardness of Euclidean sum-of-squares clustering
- A distributed feature selection scheme with partial information sharing
- An affine scaling methodology for best basis selection
- Multiple clustering for identifying subject clusters and brain sub-networks using functional connectivity matrices without vectorization
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