Spatio-Temporal Graph Hubness Propagation Model for Dynamic Brain Network Classification

Dynamic brain network has the advantage over static brain network in characterizing the variation pattern of functional brain connectivity, and it has attracted increasing attention in brain disease diagnosis. However, most of the existing dynamic brain networks analysis methods rely on extracting f...

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Veröffentlicht in:	IEEE transactions on medical imaging 2024-06, Vol.43 (6), p.2381-2394
Hauptverfasser:	Zhu, Qi, Li, Shengrong, Meng, Xiangshui, Xu, Qiang, Zhang, Zhiqiang, Shao, Wei, Zhang, Daoqiang
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial neural networks Attention Brain Brain - diagnostic imaging brain disease diagnosis Brain diseases Brain modeling Classification Datasets Diagnosis Diseases Dynamic brain networks Epilepsy Evolution Feature extraction Functional magnetic resonance imaging Humans Image Processing, Computer-Assisted - methods Information processing Magnetic Resonance Imaging - methods Medical diagnosis Multilayer perceptrons Multilayers Nerve Net - diagnostic imaging Nerve Net - physiology Network topologies Network topology Neural networks optimal transport spatio-temporal feature Topology topology evolution Windows (intervals)
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