Braingb: a benchmark for brain network analysis with graph neural networks
Mapping the connectome of the human brain using structural or functional connectivity has
become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph …
become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph …
Multicenter and multichannel pooling GCN for early AD diagnosis based on dual-modality fused brain network
For significant memory concern (SMC) and mild cognitive impairment (MCI), their
classification performance is limited by confounding features, diverse imaging protocols, and …
classification performance is limited by confounding features, diverse imaging protocols, and …
Graph convolution network with similarity awareness and adaptive calibration for disease-induced deterioration prediction
Graph convolution networks (GCN) have been successfully applied in disease prediction
tasks as they capture interactions (ie, edges and edge weights on the graph) between …
tasks as they capture interactions (ie, edges and edge weights on the graph) between …
Learning physical properties in complex visual scenes: An intelligent machine for perceiving blood flow dynamics from static CT angiography imaging
Humans perceive physical properties such as motion and elastic force by observing objects
in visual scenes. Recent research has proven that computers are capable of inferring …
in visual scenes. Recent research has proven that computers are capable of inferring …
Ensemble deep learning for automated visual classification using EEG signals
X Zheng, W Chen, Y You, Y Jiang, M Li, T Zhang - Pattern Recognition, 2020 - Elsevier
This paper proposes an automated visual classification framework in which a novel analysis
method (LSTMS-B) of EEG signals guides the selection of multiple networks that leads to the …
method (LSTMS-B) of EEG signals guides the selection of multiple networks that leads to the …
Prior-guided adversarial learning with hypergraph for predicting abnormal connections in Alzheimer's disease
Alzheimer's disease (AD) is characterized by alterations of the brain's structural and
functional connectivity during its progressive degenerative processes. Existing auxiliary …
functional connectivity during its progressive degenerative processes. Existing auxiliary …
Topological clustering of multilayer networks
Multilayer networks continue to gain significant attention in many areas of study, particularly
due to their high utility in modeling interdependent systems such as critical infrastructures …
due to their high utility in modeling interdependent systems such as critical infrastructures …
Alzheimer's disease prediction via brain structural-functional deep fusing network
Q Zuo, Y Shen, N Zhong, CLP Chen… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Fusing structural-functional images of the brain has shown great potential to analyze the
deterioration of Alzheimer's disease (AD). However, it is a big challenge to effectively fuse …
deterioration of Alzheimer's disease (AD). However, it is a big challenge to effectively fuse …
A toolbox for brain network construction and classification (BrainNetClass)
Brain functional network has been increasingly used in understanding brain functions and
diseases. While many network construction methods have been proposed, the progress in …
diseases. While many network construction methods have been proposed, the progress in …
Towards an accurate autism spectrum disorder diagnosis: multiple connectome views from fMRI data
J Yang, X Xu, M Sun, Y Ruan, C Sun, W Li… - Cerebral …, 2024 - academic.oup.com
Functional connectome has revealed remarkable potential in the diagnosis of neurological
disorders, eg autism spectrum disorder. However, existing studies have primarily focused on …
disorders, eg autism spectrum disorder. However, existing studies have primarily focused on …