BrainTGL: A dynamic graph representation learning model for brain network analysis
Modeling the dynamics characteristics in functional brain networks (FBNs) is important for
understanding the functional mechanism of the human brain. However, the current works do …
understanding the functional mechanism of the human brain. However, the current works do …
Modeling the dynamic brain network representation for autism spectrum disorder diagnosis
The dynamic functional connectivity analysis provides valuable information for
understanding functional brain activity underlying different cognitive processes. Modeling …
understanding functional brain activity underlying different cognitive processes. Modeling …
Learning dynamic graph representation of brain connectome with spatio-temporal attention
Functional connectivity (FC) between regions of the brain can be assessed by the degree of
temporal correlation measured with functional neuroimaging modalities. Based on the fact …
temporal correlation measured with functional neuroimaging modalities. Based on the fact …
GAT-LI: a graph attention network based learning and interpreting method for functional brain network classification
Background Autism spectrum disorders (ASD) imply a spectrum of symptoms rather than a
single phenotype. ASD could affect brain connectivity at different degree based on the …
single phenotype. ASD could affect brain connectivity at different degree based on the …
Dynamic adaptive spatio-temporal graph convolution for fMRI modelling
The characterisation of the brain as a functional network in which the connections between
brain regions are represented by correlation values across time series has been very …
brain regions are represented by correlation values across time series has been very …
Joint embedding of structural and functional brain networks with graph neural networks for mental illness diagnosis
Multimodal brain networks characterize complex connectivities among different brain
regions from both structural and functional aspects and provide a new means for mental …
regions from both structural and functional aspects and provide a new means for mental …
DS-GCNs: connectome classification using dynamic spectral graph convolution networks with assistant task training
Functional connectivity (FC) matrices measure the regional interactions in the brain and
have been widely used in neurological brain disease classification. A brain network, also …
have been widely used in neurological brain disease classification. A brain network, also …
Deep reinforcement learning guided graph neural networks for brain network analysis
Modern neuroimaging techniques enable us to construct human brains as brain networks or
connectomes. Capturing brain networks' structural information and hierarchical patterns is …
connectomes. Capturing brain networks' structural information and hierarchical patterns is …
[HTML][HTML] Spatio-temporal directed acyclic graph learning with attention mechanisms on brain functional time series and connectivity
We develop a deep learning framework, spatio-temporal directed acyclic graph with
attention mechanisms (ST-DAG-Att), to predict cognition and disease using functional …
attention mechanisms (ST-DAG-Att), to predict cognition and disease using functional …
Deep representation learning for multimodal brain networks
Applying network science approaches to investigate the functions and anatomy of the
human brain is prevalent in modern medical imaging analysis. Due to the complex network …
human brain is prevalent in modern medical imaging analysis. Due to the complex network …