5 Result(s)
-
Chapter and Conference Paper
Detection of Discriminative Neurological Circuits Using Hierarchical Graph Convolutional Networks in fMRI Sequences
Graph convolutional network (GCN) has shown its potential on modeling functional MRI connectivity and recognizing neurological disease tasks. However, conventional GCN layers generally inherit the original gra...
-
Chapter and Conference Paper
Spatial Similarity-Aware Learning and Fused Deep Polynomial Network for Detection of Obsessive-Compulsive Disorder
Hereditary mental illness (e.g., obsessive-compulsive disorder (OCD)) shall reduce the quality of daily life of patients. To detect OCD objectively, sparse learning is an effective method for constructing a brain...
-
Chapter and Conference Paper
OCD Diagnosis via Smoothing Sparse Network and Stacked Sparse Auto-Encoder Learning
Obsessive-compulsive disorder (OCD) is a serious mental illness that affects the overall quality of patients’ daily life. Since sparse learning can remove redundant information in resting-state functional magn...
-
Chapter and Conference Paper
Constructing Multi-scale Connectome Atlas by Learning Graph Laplacian of Common Network
Recent development of neuroimaging and network science allow us to visualize and characterize the whole brain connectivity map in vivo. As the importance of volumetric image atlas, a common brain connectivity ma...
-
Chapter and Conference Paper
Identifying High Order Brain Connectome Biomarkers via Learning on Hypergraph
The functional connectome has gained increased attention in the neuroscience community. In general, most network connectivity models are based on correlations between discrete-time series signals that only con...
