6 papers
Robust probabilistic measurement of structural-functional module consistency in infant brain development
Lingbin Bian, Feihong Liu, Qian Wang +3
Brain network is commonly divided into modules for analyzing their functionally segregated roles for group-level analysis in neuroimaging studies. Here, we introduce stochastic mod…
Hierarchical Bayesian inference for community detection and connectivity of functional brain networks
Lingbin Bian, Nizhuan Wang, Leonardo Novelli +2
Most functional magnetic resonance imaging studies rely on estimates of hierarchically organized functional brain networks whose segregation and integration reflect the cognitive a…
Hypergraph Multi-Modal Learning for EEG-based Emotion Recognition in Conversation
Zijian Kang, Yueyang Li, Shengyu Gong +6
Emotional Recognition in Conversation (ERC) is valuable for diagnosing health conditions such as autism and depression, and for understanding the emotions of individuals who strugg…
Information Bottleneck-Guided Heterogeneous Graph Learning for Interpretable Neurodevelopmental Disorder Diagnosis
Yueyang Li, Lei Chen, Wenhao Dong +9
Developing interpretable models for neurodevelopmental disorders (NDDs) diagnosis presents significant challenges in effectively encoding, decoding, and integrating multimodal neur…
MM-GTUNets: Unified Multi-Modal Graph Deep Learning for Brain Disorders Prediction
Luhui Cai, Weiming Zeng, Hongyu Chen +7
Graph deep learning (GDL) has demonstrated impressive performance in predicting population-based brain disorders (BDs) through the integration of both imaging and non-imaging data.…
MHNet: Multi-view High-order Network for Diagnosing Neurodevelopmental Disorders Using Resting-state fMRI
Yueyang Li, Weiming Zeng, Wenhao Dong +6
Background: Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN)…