214 citations · 328 across the 9 of their papers we have counts for
10 papers
BSNMani: Bayesian Scalar-on-network Regression with Manifold Learning
Yijun Li, Ki Sueng Choi, Boadie W. Dunlop +5
Brain connectivity analysis is crucial for understanding brain structure and neurological function, shedding light on the mechanisms of mental illness. To study the association bet…
BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations
Kaiqiao Han, Yi Yang, Zijie Huang +8
Brain network analysis is vital for understanding the neural interactions regarding brain structures and functions, and identifying potential biomarkers for clinical phenotypes. Ho…
Dynamic Brain Transformer with Multi-level Attention for Functional Brain Network Analysis
Xuan Kan, Antonio Aodong Chen Gu, Hejie Cui +2
Recent neuroimaging studies have highlighted the importance of network-centric brain analysis, particularly with functional magnetic resonance imaging. The emergence of Deep Neural…
R-Mixup: Riemannian Mixup for Biological Networks
Xuan Kan, Zimu Li, Hejie Cui +6
Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological ent…
Transformer-Based Hierarchical Clustering for Brain Network Analysis
Wei Dai, Hejie Cui, Xuan Kan +3
Brain networks, graphical models such as those constructed from MRI, have been widely used in pathological prediction and analysis of brain functions. Within the complex brain syst…
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks
Yue Yu, Xuan Kan, Hejie Cui +9
Functional magnetic resonance imaging (fMRI) has become one of the most common imaging modalities for brain function analysis. Recently, graph neural networks (GNN) have been adopt…
Brain Network Transformer
Xuan Kan, Wei Dai, Hejie Cui +3
Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders. Recently, Transform…
Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning
Yi Yang, Yanqiao Zhu, Hejie Cui +4
Brain networks characterize complex connectivities among brain regions as graph structures, which provide a powerful means to study brain connectomes. In recent years, graph neural…
FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation
Xuan Kan, Hejie Cui, Joshua Lukemire +2
Functional magnetic resonance imaging (fMRI) is one of the most common imaging modalities to investigate brain functions. Recent studies in neuroscience stress the great potential…
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
Hejie Cui, Wei Dai, Yanqiao Zhu +7
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 Neur…