74 citations · 162 across the 36 of their papers we have counts for
3 papers · 1 filter
Sulcal Pattern Matching with the Wasserstein Distance
Zijian Chen, Soumya Das, Moo K. Chung
We present the unified computational framework for modeling the sulcal patterns of human brain obtained from the magnetic resonance images. The Wasserstein distance is used to alig…
Heterogeneous Graph Convolutional Neural Network via Hodge-Laplacian for Brain Functional Data
Jinghan Huang, Moo K. Chung, Anqi Qiu
This study proposes a novel heterogeneous graph convolutional neural network (HGCNN) to handle complex brain fMRI data at regional and across-region levels. We introduce a generic…
Unified Topological Inference for Brain Networks in Temporal Lobe Epilepsy Using the Wasserstein Distance
Moo K. Chung, Camille Garcia Ramos, Felipe Branco De Paiva +7
Persistent homology offers a powerful tool for extracting hidden topological signals from brain networks. It captures the evolution of topological structures across multiple scales…