74 citations · 162 across the 36 of their papers we have counts for
9 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…
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…
Hodge-Decomposition of Functional Brain Networks
D. Vijay Anand, Anass B El-Yaagoubi, Hernando Ombao +1
We propose to analyze dynamically changing brain networks by decomposing them into three orthogonal components through the Hodge decomposition. We propose to quantify the magnitude…
Statistical Analysis on Brain Surfaces
Moo K. Chung, Jamie L. Hanson, Seth D. Pollak
In this paper, we review widely used statistical analysis frameworks for data defined along cortical and subcortical surfaces that have been developed in last two decades. The cere…
Hodge-Laplacian of Brain Networks
D. Vijay Anand, Moo K. Chung
The closed loops or cycles in a brain network embeds higher order signal transmission paths, which provide fundamental insights into the functioning of the brain. In this work, we…
Graph Theory in Brain Networks
Moo K. Chung
Recent developments in graph theoretic analysis of complex networks have led to deeper understanding of brain networks. Many complex networks show similar macroscopic behaviors des…