33 citations · 42 across the 11 of their papers we have counts for
5 papers · 1 filter
Causality as a Minimum Energy Principle
Moo K. Chung, D. Vijay Anand, Anass B El-Yaagoubi +3
Classical causal models, such as Granger causality and structural equation modeling, are largely restricted to acyclic interactions and struggle to represent cyclic and higher-orde…
From Density to Void: Why Brain Networks Fail to Reveal Complex Higher-Order Structures
Moo K. Chung, Anass B. El-Yaagoubi, Anqi Qiu +1
In brain network analysis using resting-state fMRI, there is growing interest in modeling higher-order interactions beyond simple pairwise connectivity via persistent homology. Des…
Topological Analysis of Seizure-Induced Changes in Brain Hierarchy Through Effective Connectivity
Anass B. El-Yaagoubi, Moo K. Chung, Hernando Ombao
Traditional Topological Data Analysis (TDA) methods, such as Persistent Homology (PH), rely on distance measures (e.g., cross-correlation, partial correlation, coherence, and parti…
Spectral Topological Data Analysis of Brain Signals
Anass El-Yaagoubi, Shuhao Jiao, Moo K. Chung +1
Topological analyses of brain functional connectivity usually reduce each pair of channels to a single scalar dependence, typically the Pearson correlation, and so cannot resolve t…
Hodge-Decomposition of Brain Networks
D Vijay Anand, D. Vijay Anand, Anass B El-Yaagoubi +2
We analyze brain networks by decomposing them into three orthogonal components: gradient, curl, and harmonic flows, through the Hodge decomposition, a technique advantageous for ca…