33 citations · 42 across the 11 of their papers we have counts for
11 papers
Topological Effective Connectivity Modeling in Brain Networks
Anass El-Yaagoubi, Moo K. Chung, Hernando Ombao
Characterizing directed information flow in brain networks is difficult because neural circuits are full of recurrent feedback loops. Many existing tools for directed dependence as…
Vector Space of Cycles
Moo K. Chung, Anass B. El-Yaagoubi, Hernando Ombao
Most statistical and machine learning methods for directed interactions focus on pairwise effects among variables. Even existing cyclic models represent feedback primarily through…
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…
A Robust Topological Framework for Detecting Regime Changes in Multi-Trial Experiments with Application to Predictive Maintenance
Anass B. El-Yaagoubi, Jean-Marc Freyermuth, Hernando Ombao
We present a general and flexible framework for detecting regime changes in complex, non-stationary data across multi-trial experiments. Traditional change point detection methods…
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…
Topological Data Analysis for Directed Dependence Networks of Multivariate Time Series Data
Anass B. El-Yaagoubi, Hernando Ombao
Topological data analysis (TDA) approaches are becoming increasingly popular for studying the dependence patterns in multivariate time series data. In particular, various dependenc…
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…
Modeling and Simulating Dependence in Networks Using Topological Data Analysis
Anass El Yaagoubi Bourakna, Moo K. Chung, Hernando Ombao
Topological data analysis (TDA) approaches are becoming increasingly popular for studying the dependence patterns in multivariate time series data. In particular, various dependenc…
Topological Data Analysis for Multivariate Time Series Data
Anass El Yaagoubi Bourakna, Moo K. Chung, Hernando Ombao
Over the last two decades, topological data analysis (TDA) has emerged as a very powerful data analytic approach which can deal with various data modalities of varying complexities…