6 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…
Geometry-Driven Flow Analysis of Brain Sulcal Pattern
Moo K. Chung, Luigi Maccotta, Aaron Struck
Cortical folding reflects coordinated neurodevelopmental processes and is increasingly recognized as a sensitive marker of neurological disease. However, most existing analyses rel…
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…
Poisson Flow Model of Cortical Folding Pattern
Moo K. Chung, Luigi Maccotta, Aaron Struck
Cortical folding reflects coordinated neurodevelopmental processes and provides a sensitive marker of neurological disease. In juvenile myoclonic epilepsy (JME), structural abnorma…
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…
Counterfactual Analysis of Brain Network Dynamics
Moo K. Chung, Luigi Maccotta, Aaron Struck
Causal inference in brain networks has traditionally relied on regression-based models such as Granger causality, structural equation modeling, and dynamic causal modeling. While e…