Fatemeh Hadaeghi

The paper presents a game-theoretic model showing human cortical networks prioritize computational reliability over communication efficiency and wiring cost.

This paper introduces Network Reciprocity Control algorithms to adjust asymmetry and reciprocity in networks while preserving density and weight, demonstrating their impact on network properties and scalability.

The paper shows that connectivity asymmetry in brain networks supports higher working-memory capacity, while increased reciprocity reduces it, using computational models of recurrent neural networks.

This study introduces a framework using game theory to characterize optimal communication in brain networks, finding that broadcasting models best describe communication efficiency, with rich-club regions playing a key role.

This study identifies differences in auditory-visual integration networks between children with ADHD and neurotypical controls using machine learning on fMRI data, showing potential diagnostic utility.

The paper demonstrates that neural activity patterns may not reliably indicate causal contributions of neural units to behavior and introduces a lesioning framework to better infer causal relationships.