Andrew C. Papanicolaou

This paper uses MEG and dynamic connectivity analysis to identify altered brain network microstates in mTBI patients compared to controls.

The paper analyzes resting-state neuromagnetic signals in children with and without reading difficulties using symbolic mutual information to identify network abnormalities associated with reading ability.

This paper introduces a data-driven topological filtering method using orthogonal minimal spanning trees to analyze resting-state MEG connectivity, showing improved classification of brain injury and developmental groups.

This study examines changes in brain connectivity patterns in mild traumatic brain injury using resting-state MEG data, finding altered coupling modes and high classification accuracy between mTBI and control groups.