Silvia Daun

This paper presents a statistical framework to resolve conflicting conclusions in lesion network mapping by introducing permutation-based null models and analytical expressions for significance testing.

This study examines how brain network organization develops in typically developing children and those with Tourette Syndrome, finding altered network flexibility and stability in TS patients.

This study uses computational models to replicate static and dynamic functional connectivity patterns in the brain and identifies key neurobiological parameters influencing these patterns.

The paper uses intracranial EEG to show that earlier motor planning in premotor cortex predicts faster motor execution in primary motor cortex, correlating with shorter reaction times.

AutoGaitA provides a framework for analyzing motor programs across species and behaviors, showing age-related loss of propulsive strength and task-dependent adaptations in mice.

The paper analyzes resting-state EEG data using phase-based connectivity states and shows that motor task performance affects these states but not classical microstates.

The paper assesses whether longitudinal resting state EEG data can reliably distinguish neurophysiological changes from cognitive state confounding, finding that multi-frequency, multi-channel classifiers achieve high cross-day accuracy.

This study uses dynamic graph analysis of EEG signals to show age-related changes in brain network communication during motor tasks, including increased coupling and altered temporal organization in older subjects.

The paper investigates neuronal connectivity between central pattern generating networks in stick insect legs using experimental and modeling approaches to identify coupling structures and strengths.