Fabio Pasqualetti

This paper introduces a framework for applying network control theory to the human structural connectome to study neural dynamics and control energy, controllability, and their correlations with behavior and mental health.

This study uses network control theory to analyze energy dynamics in temporal lobe epilepsy, finding increased control energy requirements and metabolic asymmetry in affected brain regions.

The paper investigates how scanning factors affect brain network dynamics in multi-site fMRI data and identifies factors that influence variability in subject-specific dynamical patterns.

The paper uses network control theory to show that structural brain networks mature during adolescence to reduce the energy cost of activating the frontoparietal system, supporting executive function development.

The paper shows working memory involves brain-wide state switching, with dopamine D1/D2 receptor expression affecting stability and state transitions, and schizophrenia linked to altered network control properties.

This paper uses network control theory to model how white matter tracts guide electrical stimulation in the brain, showing that real white matter structures better predict state transitions than artificial models.