Alessandro Chiuso

This paper decomposes whole-brain effective connectivity to reveal multiscale hierarchical dynamics and their relation to temporal irreversibility and individual-specific signatures.

This paper introduces a scalable method for estimating effective connectivity in fMRI data, showing it matches advanced DCM results and outperforms functional connectivity for connectome fingerprinting.

This study uses a dynamic causal model to analyze resting-state fMRI data and identifies spatial patterns of hemodynamic sensitivity to healthy aging, showing region-specific vascular changes and their predictive power for age classification.

The paper examines the relationship between structural and effective connectivity in the mouse brain, finding that effective connectivity aligns with a unimodal-transmodal hierarchy while structural connectivity shows mismatches in higher-order networks.

This paper applies network control theory to identify optimal input nodes for controlling specific brain regions using effective connectivity models.

The paper uses a linear state-space model to analyze asymmetry in brain hierarchies from resting-state fMRI data, revealing that outgoing strengths in effective connectivity relate to information flow, while incoming strengths reflect conditional independence.