Danilo Benozzo

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

The paper uses linear state-space models to analyze resting-state fMRI data from mice, decomposing the Jacobian matrix to link time-lagged functional dynamics with spatial constraints.

The paper investigates how previous stimuli influence magnitude processing in the macaque prefrontal cortex during stimulus presentation, showing that history biases affect neural decoding of stimulus magnitude.

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.

The paper uses hidden Markov models to analyze neural activity in rhesus monkeys during a distance discrimination task, finding that slower prefrontal metastable dynamics predict error trials.