Alessandra Bertoldo

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 paper uses multivariate PLSC to analyze functional-metabolic coupling in the brain, finding strong links between fMRI-derived functional connectivity and glucose metabolism parameters.

This study links long-range temporal correlations in brain activity to glucose metabolism and biosynthetic processes, showing higher correlations correlate with greater metabolic costs.

The paper predicts [18F]FDG PET parameters using rs-fMRI and 15O PET data, showing rs-fMRI and CMRO2 explain 45% of Ki variance.

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.

This study uses dynamic 11C-PiB PET to show reduced cerebrospinal fluid clearance in Alzheimer’s disease and multiple sclerosis compared to healthy controls.

The paper identifies three archetypes of time preference for reward in humans, linking them to brain structure, functional connectivity, and socio-economic traits.