Alessandra Griffa

This study shows that balance learning improves cortical GABA levels and functional connectivity in elderly adults, potentially counteracting age-related declines.

The paper embeds brain networks into high-dimensional graph spaces using nodal invariants and shows improved classification accuracy in higher dimensions.

This study shows that MEG signatures for individual identification improve during task-related behavior compared to resting-state, with controlled tasks showing the most significant improvements.

The paper compares information transmission patterns in human, macaque, and mouse brains, finding that humans use parallel pathways while other mammals use single pathways.

This paper uses graph signal processing to decode brain tasks and fingerprint individuals by analyzing structure-function coupling in brain imaging data.

This paper evaluates MEG brain fingerprinting using resting-state data, finding that performance depends on connectivity measures, frequency bands, and spatial correction, with higher scores in phase-coupling methods and specific brain networks.

The paper introduces a method to generate group-representative structural brain networks that better preserve subject-level properties by adjusting for distance-dependent consistency thresholds.

The paper shows that the right auditory cortex is more integrated in the brain network, enabling more efficient communication, with this asymmetry explained by diffusive communication mechanisms.

The paper shows that cortical thinning in early Parkinson’s Disease correlates with connectivity to subcortical disease reservoirs, suggesting disease spread along neural networks.