Paolo Belardinelli

This study shows that pre-stimulus aperiodic neural activity, not periodic alpha/beta power, better predicts cortical excitability measured via TMS-EEG in motor and prefrontal regions.

This paper demonstrates theta-phase-locked fronto-parietal cortico-cortical paired associative stimulation (ccPAS) in humans, showing distinct changes in EEG activity and network connectivity compared to controls.

This study investigates how aperiodic and periodic EEG features influence corticospinal excitability measured by motor evoked potentials in EEG-TMS experiments.

This study investigates how the phase of the ongoing micro-rhythm in motor cortices affects long-term changes in interhemispheric inhibition using cortico-cortical paired associative stimulation.

This study uses hyperbolic neural networks to analyze MEG brain networks from the Cam-CAN dataset, revealing age-related hierarchical changes in brain subnetworks.

This study develops a machine learning framework to predict motor excitability states using EEG features, achieving 72% mean accuracy in healthy participants.

This paper evaluates ROI extraction methods for MEG connectivity analysis and recommends strategies based on sensitivity and specificity trade-offs.

The paper examines aging effects on cortical thickness and myelin concentration using MRI data from a large cohort, finding complex regional patterns and MTR as a more reliable myelin proxy.

This study shows that artifacts in EEG signals are more informative than brain activity for classification, highlighting the need for careful feature design in BCI applications.

This study uses TMS-EEG to show that anti-glutamatergic drugs affect specific TEP components, indicating distinct roles for NMDA and AMPA receptors in cortical excitability.

The paper compares two EEG filtering methods for extracting the μ-rhythm and assesses their impact on corticospinal excitability measured via TMS.