Nicolas Farrugia

This study investigates how LSD alters brain dynamics using MEG and machine learning, finding increased peak frequencies, reduced oscillatory power, and changes in spectral complexity.

This study uses MEG and connectome harmonics to show LSD reduces structure-function coupling in low-frequency brain activity, linking this to ego dissolution and altered network dynamics.

This paper quantifies structure-function coupling during movie-watching and resting-state using EEG and diffusion imaging, revealing how brain anatomy shapes electrophysiological dynamics.

The paper aligns auditory artificial neural networks with individual brain fMRI data, improving brain encoding and downstream auditory tasks, especially with limited training data.

This study uses machine learning to model rhythmic abilities in individuals with and without musical training, revealing profiles that capture variability linked to music experience.

This study shows that deep graph neural networks using human connectomes improve cognitive decoding accuracy and robustness by integrating brain dynamics with high-order graph convolutions.

This paper uses deep graph convolutions to decode brain activity across cognitive domains, finding that integration boosts high-order cognition decoding while segregation suffices for motor tasks.

The paper examines static and dynamic functional connectivity between the cerebellum and brain networks, linking them to self-reported impulsivity measures in healthy individuals.

The paper introduces a dataset linking brain functional architecture to cognitive states and personality traits through multimodal MRI and phenotypic assessments.