Sydney M. Bailes

This study shows high-frequency fMRI signals are linked to brain states and can predict spontaneous neural activity using machine learning.

This paper predicts EEG neural rhythms from fMRI data across sleep and wakefulness using machine learning, identifying large-scale network patterns associated with alpha and delta rhythms.

This study shows that aging reduces cerebrospinal fluid flow during sleep, linked to neural and vascular changes, and associated with gray matter atrophy.

The paper shows resting-state fMRI signals contain spectral signatures reflecting local hemodynamic response timing variations across brain regions.