Stephanie D. Williams

This study shows that sleep deprivation leads to attentional failures linked to increased cerebrospinal fluid flow and systemic physiological changes.

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.

EEG-LLAMAS improves real-time EEG-fMRI neurofeedback by reducing BCG artifacts with low latency, enabling closed-loop experiments previously difficult to perform.

This study uses fast fMRI to identify a temporal sequence of thalamic activity during transitions in behavioral arousal states, linking thalamic dynamics to wakefulness and sleep.

The paper shows sustained attention and spatial attention have distinct effects on long-term memory encoding through three experiments and EEG analysis.