Laura D. Lewis

This study shows that hemodynamic oscillations during sleep coordinate directional CSF flow in the subarachnoid space and ventricles, enhancing fluid transport.

This study examines how different slow breathing regimes affect cerebrospinal fluid oscillations during wakefulness, finding that infraslow sighing enhances CSF-BOLD coupling.

This study identifies and characterizes fifteen subnetworks within subcortical brain regions, revealing their functional organization and potential applications in understanding neuropsychiatric disorders.

This paper introduces a method to measure high-resolution cardiac-locked brainstem velocity using DENSE MRI at 7T, revealing a double-peak pattern of motion.

The paper investigates how visual responses change during sleep using EEG-fMRI, finding that visual thalamus responses remain intact while early visual cortex responses are suppressed and inverted.

This study uses MRI to show neural activity drives directional CSF flow in the subarachnoid space near activated brain regions in awake humans.

This study introduces SOPHI, a method to map ultra-slow CSF flow in the human brain, revealing coherent flow patterns linked to vascular anatomy and potential CSF efflux pathways.

The paper identifies a hypothalamic-brainstem activity sequence that drives arousal fluctuations and cortical hemodynamics during drowsiness using fMRI.

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 study identifies subcortical hubs connected to multiple cortical networks using rs-fMRI data, suggesting they are targets for restoring consciousness in disorders of consciousness.

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 paper introduces a phase-contrast MRI technique to quantify slow cerebrospinal fluid flow in the human brain, showing cardiac pulsation drives brain-wide flow more than respiration.

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

This study uses EEG-PET-MRI to show how hemodynamic and metabolic dynamics change during wakefulness and NREM sleep, revealing coupled temporal patterns and distinct network activity.

This paper introduces a method to distinguish BOLD and non-BOLD signals in fMRI by analyzing cross-cortical depth delay patterns, demonstrating its effectiveness with visual task data.

This paper develops a dynamic model and deep learning framework to estimate cerebrospinal fluid velocity from fMRI data, enabling physically interpretable flow measurements.

The paper shows that hemodynamic responses in the cortex are influenced by neuromodulators like norepinephrine, with distinct impulse response functions for different neuromodulatory signals.

This study investigates how eye closure affects visual processing using fMRI, finding distinct patterns in thalamocortical networks with preserved early processing but attenuated responses in extrastriate cortex.

This paper investigates how anatomical and vascular features of the human brain affect the temporal specificity of fMRI signals in the primary visual cortex.

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

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.

This study examines ketamine-induced brain dynamics in humans using intracranial recordings, identifying oscillatory changes linked to antidepressant and dissociative effects.

The paper investigates how physiological processes like respiration and heart rate can create apparent brain connectivity patterns in resting-state fMRI data, challenging interpretations of functional networks.