Catie E. Chang

This study examines how arousal states affect brain network switching rates and their relationship with cognitive task performance using fMRI, EEG, and eye tracking.

This study investigates how baseline CO2 levels modulate neurofluid dynamics in awake humans, showing that autonomic nervous system regulation affects flow through frequency changes rather than amplitude.

The paper predicts chronological age from whole-brain fMRI-physiology coupling patterns, showing brain regions involved in autonomic regulation are key predictors.

This study examines how spatiotemporal brain activity propagations change from childhood to early adulthood, finding that certain propagation patterns become more prevalent with age and correlate with cognitive performance.

The paper investigates brain regions involved in autonomic activity during emotion regulation and rest, finding distinct afferent and efferent systems and age-related differences in their coordination.

This study examines age-related changes in fMRI signatures of autonomic physiology, finding slower respiratory responses and faster cardiac responses with aging, with a notable turning point after age 60.

This study compares the orientation of white matter nerve fibers and vasculature in the human brain using diffusion MRI and susceptibility weighted imaging, finding that vasculature aligns with some but not all white matter tracts.

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 study examines how connectivity patterns of arousal regulation centers change with vigilance states using EEG and fMRI, showing distinct network correlations in low and high alertness.

This study examines age-related differences in how cardiac and respiratory fluctuations influence the BOLD signal and shows that HRV-BF training can modulate physiological-BOLD coupling in an age-dependent manner.

This paper develops methods to estimate cerebrovascular reactivity (CVR) amplitude and delay in breath-hold fMRI when PETCO2 data quality is low, using rescaled RVT and deep learning-predicted PETCO2.

This paper identifies novel neural markers of sleep depth using auditory arousal threshold instead of conventional sleep stages, revealing unique brain network correlations.

This paper develops a deep learning model to accurately segment the nucleus basalis of Meynert on 3T MRI, showing improved performance over standard atlases in both healthy subjects and TLE patients.

This study identifies widespread brain-body co-fluctuations across multiple physiological systems and shows they are linked to autonomic nervous system activity.

This paper identifies three spatiotemporal patterns that unify diverse resting-state functional MRI phenomena into a parsimonious framework of global brain organization.

The paper shows lifespan changes in global signal topography in the human brain, with distinct patterns in subcortical and cortical regions, suggesting a dual-layer model of the signal.

This paper develops a deep learning model to classify seizure onset zones using cortico-cortical evoked potentials from intracranial neurostimulation recordings.

The paper tests the affective dial hypothesis by comparing brain regions activated during emotion up- and down-regulation, finding they target distinct circuits rather than opposing effects in the same areas.

This study shows that intrinsic connectivity networks in fMRI are continuously present and overlapping, not just during discrete events, and that analyzing all time points provides more comprehensive insights into functional patterns and schizophrenia symptoms.

This study shows that EEG microstate parameters are associated with vigilance levels, with different effects on microstate class durations and transitions.

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.

This paper evaluates a template-based method for estimating vigilance from fMRI data in humans, showing it correlates with EEG and outperforms global signal estimation.

This study introduces a novel bottom-up approach to analyze task-driven brain activity using phase synchronization and independent component analysis, revealing dynamic network synchronization across cognitive tasks.