Jason S. Nomi

The paper shows that alpha-range EEG oscillations are linked to dynamic BOLD brain networks, with low-alpha periods associated with unique configurations and both low- and high-alpha periods linked to overlapping networks.

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

The paper identifies a transdiagnostic functional connectivity pattern in early psychosis linked to cognitive inflexibility and arousal dysregulation across different diagnoses.

The paper uses fMRI to compare brain state dynamics between active and lecture-based physics learning, finding distinct neural patterns associated with each approach.

This study uses brain entropy measured via fMRI to explore the neural mechanisms of rTMS in tobacco use disorder, finding reduced entropy in key brain regions after dlPFC stimulation.

This study investigates the variability of fMRI BOLD signals in the human brain, showing it reflects multi-scale brain organization rather than noise.

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.

The paper identifies latent dimensions in task-fMRI data that explain more variance than conventional cognitive categories, suggesting current cognitive frameworks may not fully capture brain activation patterns.

The paper uses inter-subject phase synchronization to analyze fMRI data and identifies distinct neural networks associated with physical pain empathy, but not affective pain.

The paper examines how individual differences in resisting temptation relate to salience network dynamics using fMRI and gaze data.

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.

The paper identifies a nested structure in brain activation patterns using bi-factor analysis, revealing a general factor and sub-factors that explain variance in task-driven cognitive processes.

This study examines age-related changes in spatial and temporal features of resting state fMRI data using IVA, revealing stable network configurations but age-related shifts in local organization and frequency spectrum.