Peter J. Gianaros

This study examines the relationship between neural activation during reappraisal tasks and individual differences in emotion regulation, finding no shared overlap across three measures and highlighting methodological challenges in fMRI research.

This study uses finite impulse response modeling to reveal distinct temporal dynamics of amygdala subregions in response to negative affect, showing prolonged activation in centromedial regions.

This study finds that adolescents with autism have higher biological aging rates in brain structures compared to neurotypical controls, as measured by DunedinPACNI.

This study shows that a brain pattern predicting negative affect in fMRI correlates with real-world negative affect in midlife adults.

This study finds that community socioeconomic disadvantage is associated with increased white matter hyperintensity volume in mid-to-late life adults, independent of individual factors and biological risk markers.

The paper investigates the neural basis of negative affective traits using fMRI data from 458 adults, finding that whole-brain machine learning models better predict stress vulnerability than region- or network-level measures.

The paper predicts instantaneous heart period from whole brain fMRI data using machine learning, identifying cortical and subcortical brain networks linked to psychological stress as reliable predictors.

The paper identifies four neural systems related to cognitive reappraisal using Bayes factors and systems-identification in fMRI data from two studies.

The paper examines how D2 dopamine receptor expression and reward sensitivity affect learning in a complex decision-making task using human behavioral and neuroimaging data.

The paper compares task-related network profiles and activation patterns in two executive control tasks, finding greater differences in functional correlations than in evoked activity.