Qolamreza Ray Razlighi

The paper characterizes the hemodynamic response function of negative BOLD signals in default mode network subregions, finding unique HRFs across regions and tasks.

This study uses machine learning to show that integrating brain connectome data with amyloid-beta PET imaging improves prediction of cognitive decline in older adults.

This study uses Network Control Theory to analyze differences in brain energy landscapes between MCI patients and healthy controls, finding lower transition energy in the limbic network and reduced global entropy in MCI.

The paper investigates the interaction between spontaneous and task-evoked brain activities using fMRI, finding no significant interaction in two distinct brain networks.

This paper introduces DRIFT, a method to remove motion artifacts in fMRI by correcting k-space displacement, and finds that hybrid prospective-retrospective approaches are needed for optimal correction.

This study examines age-related differences in cognitive abilities using fMRI and diffusion imaging, finding that vocabulary-related brain regions are better maintained with age than fluid abilities.