Abigail S. Greene

The paper examines how environmental exposures affect whole-brain functional connectivity and cognition in youth using machine learning and the ABCD Study data.

This paper introduces a feedback approach to connectome-based predictive modeling to identify which brain systems standardized tests and clinical symptoms reflect, enabling better-targeted test development.

This study shows that flexible brain state engagement predicts cognitive control across multiple psychiatric disorders using machine learning and three transdiagnostic datasets.

This study compares functional connectivity differences between left- and right-handed individuals across the whole brain, revealing non-region-specific patterns of connectivity.

The paper develops predictive models of substance use vulnerability in children using behavioral and brain data from the ABCD study, identifying risk-seeking and familial factors as key predictors.

This study shows that a validated functional connectivity model predicts attention changes across time and under different sedation states.

The paper shows that functional brain atlases change depending on brain state, using fMRI data from one subject and two replication datasets.

The paper uses task-based functional MRI connectomes to predict cognitive phenotypes across psychiatric diseases and shows differences in connectomes by disease category and symptom severity.

The paper shows that task-induced changes in functional connectivity predict phenotypes better than rest-based connectivity, and develops a method to analyze these changes.