Task integration for connectome-based prediction via canonical correlation analysis. - Siyuan Gao; Abigail S. Greene; R. Todd Constable; Dustin Scheinost (ISBI, 2018)

Combining Multiple Connectomes via Canonical Correlation Analysis Improves Predictive Models. - Siyuan Gao; Abigail S. Greene; R. Todd Constable; Dustin Scheinost (MICCAI (3), 2018)

Combining multiple connectomes improves predictive modeling of phenotypic measures. - Siyuan Gao; Abigail S. Greene; R. Todd Constable; Dustin Scheinost (NeuroImage, 2019)

Ten simple rules for predictive modeling of individual differences in neuroimaging. - Dustin Scheinost; Stephanie Noble; Corey Horien; Abigail S. Greene; Evelyn M. R. Lake; Mehraveh Salehi; Siyuan Gao; Xilin Shen; David O'Connor; Daniel S. Barron; Sarah W. Yip; Monica D. Rosenberg; et al. (NeuroImage, 2019)

Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals. - Emily W. Avery; Kwangsun Yoo; Monica D. Rosenberg; Abigail S. Greene; Siyuan Gao; Duk L. Na; Dustin Scheinost; R. Todd Constable; Marvin M. Chun (J. Cogn. Neurosci., 2020)

There is no single functional atlas even for a single individual: Functional parcel definitions change with task. - Mehraveh Salehi; Abigail S. Greene; Amin Karbasi; Xilin Shen; Dustin Scheinost; R. Todd Constable (NeuroImage, 2020)

Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain. - Wenjing Luo; Abigail S. Greene; R. Todd Constable (NeuroImage, 2021)

Associations among Household and Neighborhood Socioeconomic Disadvantages, Resting-state Frontoamygdala Connectivity, and Internalizing Symptoms in Youth. - Ka I. Ip; Lucinda M. Sisk; Corey Horien; May I. Conley; Kristina M. Rapuano; Monica D. Rosenberg; Abigail S. Greene; Dustin Scheinost; R. Todd Constable; B. J. Casey; Arielle Baskin-Sommers; Dylan G. Gee (J. Cogn. Neurosci., 2022)

Large-scale differences in functional organization of left- and right-handed individuals using whole-brain, data-driven analysis of connectivity. - Link Tejavibulya; Hannah Peterson; Abigail S. Greene; Siyuan Gao; Max Rolison; Stephanie Noble; Dustin Scheinost (NeuroImage, 2022)

Connectome-based machine learning models are vulnerable to subtle data manipulations. - Matthew Rosenblatt; Raimundo X. Rodriguez; Margaret L. Westwater; Wei Dai; Corey Horien; Abigail S. Greene; R. Todd Constable; Stephanie Noble; Dustin Scheinost (Patterns, 2023)