Fluctuations in Global Brain Activity Are Associated With Changes in Whole-Brain Connectivity of Functional Networks. - Dustin Scheinost; Fuyuze Tokoglu; Xilin Shen; Emily S. Finn; Stephanie Noble; Xenophon Papademetris; R. Todd Constable (IEEE Trans. Biomed. Eng., 2016)

Multisite reliability of MR-based functional connectivity. - Stephanie Noble; Dustin Scheinost; Emily S. Finn; Xilin Shen; Xenophon Papademetris; Sarah C. McEwen; Carrie E. Bearden; Jean Addington; Bradley G. Goodyear; Kristin S. Cadenhead; Heline Mirzakhanian; Barbara A. Cornblatt; et al. (NeuroImage, 2017)

Considering factors affecting the connectome-based identification process: Comment on Waller et al. - Corey Horien; Stephanie Noble; Emily S. Finn; Xilin Shen; Dustin Scheinost; R. Todd Constable (NeuroImage, 2018)

A Mass Multivariate Edge-wise Approach for Combining Multiple Connectomes to Improve the Detection of Group Differences. - Javid Dadashkarimi; Siyuan Gao; Erin Yeagle; Stephanie Noble; Dustin Scheinost (CNI@MICCAI, 2019)

A decade of test-retest reliability of functional connectivity: A systematic review and meta-analysis. - Stephanie Noble; Dustin Scheinost; R. Todd Constable (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)

Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors. - Kwangsun Yoo; Monica D. Rosenberg; Stephanie Noble; Dustin Scheinost; R. Todd Constable; Marvin M. Chun (NeuroImage, 2019)

The Constrained Network-Based Statistic: A New Level of Inference for Neuroimaging. - Stephanie Noble; Dustin Scheinost (MICCAI (7), 2020)

Cluster failure or power failure? Evaluating sensitivity in cluster-level inference. - Stephanie Noble; Dustin Scheinost; R. Todd Constable (NeuroImage, 2020)

Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics. - Eric W. Bridgeford; Shangsi Wang; Zeyi Wang; Ting Xu 001; R. Cameron Craddock; Jayanta Dey; Gregory Kiar; William R. Gray Roncal; Carlo Colantuoni; Christopher Douville; Stephanie Noble; Carey E. Priebe; et al. (PLoS Comput. Biol., 2021)

The Semi-constrained Network-Based Statistic (scNBS): Integrating Local and Global Information for Brain Network Inference. - Wei Dai; Stephanie Noble; Dustin Scheinost (MICCAI (1), 2022)

Leveraging edge-centric networks complements existing network-level inference for functional connectomes. - Raimundo X. Rodriguez; Stephanie Noble; Link Tejavibulya; Dustin Scheinost (NeuroImage, 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)

Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available. - Javid Dadashkarimi; Amin Karbasi; Qinghao Liang; Matthew Rosenblatt; Stephanie Noble; Maya Foster; Raimundo X. Rodriguez; Brendan Adkinson; Jean Ye; Huili Sun; Chris Camp; Michael Farruggia; et al. (Medical Image Anal., 2023)