Author of the publication

Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals.

, , , , , , , , and . J. Cogn. Neurosci., 32 (2): 241-255 (2020)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

 

Other publications of authors with the same name

Connectome-based machine learning models are vulnerable to subtle data manipulations., , , , , , , , and . Patterns, 4 (7): 100756 (July 2023)Associations among Household and Neighborhood Socioeconomic Disadvantages, Resting-state Frontoamygdala Connectivity, and Internalizing Symptoms in Youth., , , , , , , , , and 2 other author(s). J. Cogn. Neurosci., 34 (10): 1810-1841 (2022)Large-scale differences in functional organization of left- and right-handed individuals using whole-brain, data-driven analysis of connectivity., , , , , , and . NeuroImage, (2022)Combining Multiple Connectomes via Canonical Correlation Analysis Improves Predictive Models., , , and . MICCAI (3), volume 11072 of Lecture Notes in Computer Science, page 349-356. Springer, (2018)Task integration for connectome-based prediction via canonical correlation analysis., , , and . ISBI, page 87-91. IEEE, (2018)There is no single functional atlas even for a single individual: Functional parcel definitions change with task., , , , , and . NeuroImage, (2020)Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals., , , , , , , , and . J. Cogn. Neurosci., 32 (2): 241-255 (2020)Ten simple rules for predictive modeling of individual differences in neuroimaging., , , , , , , , , and 3 other author(s). NeuroImage, (2019)Combining multiple connectomes improves predictive modeling of phenotypic measures., , , and . NeuroImage, (2019)Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain., , and . NeuroImage, (2021)