Algorithmic biases that favor majority populations pose a key challenge to the application of
machine learning for precision medicine. Here, we assessed such bias in prediction models …

Establishing brain-behavior associations that map brain organization to phenotypic
measures and generalize to novel individuals remains a challenge in neuroimaging …

Resting-state functional connectivity (RSFC) is widely used to predict behavioral measures.
To predict behavioral measures, representing RSFC with parcellations and gradients are the …

A fundamental goal across the neurosciences is the characterization of relationships linking
brain anatomy, functioning, and behavior. Although various MRI modalities have been …

Spontaneous brain activity, as observed in functional neuroimaging, has been shown to
display reproducible structure that expresses brain architecture and carries markers of brain …

Cognitive performance can be predicted from an individual's functional brain connectivity
with modest accuracy using machine learning approaches. As yet, however, predictive …

Resting-state functional magnetic resonance imaging (rs-fMRI) allows estimation of
individual-specific cortical parcellations. We have previously developed a multi-session …

Prior work has shown that there is substantial interindividual variation in the spatial
distribution of functional networks across the cerebral cortex, or functional topography …

Global signal regression (GSR) is one of the most debated preprocessing strategies for
resting-state functional MRI. GSR effectively removes global artifacts driven by motion and …

Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to
delineate individual-specific brain networks. A major question is whether individual-specific …