How individual differences in brain network organization track behavioral variability is a
fundamental question in systems neuroscience. Recent work suggests that resting-state and task…

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 …

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

Individual differences in brain anatomy can be used to predict variations in cognitive ability.
Most studies to date have focused on broad population-level trends, but the extent to which …

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

There is significant interest in using neuroimaging data to predict behavior. The predictive
models are often interpreted by the computation of feature importance, which quantifies the …

Background Individual differences in functional brain connectivity can be used to predict
both the presence of psychiatric illness and variability in associated behaviors. However, …

The manner through which individual differences in brain network organization track
population-level behavioral variability is a fundamental question in systems neuroscience. Recent …

Objectives Simultaneous multi-slice (SMS) imaging with short repetition time (TR) accelerates
diffusion tensor imaging (DTI) acquisitions. However, its impact when combined with …

Background Deep gray nuclear pathology relates to motor deterioration in idiopathic Parkinson's
disease (PD). Inconsistent deep nuclear diffusion tensor imaging (DTI) findings in cross-…