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

Individualized behavioral/cognitive prediction using machine learning (ML) regression
approaches is becoming increasingly applied. The specific ML regression algorithm and …

Abstracts Brain functional connectivity features can predict cognition and behavior at the
level of the individual. Most studies measure univariate signals, correlating timecourses from …

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

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

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

Neuroimaging is a fast-developing research area in which anatomical and functional images
of human brains are collected using techniques such as functional magnetic resonance …

A major goal of neuroimaging studies is to develop predictive models to analyze the
relationship between whole brain functional connectivity patterns and behavioural traits …

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 …