Cognitive performance can be predicted from an individual's functional brain connectivity
with modest accuracy using machine learning approaches. As yet, however, predictive
models have arguably yielded limited insight into the neurobiological processes supporting
cognition. To do so, feature selection and feature weight estimation need to be reliable to
ensure that important connections and circuits with high predictive utility can be reliably
identified. We comprehensively investigate feature weight test-retest reliability for various …