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Abstract Poor generalizability continues to hinder mapping brain activity to behavior using human neuroimaging data. A potential solution is predictive modeling, which evaluates generalizability in unseen data from the same dataset. However, predictive models often fail external validation—a stricter test of generalizability involving evaluation in an independent dataset.

Scientists use rare 'Einstein Cross' to learn about young galaxy with surprisingly old stars "The discovery of this exceptional object has allowed us to accurately study the nature of the stars at the center of an elliptical galaxy in a …

Abstract A central objective in human neuroimaging is to understand the neurobiology underlying cognition and mental health. Machine learning models trained on neuroimaging data are increasingly used as tools for predicting behavioural phenotypes, enhancing precision medicine and improving generalizability compared with traditional MRI studies. However, the high dimensionality of brain connectivity data makes model interpretation challenging.