Brain-predicted age difference scores are calculated by subtracting chronological age from 'brain' age. Positive scores reflect accelerated ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brainpredicted age difference scores and specific cognitive functions has not been systematically examined. First, applying machine learning to 1,359 T1-weighted MRI scans, we predicted the relationship between chronological age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological age: Dokuz Eylül University (n=175), the Cognitive Reserve/Reference Ability Neural Network study (n=380), and The Irish Longitudinal Study on Ageing (n=487). Each independent dataset had rich neuropsychological data. Brain-predicted age difference scores were significantly negatively correlated with general cognitive status (two datasets); processing speed, visual attention, cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). They were not significantly correlated with processing speed, cognitive flexibility, response inhibition and selective attention, sustained attention, verbal episodic memory or working memory in any dataset. As such, there is firm evidence of correlations between increased brain-predicted age differences and reduced cognitive function only in some domains that are implicated in cognitive ageing.