Numerous functional neuroimaging studies in humans used univariate group analyses to identify brain areas that show increased activity across participants during specific tasks. However, there is increasing evidence that group-level analyses may obscure important parts of the signal response (Margulies, 2017; Poldrack, 2017). Here, we re-analyzed data from our recent study (Hartwigsen et al., 2017) that investigated functional reorganization in the language network. We show that across-voxel pattern-learning approaches are useful to isolate plastic changes in neural networks underlying cognitive functions at the individual subject level. Using predictive machine-learning tools to identify and exploit subject-specific neural activity patterns have been argued to become an important cornerstone of precision medicine in psychiatry and neurology.