The analysis and understanding of brain characteristics often require considering regions-level information rather than voxel-sampled data. Subject-specific parcellations have been put forward in recent years, as they can adapt to individual brain organization and thus offer more accurate individual summaries than standard atlases. But the price to pay for adaptability is the lack of group-level consistency of the data representation. Here, we investigate whether the good representations brought by individualized models is merely an effect of circular analysis, in which individual brain features are better represented by subjectspecific summaries, or whether this carries over to new individuals, i.e. whether one can actually adapt an existing parcellation to new individuals and still obtain good summaries in these individuals. For this, we adapt a dictionary learning method to produce brain parcellations. We use it on a deep phenotyping dataset to assess quantitatively the patterns of activity obtained under naturalistic and controlled-task-based settings. We show that the benefits of individual parcellations are substantial, but that they vary a lot across brain systems. Highlights • Individualized parcellations schemes learned on naturalistic data generalize to task data, not only within, but also across individuals • Individualized parcellations better represent functional signal than fixed parcellations in most brain regions • Parcellations obtained from naturalistic data fail to capture structure in motor and somato-sensory cortex • Data-driven parcellations make it possible to adapt resolution and better fit brain signals. • Some brain systems require higher resolution for accurate representations than that of standard atlases