The advent of noninvasive imaging methods like functional magnetic resonance imaging (fMRI) transformed cognitive neuroscience, providing insights into large-scale brain networks and their link to cognition. In the decades since, the majority of fMRI studies have employed a group-level approach, which has characterized the average brain—a construct that emphasizes features aligned across individuals but obscures the idiosyncrasies of any single person's brain. This is a critical limitation, as each brain is unique, including in the topography (i.e., arrangement) of large-scale brain networks. Recently, a new precision fMRI movement, emphasizing extensive scanning of single subjects, has spurred another leap in progress, allowing fMRI researchers to reliably map whole-brain network organization within individuals. Precision fMRI reveals a more detailed picture of functional neuroanatomy, unveiling common features that are obscured at the group level as well as forms of individual variation. However, this presents conceptual hurdles. For instance, if all brains are unique, how do we identify commonalities? And what forms of variation in functional organization are meaningful for understanding cognition? Which sources of variability are stochastic, and which are due to measurement noise? Here, we review recent findings and describe how precision fMRI can be used ( a ) to account for variation across individuals to identify core principles of brain organization and ( b ) to characterize how and why human brains vary. We argue that, as we dive deeper into the individual, overarching principles of brain organization emerge from fine-scale features, even when these vary across individuals.
Gratton et al. (2025) studied this question.