  A long standing goal in neuroscience has been to elucidate the functional
organization of the brain. Within higher visual cortex, functional accounts
have remained relatively coarse, focusing on regions of interest (ROIs) and
taking the form of selectivity for broad categories such as faces, places,
bodies, food, or words. Because the identification of such ROIs has typically
relied on manually assembled stimulus sets consisting of isolated objects in
non-ecological contexts, exploring functional organization without robust a
priori hypotheses has been challenging. To overcome these limitations, we
introduce a data-driven approach in which we synthesize images predicted to
activate a given brain region using paired natural images and fMRI recordings,
bypassing the need for category-specific stimuli. Our approach -- Brain
Diffusion for Visual Exploration ("BrainDiVE") -- builds on recent generative
methods by combining large-scale diffusion models with brain-guided image
synthesis. Validating our method, we demonstrate the ability to synthesize
preferred images with appropriate semantic specificity for well-characterized
category-selective ROIs. We then show that BrainDiVE can characterize
differences between ROIs selective for the same high-level category. Finally we
identify novel functional subdivisions within these ROIs, validated with
behavioral data. These results advance our understanding of the fine-grained
functional organization of human visual cortex, and provide well-specified
constraints for further examination of cortical organization using
hypothesis-driven methods.
