Diffusion-based stimulus optimization reveals functional organization across higher visual cortex

Abstract

Characterizing the fine-grained functional organization of human higher visual cortex remains a central challenge, as traditional neuroimaging experiments constrain the diversity of stimuli that can be sampled. In prior work we addressed this challenge by developing a novel data-driven tool, termed "BrainDiVE" (Luo et al. 2023), which synthesizes naturalistic images predicted to strongly activate specific brain regions. BrainDiVE leverages pretrained image diffusion models guided by gradients from an image-computable fMRI encoding model. Here, we experimentally validated BrainDiVE by generating images predicted to maximally activate different functional regions of interest and then presenting them to new participants (n=12) in an fMRI study. The model-generated images elicited robust, spatially specific responses in the targeted brain regions, producing significantly greater category selectivity than natural images, validating the methods ability to capture generalizable neural tuning properties in human ventral visual cortex. We further showed that region-targeted images exaggerate specific sets of low-level and mid-level image statistics, suggesting that category-selective regions are tuned to continuous directions in feature space. Moreover, we demonstrated fine-grained experimental control by differentially activating two face-selective regions, the occipital face area (OFA) and fusiform face area (FFA), providing additional evidence that these regions encode distinct aspects of faces. Finally, we identify a posterior-to-anterior functional gradient within the occipital place area (OPA), suggesting topographic organization based on scene properties such as distance and indoor-outdoor location. These findings enhance our understanding of the representational structure of category-selective regions and introduce a new paradigm for probing neural selectivity in human visual cortex.