  We introduce a method that takes advantage of high-quality pretrained
multimodal representations to explore fine-grained semantic networks in the
human brain. Previous studies have documented evidence of functional
localization in the brain, with different anatomical regions preferentially
activating for different types of sensory input. Many such localized structures
are known, including the fusiform face area and parahippocampal place area.
This raises the question of whether additional brain regions (or conjunctions
of brain regions) are also specialized for other important semantic concepts.
To identify such brain regions, we developed a data-driven approach to uncover
visual concepts that are decodable from a massive functional magnetic resonance
imaging (fMRI) dataset. Our analysis is broadly split into three sections.
First, a fully connected neural network is trained to map brain responses to
the outputs of an image-language foundation model, CLIP (Radford et al., 2021).
Subsequently, a contrastive-learning dimensionality reduction method reveals
the brain-decodable components of CLIP space. In the final section of our
analysis, we localize shared decodable concepts in the brain using a
voxel-masking optimization method to produce a shared decodable concept (SDC)
space. The accuracy of our procedure is validated by comparing it to previous
localization experiments that identify regions for faces, bodies, and places.
In addition to these concepts, whose corresponding brain regions were already
known, we localize novel concept representations which are shared across
participants to other areas of the human brain. We also demonstrate how this
method can be used to inspect fine-grained semantic networks for individual
participants. We envisage that this extensible method can also be adapted to
explore other questions at the intersection of AI and neuroscience.
