English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Identifying Shared Decodable Concepts in the Human Brain Using Image-Language Foundation Models