NeuroConText: Contrastive learning for neuroscience meta-analysis with rich text representation

Abstract Brain meta-analysis is the common way to gather information about human brain function across the existing literature in order to formulate hypotheses and contextualize new findings. However, automated meta-analysis tools face challenges such as inconsistent terminology and difficulties in analyzing long texts and capturing semantic meaning because they still rely on bag-of-words approaches; furthermore, sparse coordinate reporting in articles distorts the activation distribution due to incomplete data. This paper introduces NeuroConText, a predictive text-to-brain modeling framework designed to support brain meta-analysis by bridging neuroscience text, brain location coordinates, and brain images within a shared latent space. This framework follows the predictive brain meta-analysis paradigm: it learns a regression from text descriptions to whole-brain activation maps and also enables the retrieval of relevant studies through contrastive learning, optimizing a multi-objective loss that combines retrieval and reconstruction objectives. Furthermore, NeuroConText supports second-level statistical synthesis by providing activation associated with top-K retri