A Transformer-based Neural Language Model that Synthesizes Brain Activation Maps from Free-Form Text Queries

• URL: http://arxiv.org/abs/2208.00840v1
• Date: Sun, 24 Jul 2022 09:15:03 GMT
• Title: A Transformer-based Neural Language Model that Synthesizes Brain Activation Maps from Free-Form Text Queries
• Authors: Gia H. Ngo, Minh Nguyen, Nancy F. Chen, Mert R. Sabuncu
• Abstract summary: Text2Brain is an easy to use tool for synthesizing brain activation maps from open-ended text queries. Text2Brain was built on a transformer-based neural network language model and a coordinate-based meta-analysis of neuroimaging studies.
• Score: 37.322245313730654
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neuroimaging studies are often limited by the number of subjects and cognitive processes that can be feasibly interrogated. However, a rapidly growing number of neuroscientific studies have collectively accumulated an extensive wealth of results. Digesting this growing literature and obtaining novel insights remains to be a major challenge, since existing meta-analytic tools are constrained to keyword queries. In this paper, we present Text2Brain, an easy to use tool for synthesizing brain activation maps from open-ended text queries. Text2Brain was built on a transformer-based neural network language model and a coordinate-based meta-analysis of neuroimaging studies. Text2Brain combines a transformer-based text encoder and a 3D image generator, and was trained on variable-length text snippets and their corresponding activation maps sampled from 13,000 published studies. In our experiments, we demonstrate that Text2Brain can synthesize meaningful neural activation patterns from various free-form textual descriptions. Text2Brain is available at https://braininterpreter.com as a web-based tool for efficiently searching through the vast neuroimaging literature and generating new hypotheses.

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