  Most neuroimaging experiments are under-powered, limited by the number of
subjects and cognitive processes that an individual study can investigate.
Nonetheless, over decades of research, neuroscience has accumulated an
extensive wealth of results. It remains a challenge to digest this growing
knowledge base and obtain new insights since existing meta-analytic tools are
limited to keyword queries. In this work, we propose Text2Brain, a neural
network approach for coordinate-based meta-analysis of neuroimaging studies to
synthesize brain activation maps from open-ended text queries. Combining a
transformer-based text encoder and a 3D image generator, Text2Brain was trained
on variable-length text snippets and their corresponding activation maps
sampled from 13,000 published neuroimaging studies. We demonstrate that
Text2Brain can synthesize anatomically-plausible neural activation patterns
from free-form textual descriptions of cognitive concepts. Text2Brain is
available at https://braininterpreter.com as a web-based tool for retrieving
established priors and generating new hypotheses for neuroscience research.
