# Text2Brain: Synthesis of Brain Activation Maps from Free-form Text Query

arXiv: https://arxiv.org/abs/2109.13814 · HTML: https://mlsanity.com/paper/2109.13814/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [What do you learn from context? Probing for sentence structure in contextualized word representations](https://mlsanity.com/paper/1905.06316/) (2019)
- [NeuroQuery: comprehensive meta-analysis of human brain mapping](https://mlsanity.com/paper/2002.09261/) (2020)

## Built on by (most cited first, 5 of 5)

- [Transforming Evidence Synthesis: A Systematic Review of the Evolution of Automated Meta-Analysis in the Age of AI](https://mlsanity.com/paper/2504.20113/) (2025)
- [Chat2Brain: A Method for Mapping Open-Ended Semantic Queries to Brain Activation Maps](https://mlsanity.com/paper/2309.05021/) (2023)
- [A Transformer-based Neural Language Model that Synthesizes Brain Activation Maps from Free-Form Text Queries](https://mlsanity.com/paper/2208.00840/) (2022)
- [MNM : Multi-level Neuroimaging Meta-analysis with Hyperbolic Brain-Text Representations](https://mlsanity.com/paper/2511.21092/) (2025)
- [Zero-shot Learning of Individualized Task Contrast Prediction from Resting-state Functional Connectomes](https://mlsanity.com/paper/2310.14105/) (2023)

## Abstract (arXiv)

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.