# NeuroQuery: comprehensive meta-analysis of human brain mapping

arXiv: https://arxiv.org/abs/2002.09261 · HTML: https://mlsanity.com/paper/2002.09261/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2020.

## Built on by (most cited first, 10 of 10)

- [Scaling laws for decoding images from brain activity](https://mlsanity.com/paper/2501.15322/) (2025)
- [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)
- [Text2Brain: Synthesis of Brain Activation Maps from Free-form Text Query](https://mlsanity.com/paper/2109.13814/) (2021)
- [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)
- [On Creating A Brain-To-Text Decoder](https://mlsanity.com/paper/2501.06326/) (2025)
- [Deep Variational Lesion-Deficit Mapping](https://mlsanity.com/paper/2305.17478/) (2023)
- [Computational limits to the legibility of the imaged human brain](https://mlsanity.com/paper/2309.07096/) (2023)
- [Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networks](https://mlsanity.com/paper/2510.03351/) (2025)
- [MNM : Multi-level Neuroimaging Meta-analysis with Hyperbolic Brain-Text Representations](https://mlsanity.com/paper/2511.21092/) (2025)

## Abstract (arXiv)

Reaching a global view of brain organization requires assembling evidence on widely different mental processes and mechanisms. The variety of human neuroscience concepts and terminology poses a fundamental challenge to relating brain imaging results across the scientific literature. Existing meta-analysis methods perform statistical tests on sets of publications associated with a particular concept. Thus, large-scale meta-analyses only tackle single terms that occur frequently. We propose a new paradigm, focusing on prediction rather than inference. Our multivariate model predicts the spatial distribution of neurological observations, given text describing an experiment, cognitive process, or disease. This approach handles text of arbitrary length and terms that are too rare for standard meta-analysis. We capture the relationships and neural correlates of 7 547 neuroscience terms across 13 459 neuroimaging publications. The resulting meta-analytic tool, neuroquery.org, can ground hypothesis generation and data-analysis priors on a comprehensive view of published findings on the brain.