NeuroQuery is a tool and a statistical model for meta-analysis of the functional neuroimaging literature.
Given a text query, it can produce a brain map of the most relevant anatomical structures according to the current scientific literature.
It can be used through a web interface: https://neuroquery.org
Technical details and extensive validation are provided in this paper.
This Python package permits using NeuroQuery offline or integrating it in other applications.
NeuroQuery requires Python 3, numpy, scipy, scikit-learn, nilearn, pandas, regex, lxml, and requests.
nltk is an optional dependency needed only if you use stemming or lemmatization for tokenization of input text.
python-Levenshtein is an optional dependency used only in some parts of
tokenization. If you use the vocabulary lists provided with neuroquery or in
neuroquery_data it is not needed.
neuroquery can be installed with
pip install neuroquery
In the examples folder,
minimal_example.ipynb
shows basic usage of neuroquery.
neuroquery has a function to download a trained model so that users can get
started right away:
from neuroquery import fetch_neuroquery_model, NeuroQueryModel
from nilearn.plotting import view_img
encoder = NeuroQueryModel.from_data_dir(fetch_neuroquery_model())
# encoder returns a dictionary containing a brain map and more,
# see examples or documentation for details
view_img(
encoder("Parkinson's disease")["brain_map"], threshold=3.).open_in_browser()neuroquery also provides classes to train new models from scientific
publications' text and stereotactic peak activation coordinates (see
training_neuroquery.ipynb
in the examples).