[](https://github.com/neuroquery/neuroquery/actions/workflows/testing.yml)[](https://codecov.io/gh/neuroquery/neuroquery)
[](https://mybinder.org/v2/gh/neuroquery/neuroquery.git/main?filepath=examples)
# NeuroQuery
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](https://elifesciences.org/articles/53385).
This Python package permits using NeuroQuery offline or integrating it in other
applications.
## Getting started
[Quick demo](https://nbviewer.jupyter.org/github/neuroquery/neuroquery/blob/main/examples/minimal_example.ipynb)
### Dependencies
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.
### Installation
`neuroquery` can be installed with
```
pip install neuroquery
```
### Usage
In the `examples` folder,
[`minimal_example.ipynb`](https://nbviewer.jupyter.org/github/neuroquery/neuroquery/blob/main/examples/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:
```python
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`](https://nbviewer.jupyter.org/github/neuroquery/neuroquery/blob/main/examples/training_neuroquery.ipynb)
in the examples).
BSD 3-Clause License
Copyright (c) 2023, Jérôme Dockès
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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