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The Brain Imaging Analysis Kit is a package of Python modules useful for neuroscience, primarily focused on functional Magnetic Resonance Imaging (fMRI) analysis.

The package was originally created by a collaboration between Intel and the Princeton Neuroscience Institute (PNI).

To reduce verbosity, we may refer to the Brain Imaging Analysis Kit using the BrainIAK abbreviation. Whenever lowercase spelling is used (e.g., Python package name), we use brainiak.


If you have Conda:

conda install -c brainiak -c defaults -c conda-forge brainiak

Otherwise, or if you want to compile from source, install the requirements (see docs/installation) and then install from PyPI:

python3 -m pip install brainiak

Note that to use the brainiak.matnormal package, you need to install additional dependencies. As of October 2020, the required versions are not available as Conda packages, so you should install from PyPI, even when using Conda:

python3 -m pip install -U tensorflow tensorflow-probability

Note that we do not support Windows.


You can also test BrainIAK without installing it using Docker:

docker pull brainiak/brainiak
docker run -it -p 8888:8888 -v brainiak:/mnt --name demo brainiak/brainiak

To run Jupyter notebooks in the running container, try:

python3 -m notebook --allow-root --no-browser --ip=

Then visit http://localhost:8888 in your browser and enter the token. Protip: run screen before running the notebook command.

Note that we do not support MPI execution using Docker containers and that performance will not be optimal.


If you have a question or feedback, chat with us on Gitter or email our list at If you find a problem with BrainIAK, you can also open an issue on GitHub.


We include BrainIAK usage examples in the examples directory of the code repository, e.g., funcalign/srm_image_prediction_example.ipynb.

To run the examples, download an archive of the latest BrainIAK release from GitHub. Note that we only support the latest release at this moment, so make sure to upgrade your BrainIAK installation.


The documentation is available at


We welcome contributions. Have a look at the issues labeled “easy” for starting contribution ideas. Please read the guide in CONTRIBUTING.rst first.


Please cite BrainIAK in your publications as: “Brain Imaging Analysis Kit,” along with the two papers below. Additionally, if you use RRIDs to identify resources, please mention BrainIAK as “Brain Imaging Analysis Kit, RRID:SCR_014824”.

Manoj Kumar, Michael J. Anderson, James W. Antony, Christopher Baldassano, Paula P. Brooks, Ming Bo Cai, Po-Hsuan Cameron Chen, Cameron T. Ellis, Gregory Henselman-Petrusek, David Huberdeau, J. Benjamin Hutchinson, Y. Peeta Li, Qihong Lu, Jeremy R. Manning, Anne C. Mennen, Samuel A. Nastase, Hugo Richard, Anna C. Schapiro, Nicolas W. Schuck, Michael Shvartsman, Narayanan Sundaram, Daniel Suo, Javier S. Turek, David Turner, Vy A. Vo, Grant Wallace, Yida Wang, Jamal A. Williams, Hejia Zhang, Xia Zhu, Mihai Capota, Jonathan D. Cohen, Uri Hasson, Kai Li, Peter J. Ramadge, Nicholas B. Turk-Browne, Theodore L. Willke, Kenneth A. Norman, 2021. BrainIAK: The Brain Imaging Analysis Kit. Aperture Neuro, 1(4), 19.

Kumar, M., Ellis, C.T., Lu, Q., Zhang, H., Capotă, M., Willke, T.L., Ramadge, P.J., Turk-Browne, N.B. and Norman, K.A., 2020. BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis. PLoS Computational Biology, 16(1), e1007549.

Finally, please cite the publications referenced in the documentation of the BrainIAK modules you use, e.g., SRM.

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