Brain Imaging Analysis Kit
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
.
Quickstart
You can install BrainIAK wheels from PyPI on Linux, macOS, and Windows, e.g. using pip:
python3 -m pip install brainiak
If you need MPI, see the installation requirements in docs/installation
.
If you have Conda, you can also use our Conda packages (not available for Windows). Installing BrainIAK will also install MPI:
conda install -c brainiak -c conda-forge brainiak
Docker
You can also test BrainIAK without installing it using Docker:
docker pull brainiak/brainiak
docker run -it -p 8899:8899 brainiak/brainiak
Jupyter Notebook will start automatically; visit the URL shown in the Docker command output to access it. You can then run the BrainIAK examples or create new notebooks. You can also try a sample example.
Note that we do not support MPI execution using Docker containers and that performance will not be optimal.
Support
If you have a question or feedback, chat with us on Matrix via Gitter or email our list at brainiak@googlegroups.com. If you find a problem with BrainIAK, you can also open an issue on GitHub.
Examples
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.
Documentation
The documentation is available at http://brainiak.org/docs.
Contributing
We welcome contributions. Have a look at the issues labeled “easy” for starting contribution ideas. Please read the guide in CONTRIBUTING.rst first.
Citing
Please cite BrainIAK in your publications as: “Brain Imaging Analysis Kit, http://brainiak.org.” Additionally, if you use RRIDs to identify resources, please mention BrainIAK as “Brain Imaging Analysis Kit, RRID:SCR_014824”. Finally, please cite the publications referenced in the documentation of the BrainIAK modules you use, e.g., SRM.
Table of contents
- Installation
- Examples
- (Group) Bayesian Representational Similarity Analysis
- Event segmentation and alignment in fMRI data
- Full Correlation Matrix Analysis (FCMA) demo
- fmrisim demo script
- Overview
- Annotated bibliography
- Table of Contents
- 1. Set parameters
- 2. Generate noise
- 3. Generate signal
- 3.1 Specify which voxels in the brain contain signal
- 3.2 Characterize signal for voxels
- 3.3 Generate event time course
- 3.4 Export stimulus time course for analysis
- 3.5 Estimate the voxel weight for each event
- 3.6 Convolve each voxel’s time course with the Hemodynamic Response Function
- 3.7 Establish signal magnitude
- 3.8 Multiply the convolved response with the signal voxels
- 3.9 Combine signal and noise
- 4. Analyse data
- Summary
- Hierarchical Topographic Factor Analysis
- Overview
- Annotated bibliography
- Getting started
- Code
- Summary
- Inverted Encoding Model
- Inverted encoding model
- Intersubject correlation
- Rapid prototyping of fMRI models with
brainiak.matnormal
- Annotated Bibliography
- Set Up Instructions for the Real-Time fMRI Cloud-Based Framework
- Implementing a Real-Time fMRI Cloud-Based Framework
- Overview
- Annotated Bibliography
- Table of Contents
- Before Running This Notebook
- Import Necessary Modules and Declare Important Variables
- Step 1: Start the ProjectInterface Web Server
- Step 2: Start the Synthetic Data Generator
- Step 3: Open the Web Server on the localhost
- Alternate Step 3: Run the classification script from command line
- Summary
- Shared response model
- Release notes
- BrainIAK 0.12 (2024-12-24)
- BrainIAK 0.11 (2020-10-14)
- BrainIAK 0.10 (2020-02-06)
- BrainIAK 0.9.1 (2019-08-27)
- BrainIAK 0.9 (2019-06-18)
- BrainIAK 0.8 (2018-11-06)
- BrainIAK 0.7.1 (2018-02-20)
- BrainIAK 0.7 (2018-02-12)
- BrainIAK 0.6 (2017-11-10)
- BrainIAK 0.5 (2017-05-23)
- BrainIAK 0.4 (2017-01-19)
- BrainIAK 0.3.2 (2016-10-31)
- BrainIAK 0.3.1 (2016-09-30)
- BrainIAK 0.3 (2016-09-30) [YANKED]
- BrainIAK 0.2 (2016-08-03)
- BrainIAK 0.1 (2016-07-12)
- API
- brainiak
- brainiak package
- Subpackages
- brainiak.eventseg package
- brainiak.factoranalysis package
- Submodules
- brainiak.factoranalysis.htfa module
- brainiak.factoranalysis.tfa module
TFA
TFA.local_posterior_
TFA.F_
TFA.W_
TFA.fit()
TFA.get_bounds()
TFA.get_centers()
TFA.get_centers_mean_cov()
TFA.get_factors()
TFA.get_map_offset()
TFA.get_template()
TFA.get_unique_R()
TFA.get_weights()
TFA.get_widths()
TFA.get_widths_mean_var()
TFA.init_centers_widths()
TFA.init_prior()
TFA.set_K()
TFA.set_centers()
TFA.set_centers_mean_cov()
TFA.set_fit_request()
TFA.set_prior()
TFA.set_seed()
TFA.set_widths()
TFA.set_widths_mean_var()
- brainiak.fcma package
- Submodules
- brainiak.fcma.classifier module
Classifier
Classifier.training_data_
Classifier.test_raw_data_
Classifier.test_data_
Classifier.num_voxels_
Classifier.num_features_
Classifier.num_samples_
Classifier.num_digits_
Classifier.decision_function()
Classifier.fit()
Classifier.predict()
Classifier.score()
Classifier.set_fit_request()
Classifier.set_score_request()
- brainiak.fcma.mvpa_voxelselector module
- brainiak.fcma.preprocessing module
- brainiak.fcma.util module
- brainiak.fcma.voxelselector module
- brainiak.funcalign package
- brainiak.hyperparamopt package
- brainiak.matnormal package
- Some properties of the matrix-variate normal distribution
- Matrix normal marginals
- Partitioned matrix normal conditionals
- Submodules
- brainiak.matnormal.covs module
- brainiak.matnormal.matnormal_likelihoods module
- brainiak.matnormal.mnrsa module
- brainiak.matnormal.regression module
- brainiak.matnormal.utils module
- brainiak.reconstruct package
- Submodules
- brainiak.reconstruct.iem module
InvertedEncoding1D
InvertedEncoding2D
InvertedEncoding2D.channels
InvertedEncoding2D.W_
InvertedEncoding2D.define_basis_functions_sqgrid()
InvertedEncoding2D.define_basis_functions_trigrid()
InvertedEncoding2D.fit()
InvertedEncoding2D.get_params()
InvertedEncoding2D.predict()
InvertedEncoding2D.predict_feature_responses()
InvertedEncoding2D.score()
InvertedEncoding2D.score_against_reconstructed()
InvertedEncoding2D.set_fit_request()
InvertedEncoding2D.set_params()
- brainiak.reprsimil package
- Submodules
- brainiak.reprsimil.brsa module
BRSA
BRSA.U_
BRSA.L_
BRSA.C_
BRSA.nSNR_
BRSA.sigma_
BRSA.rho_
BRSA.bGP_
BRSA.lGPspace_
BRSA.lGPinten_
BRSA.beta_
BRSA.beta0_
BRSA.X0_
BRSA.beta0_null_
BRSA.X0_null_
BRSA.n_nureg_
BRSA.random_state_
BRSA.fit()
BRSA.score()
BRSA.set_fit_request()
BRSA.set_score_request()
BRSA.set_transform_request()
BRSA.transform()
GBRSA
Ncomp_SVHT_MG_DLD_approx()
prior_GP_var_half_cauchy()
prior_GP_var_inv_gamma()
- brainiak.searchlight package
- brainiak.utils package
- Submodules
- brainiak.utils.fmrisim module
- brainiak.utils.fmrisim_real_time_generator module
- brainiak.utils.kronecker_solvers module
- brainiak.utils.utils module
- Submodules
- brainiak.image module
- brainiak.io module
- brainiak.isc module
- Subpackages
- brainiak package
- brainiak
- Contributing