Brain Imaging Analysis Kit

Advanced fMRI analyses in Python, optimized for speed under the hood with MPI, Cython, and C++.


Open-source Python software

BrainIAK applies advanced machine learning methods and high-performance computing to analyzing neuroimaging data. It is tightly integrated with SciKit-Learn, and includes modules for Full Correlation Matrix Analysis (FCMA), Multi-voxel Pattern Analysis (MVPA), a suite of methods for Shared Response Modeling (SRM), Topographic Factor Analysis (TFA), Bayesian-derived methods for Representational Similarity Analysis (RSA), and more.

Get Started

Installing Brainiak

The easiest way to get started is via Conda. We provide a Docker image and a pip package (which requires extra steps) as alternatives.

Method 1: Conda (recommended)
# 1. Install Miniconda (Mac, Win, Linux)
# 2. Create a Conda environment (link)
> conda create -n venv
# 3. Activate the Conda environment (link) 
> source activate venv
# 4 Install Brainiak
> conda install -c brainiak -c defaults -c conda-forge brainiak

Method 2: Docker
# 1. Install Docker (Mac, Win, Linux)
# 2. Run the following commands:
> docker pull brainiak/brainiak
> docker run -it -p 8888:8888 -v brainiak:/mnt --name demo brainiak/brainiak
> python3 -m notebook --allow-root --no-browser --ip=
# 3. Visit http://localhost:8888
Method 3: pip
# 1. Install BrainIAK requirements
# 2. Run the following command:
> python3 -m pip install -U brainiak

Supported BrainIAK modules

FCMA: Full correlation matrix analysis.

MVPA: Multi-voxel pattern analysis.

SRM: A suite of methods for Shared Response Modeling.

TFA: Topographic factor analysis.

RSA: Bayesian-derived methods for Representational Similarity Analysis

Other: MPI-enabled searchlight, event segmentation, fMRI simulation, and more