About
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. For Windows, we only support Docker.
Method 1: Conda (Mac, Linux) (recommended)
# 1. Install Miniconda (Mac, 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 (Mac, Linux, Win)
# 1. Install Docker (Mac, Win, Linux)
# 2. Run the following commands:
> docker pull brainiak/brainiak
> docker run -it -p 8899:8899 --name demo brainiak/brainiak
# 3. Copy token from command output. Try an example in your browser (paste token when prompted): http://127.0.0.1:8899/notebooks/examples/funcalign/rsrm_synthetic_reconstruction.ipynb
Method 3: pip (Mac, Linux)
# 1. Install BrainIAK requirements
# 2. Run the following command:
> python3 -m pip install -U brainiak
Analyses
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
Realtime
Realtime fMRI with RT-Cloud
RT-Cloud (documentation) is a BrainIAK companion project that supports running real-time fMRI experiments in the cloud or on a cluster. RT-Cloud has a processing pipeline for receiving and analyzing brain image volumes and providing real-time neurofeedback to subjects. The processing pipeline supports BrainIAK modules for real-time analysis.