2020: BrainIAK Tutorials: User-friendly tutorials for cutting-edge MVPA methods
Manoj Kumar, Cameron T. Ellis, Nicholas B. Turk-Browne, Peter J. Ramadge, Kenneth A. Norman contact
2023: Enabling large-scale fMRI analysis with BrainIAK
Mihai Capotă, Daniel Suo, Theodore Willke, Kenneth Norman, Nicholas Turk-Browne, Kai Li, Jonathan Cohen contact
2045: Distributed deadline computing for real-time brain imaging analysis
Daniel C. Suo, J. Benjamin Hutchinson, Megan T. DeBettencourt, Anne C. Mennen, Yida Wang, Theodore L. Willke, Nicholas B. Turk-Browne, Kenneth A. Norman, Jonathan D. Cohen, Kai Li contact
2535: Matrix-normal models for fMRI analysis
Michael Shvartsman, Mikio C. Aoi, Narayanan Sundaram, Adam Charles, Theodore L. Willke, Jonathan D. Cohen contact
2858: Using Closed-Loop Real-Time fMRI Neurofeedback to Induce Neural Plasticity and Influence Perceived Similarity
Marius Cătălin Iordan, Victoria J.H. Ritvo, Kenneth A. Norman, Nicholas B. Turk-Browne, Jonathan D. Cohen contact
BrainIAK is a Python package for high-performance neuroimaging analysis. The above posters associated with the BrainIAK project will appear at OHBM 2018 along with several code examples.
The easiest way to get started with the examples is via conda install.
BrainIAK is the result of an ongoing collaboration among the Brain-Inspired Computing Lab at Intel, the Princeton Neuroscience Institute, and other institutions. All parties maintain a firm commitment to community-developed open-source software.