Brainiak Education: User-Friendly Tutorials for Advanced, Computationally-Intensive fMRI Analysis

Manoj Kumar Presenter
Princeton University
Princeton, NJ 
United States
Software Demonstrations 
Advanced brain imaging analysis methods, including multivariate pattern analysis (MVPA), functional connectivity, and functional alignment, have become powerful tools in cognitive neuroscience over the past decade. There now exist multiple software packages that implement some of these techniques. Although these packages are useful for expert practitioners, novice users face a steep learning curve because of the computational skills required. Furthermore, most standard fMRI analysis packages (e.g., AFNI, FSL, SPM) focus primarily on preprocessing and univariate analyses, leaving a gap in how to integrate advanced tools. BrainIAK ( is a newer, open-source Python software package that seamlessly combines several cutting-edge, computationally efficient techniques with other Python packages (e.g., nilearn, scikit-learn) for file handling, visualization, and machine learning, picking up where other packages leave off. As part of efforts to disseminate this package, we have developed user-friendly tutorials and exercises in Jupyter notebook format for learning BrainIAK and advanced fMRI analysis in Python more generally ( (Kumar et al., in press). These materials cover cutting-edge techniques including: MVPA (Norman et al., 2006); representational similarity analysis (Kriegeskorte et al., 2008); background connectivity (Al-Aidroos et al., 2012); full correlation matrix analysis (Wang et al., 2015); inter-subject correlation (Hasson et al., 2004); inter-subject functional connectivity (Simony et al., 2016); shared response modeling (Chen et al., 2015); real-time fMRI (deBettencourt et al., 2015); and event segmentation using hidden Markov models (Baldassano et al., 2017). For long running jobs, with large memory consumption, we have provided detailed information on using high-performance computing clusters (HPCs). These notebooks were successfully deployed and have been extensively tested at multiple sites, including advanced fMRI analysis courses at Yale and Princeton and at multiple workshops and hackathons. We hope that these materials become part of a growing pool of open-source software and educational materials for large-scale, reproducible fMRI analysis.