Open code

I contribute software to several open source projects, particularly the BrainIAK Python package for fMRI analysis. You can find code repositories associated with my research projects on my GitHub profile: GitHub


BrainIAK: The Brain Imaging Analysis Kit website GitHub

The Brain Imaging Analysis Kit (BrainIAK) is a free and open source Python package for computationally-optimized advanced fMRI analysis. BrainIAK supports a variety of methods including intersubject correlation (ISC) and intersubject functional connectivity (ISFC), the shared response model (SRM), full correlation matrix analysis (FCMA), Bayesian representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEM), fMRI data simulation (fmrisim), and real-time fMRI neurofeedback.

Kumar, M., Anderson, M. J., Antony, J. W., Baldassano, C., Brooks, P. P., Cai, M. B., Chen, P.-H. C., Ellis, C. T., Henselman-Petrusek, G., Huberdeau, D., Hutchinson, J. B., Li, P. Y., Lu, Q., Manning, J. R., Mennen, A. C., Nastase, S. A., Richard, H., Schapiro, A. C., Schuck, N. W., Shvartsman, M., Sundaraman, N., Suo, D., Turek, J. S., Vo, V. A., Wallace, G., Wang, Y., Zhang, H., Zhu, X., Capota, M., Cohen, J. D., Hasson, U., Li, K., Ramadge, P. J., Turk-Browne, N. B., Willke, T. L., & Norman, K. A. (2020). BrainIAK: The Brain Imaging Analysis Kit. OSF Preprints. DOI PDF GitHub


Princeton Handbook for Reproducible Neuroimaging DOI Handbook

The goal of this handbook is to provide a reference for best practices in reproducible fMRI research. There’s no single “right” answer for many questions in fMRI, but here we try to provide helpful references and recommendations. Many elements of the handbook are specific to the Princeton Neuroscience Institute computing infrastructure, but the principles are widely applicable. This document will be updated over time as best practices evolve.

Brooks, P. P., McDevitt, E. A., Mennen, A. C., Testerman, M., Kim, N. Y., Visconti di Oleggio Castello, M., & Nastase, S. A. (2020). Princeton Handbook for Reproducible Neuroimaging. DOI


Intersubject correlation tutorial  DOI GitHub

This tutorial introduces intersubject correlation (ISC) analysis and corresponding statistical tests using a Jupyter Notebook. The analysis are implemented in Python using the Brain Imaging Analysis Kit (BrainIAK). To run the analyses interactively in the cloud using Google Colab, click here: Tutorial in Google Colab. This tutorial accompanies a “tools of the trade” article published in Social Cognitive and Affective Neuroscience:

Nastase, S. A., Gazzola, V., Hasson, U., & Keysers, C. (2019). Measuring shared responses across subjects using intersubject correlation. Social Cognitive and Affective Neuroscience, 14(6), 667–685. DOI PDF