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

Predicting brain activity from word embeddings during natural language comprehension GitHub Colab

This tutorial introduces a typical encoding framework for mapping linguistic embeddings onto human brain activity during natural language comprehension. The tutorial includes worked examples for both fMRI and ECoG datasets collected while subjects listened to naturalistic spoken narratives. Two types of word embeddings are obtained based on the stimulus transcripts: static word embeddings from word2vec and contextual word embeddings from GPT-2. Encoding models are estimated using banded ridge regression—this allows us to predict brain activity from word embeddings for left-out segments of data. The tutorial can be retrieved from GitHub or run interactively on Colab.

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., Turner, D. M., Vo, V. A., Wallace, G., Wang, Y., Williams, J. A., 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. (2021). BrainIAK: The Brain Imaging Analysis Kit. Aperture Neuro, 1(4). 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