Nilearn and Nistats: Machine learning and statistics for fMRI in Python

Efficient and reproducible science depends on a strong software ecosystem [1]. We present Nilearn and Nistats, two Python packages empowering the neuroimaging community, which will soon be united in the same library. Nilearn (https://nilearn.github.io) focuses on fast and easy statistical learning on fMRI data. It provides efficient and reliable implementations of machine learning methods tailored to the needs of the neuroimaging community. It builds upon a Python "data science ecosystem" of packages such as numpy [2], scipy [3], scikit-learn [4], and pandas [5], that are extensively used, tested and optimized by a large scientific and industrial community. This makes Nilearn easy to use for a broad spectrum of researchers who are familiar with the Python ecosystem, and reduces the need of learning the idiosyncrasies of specific command line or GUI-based neuroimaging tools. Specifically, Nilearn provides methods for decoding functional connectivity analysis, and biomarker extraction. It also includes datasets for teaching, as well as interactive visualization of brain images and connectomes.

Nistats provides tools for mass univariate linear models –standard analysis in fMRI– https://nistats.github.io. It will eventually become a part of Nilearn. Both libraries have been widely used, taught, and maintained by the neuroimaging community for many years. They build upon and contribute to the growing ecosystem of Python tools for neuroimaging, with tools such as nibabel [6] and dipy [7].