Machine learning for neuroimaging with scikit-learn

TLDR

Statistical machine learning methods are increasingly used for neuroimaging data analysis, enabling high‑dimensional modeling of activation images or resting‑state time series and facilitating supervised decoding or encoding of brain images with behavioral or clinical observations, as well as unsupervised discovery of hidden structures or sub‑populations. The study illustrates how scikit‑learn can be applied to various functional neuroimaging tasks to perform key analysis steps. The authors use scikit‑learn, a Python library, to execute supervised and unsupervised learning algorithms on neuroimaging data.

Abstract

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.