Machine Learning for Neuroimaging with Scikit-Learn
This work offers a practical solution for researchers in neuroimaging by enabling the use of existing machine learning methods on new data, but it is incremental as it applies standard tools without introducing novel techniques.
The paper demonstrates how scikit-learn, a Python machine learning library, can be applied to neuroimaging data for tasks like decoding brain images and uncovering hidden structures, providing a versatile tool for brain analysis.
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.