Frontiers in Neuroinformatics (Feb 2014)

Machine Learning for Neuroimaging with Scikit-Learn

  • Alexandre eAbraham,
  • Alexandre eAbraham,
  • Fabian ePedregosa,
  • Fabian ePedregosa,
  • Michael eEickenberg,
  • Michael eEickenberg,
  • Philippe eGervais,
  • Philippe eGervais,
  • Andreas eMueller,
  • Jean eKossaifi,
  • Alexandre eGramfort,
  • Alexandre eGramfort,
  • Alexandre eGramfort,
  • Bertrand eThirion,
  • Bertrand eThirion,
  • Gael eVaroquaux,
  • Gael eVaroquaux

DOI
https://doi.org/10.3389/fninf.2014.00014
Journal volume & issue
Vol. 8

Abstract

Read online

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.

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