{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/machine-learning-for-neuroimaging-with-scikit","title":"Machine Learning for Neuroimaging with Scikit-Learn","arxiv_id":"1412.3919","date":"2014-12-12","proceeding":null,"authors":["Alexandre Abraham","Fabian Pedregosa","Michael Eickenberg","Philippe Gervais","Andreas Muller","Jean Kossaifi","Alexandre Gramfort","Bertrand Thirion","Gäel Varoquaux"],"abstract":"Statistical machine learning methods are increasingly used for neuroimaging\ndata analysis. Their main virtue is their ability to model high-dimensional\ndatasets, e.g. multivariate analysis of activation images or resting-state time\nseries. Supervised learning is typically used in decoding or encoding settings\nto relate brain images to behavioral or clinical observations, while\nunsupervised learning can uncover hidden structures in sets of images (e.g.\nresting state functional MRI) or find sub-populations in large cohorts. By\nconsidering different functional neuroimaging applications, we illustrate how\nscikit-learn, a Python machine learning library, can be used to perform some\nkey analysis steps. Scikit-learn contains a very large set of statistical\nlearning algorithms, both supervised and unsupervised, and its application to\nneuroimaging data provides a versatile tool to study the brain.","url_abs":"http://arxiv.org/abs/1412.3919v1","url_pdf":"http://arxiv.org/pdf/1412.3919v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"machine-learning-for-neuroimaging-with-scikit","repo_url":"https://github.com/AlexandreAbraham/frontiers2013","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1412.3919","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}