Frontiers in Neuroinformatics· 2014Q2
Machine learning for neuroimaging with scikit-learn
- 2,657citations
- Q2SCImago
- 2014year
Short summary
Scikit-learn, a Python machine learning library, offers a versatile toolkit for analyzing high-dimensional neuroimaging data using both supervised and unsupervised learning methods.
AI-generated from the title and abstract; the full text is not read.
Key points
- Scikit-learn provides a wide range of supervised and unsupervised machine learning algorithms.
- The library is applicable to high-dimensional neuroimaging data, such as activation images and resting-state time series.
- Supervised learning in scikit-learn can be used for decoding/encoding brain images related to behavioral/clinical observations.
- Unsupervised learning can identify hidden structures in fMRI data or sub-populations in cohorts.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Frontiers in Neuroinformatics, 2014 · DOI ↗
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Field: Cognitive Neuroscience
Cognitive NeuroscienceNeuroscience