The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Machine learning for neuroimaging with scikit-learn
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
Mesulam, M. (1998) · Brain · doi:10.1093/brain/121.6.1013 · Cited by 3,072
A method for making group inferences from functional MRI data using independent component analysis
Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001) · Human Brain Mapping · doi:10.1002/hbm.1048 · Cited by 2,994
Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature
Destrieux, C., Fischl, B., Dale, A., & Halgren, E. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.010 · Cited by 2,956
Machine learning classifiers and fMRI: A tutorial overview
Pereira, F., Mitchell, T., & Botvinick, M. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.11.007 · Cited by 1,624
Cited by
fMRIPrep: A robust preprocessing pipeline for functional MRI
Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., et al. (2019) · Nature Methods · doi:10.1038/s41592-018-0235-4 · Cited by 3,905
Variability in the analysis of a single neuroimaging dataset by many teams
(2020) · doi:10.1038/s41586-020-2314-9 · Cited by 1,097
neuromaps: structural and functional interpretation of brain maps
Markello, R. D., Hansen, J. Y., Liu, Z.-Q., Bazinet, V., Shafiei, G., Suárez, L. E., Blostein, N., Seidlitz, J., Baillet, S., Satterthwaite, T. D., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01625-w · Cited by 479
Brains and algorithms partially converge in natural language processing
Caucheteux, C., & King, J.-R. (2022) · Communications Biology · doi:10.1038/s42003-022-03036-1 · Cited by 318
Analysis of task-based functional MRI data preprocessed with fMRIPrep
Esteban, O., Ciric, R., Finc, K., Blair, R. W., Markiewicz, C. J., Moodie, C. A., Kent, J. D., Goncalves, M., DuPre, E., Gomez, D. E. P., et al. (2020) · Nature Protocols · doi:10.1038/s41596-020-0327-3 · Cited by 315
Metadata
- Authors
- Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., Gramfort, A., Thirion, B., & Varoquaux, G.
- Venue
- Frontiers in Neuroinformatics
- Publisher
- Frontiers Media SA
- Published
- 2014
- Volume
- 8
- Pages
- 14-14
- ISSN
- 1662-5196 (ISSN-L)
- OpenAlex
- W2151591509
- Cited by
- 2,384
- References
- 14
- FWCI
- 22.47
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Machine learning for neuroimaging with scikit-learn. https://app.cochat.ai/papers/10.3389/fninf.2014.00014
- The paper
- Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., Gramfort, A., Thirion, B., & Varoquaux, G. (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in Neuroinformatics. https://doi.org/10.3389/fninf.2014.00014
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
