The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Unmixing fMRI with independent component analysis
Abstract
Independent component analysis (ICA) is a statistical method used to discover hidden factors (sources or features) from a set of measurements or observed data such that the sources are maximally independent. Typically, it assumes a generative model where observations are assumed to be linear mixtures of independent sources and works with higher-order statistics to achieve independence. ICA has recently demonstrated considerable promise in characterizing functional magnetic resonance imaging (fMRI) data, primarily due to its intuitive nature and ability for flexible characterization of the brain function. In this article, ICA is introduced and its application to fMRI data analysis is reviewed.
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
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
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308627101 · Cited by 3,684
Investigations into resting-state connectivity using independent component analysis
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1634 · Cited by 3,398
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
Self-citation
Cited by
Functional and Effective Connectivity: A Review
Friston, K. J. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0008 · Cited by 3,415
Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease
Sorg, C., Riedl, V., Mühlau, M., Calhoun, V. D., Eichele, T., Läer, L., Drzezga, A., Förstl, H., Kurz, A., Zimmer, C., et al. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0708803104 · Cited by 1,036
Self-citationA Review of Feature Reduction Techniques in Neuroimaging
Mwangi, B., Tian, T. S., & Soares, J. C. (2014) · Neuroinformatics · doi:10.1007/s12021-013-9204-3 · Cited by 559
Consistency and functional specialization in the default mode brain network
Harrison, B. J., Pujol, J., López-Solà, M., Hernández-Ribas, R., Deus, J., Ortiz, H., Soriano-Mas, C., Yücel, M., Pantelis, C., & Cardoner, N. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0711791105 · Cited by 363
Modulation of Brain Resting-State Networks by Sad Mood Induction
Harrison, B. J., Pujol, J., Ortiz, H., Fornito, A., Pantelis, C., & Yücel, M. (2008) · PLoS ONE · doi:10.1371/journal.pone.0001794 · Cited by 206
Metadata
- Authors
- Calhoun, V. D., & Adali, T.
- Venue
- IEEE Engineering in Medicine and Biology Magazine
- Published
- 2006-03
- Volume
- 25
- Issue
- 2
- Pages
- 79-90
- ISSN
- 0739-5175 (ISSN-L)
- OpenAlex
- W2084147096
- Cited by
- 308
- References
- 5
- FWCI
- 37.31
- Type
- journal article
- Language
- en
- Open access
- closed
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
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.
