Wang, H-T;
Smallwood, J;
Mourao-Miranda, J;
Xia, CH;
Satterthwaite, TD;
Bassett, DS;
Bzdok, D;
(2020)
Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists.
Neuroimage
10.1016/j.neuroimage.2020.116745.
(In press).
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Abstract
The 21st century marks the emergence of “big data” with a rapid increase in the availability of data sets with multiple measurements. In neuroscience, brain-imaging datasets are more commonly accompanied by dozens or even hundreds of phenotypic subject descriptors on the behavioral, neural, and genomic level. The complexity of such “big data” repositories offer new opportunities and pose new challenges for systems neuroscience. Canonical correlation analysis (CCA) is a prototypical family of methods that is useful in identifying the links between variable sets from different modalities. Importantly, CCA is well suited to describing relationships across multiple sets of data and so is well suited to the analysis of big neuroscience datasets. Our primer discusses the rationale, promises, and pitfalls of CCA.
| Type: | Article |
|---|---|
| Title: | Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.neuroimage.2020.116745 |
| Publisher version: | https://doi.org/10.1016/j.neuroimage.2020.116745 |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, and provide a link to the Creative Commons license. You do not have permission under this license to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. |
| Keywords: | machine learning, big data, data science, neuroscience, deep phenotyping. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10095761 |
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