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Improved interpretability of brain-behavior CCA with Domain-driven Dimension Reduction
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Liu, Zhangdaihong, Whitaker, Kirstie J., Smith, Stephen M. and Nichols, Thomas E. (2022) Improved interpretability of brain-behavior CCA with Domain-driven Dimension Reduction. Frontiers in Neuroscience, 16 . 851827. doi:10.3389/fnins.2022.851827 ISSN 1662-4548.
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WRAP-Improved-interpretability-of-brain-behavior-CCA-with-Domain-driven-Dimension-Reduction-Liu-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (3764Kb) | Preview |
Official URL: http://dx.doi.org/10.3389/fnins.2022.851827
Abstract
Canonical Correlation Analysis (CCA) has been widely applied to study correlations between neuroimaging data and behavioral data. Practical use of CCA typically requires dimensionality reduction with, for example, Principal Components Analysis (PCA), however, this can result in CCA components that are difficult to interpret. In this paper, we introduce a Domain-driven Dimension Reduction (DDR) method, reducing the dimensionality of the original datasets and combining human knowledge of the structure of the variables studied. We apply the method to the Human Connectome Project S1200 release and compare standard PCA across all variables with DDR applied to individual classes of variables, finding that DDR-CCA results are more stable and interpretable, allowing the contribution of each class of variable to be better understood. By carefully designing the analysis pipeline and cross-validating the results, we offer more insights into the interpretation of CCA applied to brain-behavior data.
| Item Type: | Journal Article | |||||||||||||||||||||
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| Subjects: | R Medicine > RC Internal medicine | |||||||||||||||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics | |||||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Brain -- Imaging, Brain -- Magnetic resonance imaging, Image processing -- Digital techniques, Canonical correlation (Statistics) | |||||||||||||||||||||
| Journal or Publication Title: | Frontiers in Neuroscience | |||||||||||||||||||||
| Publisher: | Frontiers Media S.A | |||||||||||||||||||||
| ISSN: | 1662-4548 | |||||||||||||||||||||
| Official Date: | 23 June 2022 | |||||||||||||||||||||
| Dates: |
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| Volume: | 16 | |||||||||||||||||||||
| Number of Pages: | 15 | |||||||||||||||||||||
| Article Number: | 851827 | |||||||||||||||||||||
| DOI: | 10.3389/fnins.2022.851827 | |||||||||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||||||||
| Publication Status: | Published | |||||||||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||||||||
| Date of first compliant deposit: | 25 October 2022 | |||||||||||||||||||||
| Date of first compliant Open Access: | 27 October 2022 | |||||||||||||||||||||
| RIOXX Funder/Project Grant: |
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