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  Comparison of variants of canonical correlation analysis and partial least squares for combined analysis of MRI and genetic data

Grellmann, C., Bitzer, S., Neumann, J., Westlye, L., Andreassen, O., Villringer, A., et al. (2015). Comparison of variants of canonical correlation analysis and partial least squares for combined analysis of MRI and genetic data. NeuroImage, 107, 289-310. doi:10.1016/j.neuroimage.2014.12.025.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0024-52B1-A Version Permalink: http://hdl.handle.net/21.11116/0000-0003-14B6-9
Genre: Journal Article

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 Creators:
Grellmann, Claudia1, 2, Author              
Bitzer, Sebastian1, Author              
Neumann, Jane1, 2, Author              
Westlye, Lars3, 4, Author
Andreassen, Ole3, Author
Villringer, Arno1, 2, 5, 6, Author              
Horstmann, Annette1, 2, Author              
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2Integrated Research and Treatment Center Adiposity Diseases, University of Leipzig, Germany, ou_persistent22              
3NORMENT, KG Jebsen Centre for Psychosis Research, University of Oslo, Norway, ou_persistent22              
4Department of Psychology, University of Oslo, Norway, ou_persistent22              
5Clinic for Cognitive Neurology, University of Leipzig, Germany, ou_persistent22              
6Berlin School of Mind and Brain, Humboldt University Berlin, Germany, ou_persistent22              

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Free keywords: Canonical correlation analysis; Partial least squares correlation; Functional magnetic resonance imaging; Single nucleotide polymorphisms
 Abstract: The standard analysis approach in neuroimaging genetics studies is the mass-univariate linear modeling (MULM) approach. From a statistical view, however, this approach is disadvantageous, as it is computationally intensive, cannot account for complex multivariate relationships, and has to be corrected for multiple testing. In contrast, multivariate methods offer the opportunity to include combined information from multiple variants to discover meaningful associations between genetic and brain imaging data. We assessed three multivariate techniques, partial least squares correlation (PLSC), sparse canonical correlation analysis (sparse CCA) and Bayesian inter-battery factor analysis (Bayesian IBFA), with respect to their ability to detect multivariate genotype-phenotype associations. Our goal was to systematically compare these three approaches with respect to their performance and to assess their suitability for high-dimensional and multi-collinearly dependent data as is the case in neuroimaging genetics studies. In a series of simulations using both linearly independent and multi-collinear data, we show that sparse CCA and PLSC are suitable even for very high-dimensional collinear imaging data sets. Among those two, the predictive power was higher for sparse CCA when voxel numbers were below 400 times sample size and candidate SNPs were considered. Accordingly, we recommend Sparse CCA for candidate phenotype, candidate SNP studies. When voxel numbers exceeded 500 times sample size, the predictive power was the highest for PLSC. Therefore, PLSC can be considered a promising technique for multivariate modeling of high-dimensional brain-SNP-associations. In contrast, Bayesian IBFA cannot be recommended, since additional post-processing steps were necessary to detect causal relations. To verify the applicability of sparse CCA and PLSC, we applied them to an experimental imaging genetics data set provided for us. Most importantly, application of both methods replicated the findings of this data set.

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Language(s): eng - English
 Dates: 2014-12-092014-12-172015-02-15
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2014.12.025
PMID: 25527238
Other: Epub 2014
 Degree: -

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Title: NeuroImage
Source Genre: Journal
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Pages: - Volume / Issue: 107 Sequence Number: - Start / End Page: 289 - 310 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166