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11 April 2019 Distance canonical correlation analysis with application to an imaging-genetic study
Wenxing Hu, Aiying Zhang, Biao Cai, Vince Calhoun, Yu-Ping Wang
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Journal of Medical Imaging, Vol. 6, Issue 2, 026501 (April 2019). https://doi.org/10.1117/1.JMI.6.2.026501
Abstract
Distance correlation is a measure that can detect both linear and nonlinear associations. However, applying distance correlation to imaging genetic studies often needs multiple testing correction due to the large number of multiple inferences. As a result, the sensitivity of its detection may be low. We propose a new model, distance canonical correlation analysis (DCCA), which overcomes this problem by searching a combination of features with the highest distance correlation. This is achieved by constructing a distance kernel function followed by solving a subsequent optimization problem. The ability to detect both linear and nonlinear associations makes DCCA suitable for analyzing complex multimodal and imaging-genetic associations. When applied to a brain imaging-genetic study from the Philadelphia Neurodevelopmental Cohort (PNC), DCCA detected several mental disorder-related gene pathways and brain networks. Experiments on brain connectivity found that the default mode network had strong nonlinear connections with other brain networks. When applied to the study of age effects, DCCA revealed that the connections of brain networks were relatively weak in younger groups but became stronger at older age stages. It indicates that adolescence is a vital stage for brain development. DCCA thus reveals a number of interesting findings and demonstrates a powerful new approach for analyzing multimodal brain imaging data.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Wenxing Hu, Aiying Zhang, Biao Cai, Vince Calhoun, and Yu-Ping Wang "Distance canonical correlation analysis with application to an imaging-genetic study," Journal of Medical Imaging 6(2), 026501 (11 April 2019). https://doi.org/10.1117/1.JMI.6.2.026501
Received: 2 October 2018; Accepted: 22 March 2019; Published: 11 April 2019
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Cited by 10 scholarly publications.
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KEYWORDS
Brain
Simulation of CCA and DLA aggregates
Neuroimaging
Canonical correlation analysis
Genetics
Functional magnetic resonance imaging
Brain imaging
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