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Neuroimage. 2015 Apr 1;109:388-401. doi: 10.1016/j.neuroimage.2015.01.006. Epub 2015 Jan 12.

Characterizing nonlinear relationships in functional imaging data using eigenspace maximal information canonical correlation analysis (emiCCA).

Author information

1
The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
2
The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; Cuban Neuroscience Center, Havana, Cuba.
3
The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China. Electronic address: dyao@uestc.edu.cn.

Abstract

Many important problems in the analysis of neuroimages can be formulated as discovering the relationship between two sets of variables, a task for which linear techniques such as canonical correlation analysis (CCA) have been commonly used. However, to further explore potential nonlinear processes that might co-exist with linear ones in brain function, a more flexible method is required. Here, we propose a new unsupervised and data-driven method, termed the eigenspace maximal information canonical correlation analysis (emiCCA), which is capable of automatically capturing the linear and/or nonlinear relationships between various data sets. A simulation confirmed the superior performance of emiCCA in comparison with linear CCA and kernel CCA (a nonlinear version of CCA). An emiCCA framework for functional magnetic resonance imaging (fMRI) data processing was designed and applied to data from a real motor execution fMRI experiment. This analysis uncovered one linear (in primary motor cortex) and a few nonlinear networks (e.g., in the supplementary motor area, bilateral insula, and cerebellum). This suggests that these various task-related brain areas are part of networks that also contribute to the execution of movements of the hand. These results suggest that emiCCA is a promising technique for exploring various data.

KEYWORDS:

Eigenspace maximal information canonical correlation analysis (emiCCA); Functional magnetic resonance imaging data analysis; Motor execution; Nonlinearity; Unsupervised

[Indexed for MEDLINE]

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