TY - JOUR
T1 - 3D spatially-adaptive canonical correlation analysis
T2 - Local and global methods
AU - Yang, Zhengshi
AU - Zhuang, Xiaowei
AU - Sreenivasan, Karthik
AU - Mishra, Virendra
AU - Curran, Tim
AU - Byrd, Richard
AU - Nandy, Rajesh
AU - Cordes, Dietmar
N1 - Funding Information:
This research project was supported by the NIH (grant number 1R01EB014284 and COBRE grant 1P20GM109025 ).
Publisher Copyright:
© 2017 Elsevier Inc.
PY - 2018/4/1
Y1 - 2018/4/1
N2 - Local spatially-adaptive canonical correlation analysis (local CCA) with spatial constraints has been introduced to fMRI multivariate analysis for improved modeling of activation patterns. However, current algorithms require complicated spatial constraints that have only been applied to 2D local neighborhoods because the computational time would be exponentially increased if the same method is applied to 3D spatial neighborhoods. In this study, an efficient and accurate line search sequential quadratic programming (SQP) algorithm has been developed to efficiently solve the 3D local CCA problem with spatial constraints. In addition, a spatially-adaptive kernel CCA (KCCA) method is proposed to increase accuracy of fMRI activation maps. With oriented 3D spatial filters anisotropic shapes can be estimated during the KCCA analysis of fMRI time courses. These filters are orientation-adaptive leading to rotational invariance to better match arbitrary oriented fMRI activation patterns, resulting in improved sensitivity of activation detection while significantly reducing spatial blurring artifacts. The kernel method in its basic form does not require any spatial constraints and analyzes the whole-brain fMRI time series to construct an activation map. Finally, we have developed a penalized kernel CCA model that involves spatial low-pass filter constraints to increase the specificity of the method. The kernel CCA methods are compared with the standard univariate method and with two different local CCA methods that were solved by the SQP algorithm. Results show that SQP is the most efficient algorithm to solve the local constrained CCA problem, and the proposed kernel CCA methods outperformed univariate and local CCA methods in detecting activations for both simulated and real fMRI episodic memory data.
AB - Local spatially-adaptive canonical correlation analysis (local CCA) with spatial constraints has been introduced to fMRI multivariate analysis for improved modeling of activation patterns. However, current algorithms require complicated spatial constraints that have only been applied to 2D local neighborhoods because the computational time would be exponentially increased if the same method is applied to 3D spatial neighborhoods. In this study, an efficient and accurate line search sequential quadratic programming (SQP) algorithm has been developed to efficiently solve the 3D local CCA problem with spatial constraints. In addition, a spatially-adaptive kernel CCA (KCCA) method is proposed to increase accuracy of fMRI activation maps. With oriented 3D spatial filters anisotropic shapes can be estimated during the KCCA analysis of fMRI time courses. These filters are orientation-adaptive leading to rotational invariance to better match arbitrary oriented fMRI activation patterns, resulting in improved sensitivity of activation detection while significantly reducing spatial blurring artifacts. The kernel method in its basic form does not require any spatial constraints and analyzes the whole-brain fMRI time series to construct an activation map. Finally, we have developed a penalized kernel CCA model that involves spatial low-pass filter constraints to increase the specificity of the method. The kernel CCA methods are compared with the standard univariate method and with two different local CCA methods that were solved by the SQP algorithm. Results show that SQP is the most efficient algorithm to solve the local constrained CCA problem, and the proposed kernel CCA methods outperformed univariate and local CCA methods in detecting activations for both simulated and real fMRI episodic memory data.
KW - Constrained canonical correlation analysis
KW - Kernel canonical correlation analysis
KW - Multivariate analysis
KW - Orientation filters
KW - Spatial filtering
KW - fMRI
UR - http://www.scopus.com/inward/record.url?scp=85038845795&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2017.12.025
DO - 10.1016/j.neuroimage.2017.12.025
M3 - Article
C2 - 29248697
AN - SCOPUS:85038845795
SN - 1053-8119
VL - 169
SP - 240
EP - 255
JO - NeuroImage
JF - NeuroImage
ER -