Multivariate group-level analysis for task fMRI data with canonical correlation analysis

• Published in: NeuroImage (Orlando, Fla.) Vol. 194; pp. 25 - 41
• Main Authors: Zhuang, Xiaowei, Yang, Zhengshi, Sreenivasan, Karthik R., Mishra, Virendra R., Curran, Tim, Nandy, Rajesh, Cordes, Dietmar
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2019; Elsevier Limited
• Subjects: Algorithms; Brain - physiology; Brain mapping; Brain Mapping - methods; Cognitive ability; Constrained multivariate method; Correlation analysis; Episodic memory; Female; Functional magnetic resonance imaging; Functional magnetic resonance imaging (fMRI); Group-level analysis; Hippocampus; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Male; Maximum log likelihood; Memory, Episodic; Middle Aged; Mimicry; Models, Neurological; Multivariate analysis; Neuroimaging; Numerical optimization; Optimization techniques; Temporal lobe; Constrained multivariate method; Functional magnetic resonance imaging (fMRI); Numerical optimization; Maximum log likelihood; Group-level analysis
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2019.03.030

Task-based functional Magnetic Resonance Imaging (fMRI) has been widely used to determine population-based brain activations for cognitive tasks. Popular group-level analysis in fMRI is based on the general linear model and constitutes a univariate method. However, univariate methods are known to suffer from low sensitivity for a given specificity because the spatial covariance structure at each voxel is not taken entirely into account. In this study, a spatially constrained local multivariate model is introduced for group-level analysis to improve sensitivity at a given specificity for activation detection. The proposed model is formulated in terms of a multivariate constrained optimization problem based on the maximum log likelihood method and solved efficiently with numerical optimization techniques. Both simulated data mimicking real fMRI time series at multiple noise fractions and real fMRI episodic memory data have been used to evaluate the performance of the proposed method. For simulated data, the area under the receiver operating characteristic curves in detecting group activations increases for the subject and group level multivariate method by 20%, as compared to the univariate method. Results from real fMRI data indicate a significant increase in group-level activation detection, particularly in hippocampus, para-hippocampal area and nearby medial temporal lobe regions with the proposed method. •A spatially constrained multivariate model for fMRI group-level analysis is introduced and incorporates local neighboring voxels with optimal spatial weights.•The proposed method is formulated in terms of a constrained optimization problem based on the maximum log likelihood method.•The proposed method demonstrate better performance for activation detection over univariate methods in simulations and real episodic memory data.