A within-subject voxel-wise constant-block partial least squares correlation method to explore MRI-based brain structure–function relationship

• Published in: Cognitive neurodynamics Vol. 18; no. 3; pp. 813 - 827
• Main Authors: Zhao, Xiaoyu, Chen, Kewei, Wang, Hailing, Gao, Yufei, Ji, Xiangmin, Li, Yanping
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.06.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Attention; Behavior; Biochemistry; Biomedical and Life Sciences; Biomedicine; Brain; Brain mapping; Cognitive Psychology; Computer Science; Covariance; Data analysis; Datasets; Functional anatomy; Functional magnetic resonance imaging; Hubs; Least squares; Magnetic resonance imaging; Medical imaging; Methods; Neuroimaging; Neurosciences; Research Article; Sensorimotor system; Structure-function relationships; Substantia grisea; Functional magnetic resonance imaging; Covariance structure–function network; Partial least squares correlation; Structural magnetic resonance imaging; Structure–function relationship
• ISSN: 1871-4080; 1871-4099
• DOI: 10.1007/s11571-023-09941-3

The brain structure–function relationship is crucial to how the human brain works under normal or diseased conditions. Exploring such a relationship is challenging when using the 3-dimensional magnetic resonance imaging (MRI) functional dataset which is temporal dynamic and the structural MRI which is static. Partial Least Squares Correlation (PLSC) is one of the classical methods for exploring the joint spatial and temporal relationship. The goal of PLSC is to identify covarying patterns via linear voxel-wise combinations in each of the structural and functional data sets to maximize the covariance. However, existing PLSC cannot adequately deal with the unmatched temporal dimensions between structural and functional data sets. We proposed a new alternative variant of the PLSC, termed within-subject, voxel-wise, and constant-block PLSC, to address this problem. To validate our method, we used two data sets with weak and strong relationships in simulated data. Additionally, the analysis of real brain data was carried out based on gray matter volume hubs derived from sMRI and whole-brain voxel-wise measures from resting-state fMRI for aging effect based on healthy subjects aged 16–85 years. Our results showed that our constant-block PLSC can detect weak structure–function relationships and has better robustness to noise. In fact, it adequately unearthed the true simulated number of significant and more accurate latent variables for the simulated data and more meaningful LVs for the real data, with covariance improvement from 16.19 to 41.48% (simulated) and 13.29–53.68% (real data), respectively. More interestingly in the real data analysis, our method identified simultaneously the well-known brain networks such as the default mode, sensorimotor, auditory, and dorsal attention networks both functionally and structurally, implying the hubs we derived from gray matter volumes are the basis of brain function, supporting diverse functions. Constant-block PLSC is a feasible tool for analyzing the brain structure–function relationship.