Evidence map›Paper›PMID 39539980›Full record

ArticleCognitive neurodynamics2024

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

Xiaoyu Zhao, Kewei Chen, Hailing Wang, Yufei Gao, Xiangmin Ji, Yanping Li

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Article in Cognitive neurodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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Authors and funding

6 authors.

Xiaoyu ZhaoDepartment of Information Engineering, Ordos Institute of Technology, Ordos, China.
Kewei ChenBanner Alzheimer's Institute, Phoenix, AZ USA.
Hailing WangSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China.
Yufei GaoSchool of Software, Zhengzhou University, Zhengzhou, China.
Xiangmin JiSchool of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China.
Yanping LiDepartment of Information Engineering, Ordos Institute of Technology, Ordos, China.

Funding

Research Education ComponentP30AG072980 · NIA · BANNER HEALTH · PI ALIREZA ATRI · 2021 to 2026
$24.9M
NIA NIH HHS P30 AG072980
6 · The paper itself

Abstract

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.

Indexed as

Covariance structure–function networkFunctional magnetic resonance imagingPartial least squares correlationStructural magnetic resonance imagingStructure–function relationship

Identifiers

PMID39539980
PMCPMC11555187

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