Zhao Xiaoyu, Chen Kewei, Wang Hailing, Gao Yufei, Ji Xiangmin, Li Yanping
Department of Information Engineering, Ordos Institute of Technology, Ordos, China.
School of Artificial Intelligence, Beijing Normal University, Beijing, China.
Cogn Neurodyn. 2024 Jun;18(3):813-827. doi: 10.1007/s11571-023-09941-3. Epub 2023 Feb 18.
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.
脑结构-功能关系对于人类大脑在正常或患病状态下的工作方式至关重要。当使用具有时间动态性的三维磁共振成像(MRI)功能数据集和静态的结构MRI来探索这种关系时,具有挑战性。偏最小二乘相关(PLSC)是探索联合空间和时间关系的经典方法之一。PLSC的目标是通过在结构和功能数据集中的每个数据集进行线性体素级组合来识别共变模式,以最大化协方差。然而,现有的PLSC无法充分处理结构和功能数据集之间不匹配的时间维度。我们提出了一种新的PLSC替代变体,称为受试者内、体素级和恒定块PLSC,以解决这个问题。为了验证我们的方法,我们在模拟数据中使用了两个具有弱关系和强关系的数据集。此外,基于从结构MRI得出的灰质体积中心和来自静息态功能磁共振成像的全脑体素级测量,对16至85岁健康受试者的衰老效应进行了真实脑数据分析。我们的结果表明,我们的恒定块PLSC可以检测到弱的结构-功能关系,并且对噪声具有更好的鲁棒性。事实上,它分别为模拟数据充分挖掘出了显著且更准确的潜在变量的真实模拟数量,以及为真实数据挖掘出了更有意义的潜在变量,协方差分别从16.19%提高到41.48%(模拟数据)和从13.29%提高到53.68%(真实数据)。更有趣的是在真实数据分析中,我们的方法在功能和结构上同时识别出了诸如默认模式、感觉运动、听觉和背侧注意网络等著名的脑网络,这意味着我们从灰质体积得出的中心是脑功能的基础,支持多种功能。恒定块PLSC是分析脑结构-功能关系的一种可行工具。