Di Xin, Biswal Bharat B
Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
bioRxiv. 2025 Feb 28:2024.12.03.626661. doi: 10.1101/2024.12.03.626661.
Inferring brain connectivity from inter-individual correlations has been applied across various neuroimaging modalities, including positron emission tomography (PET) and MRI. The variability underlying these inter-individual correlations is generally attributed to factors such as genetics, life experiences, and long-term influences like aging. This study leveraged two unique longitudinal datasets to examine intra-individual correlations of structural and functional brain measures across an extended time span. By focusing on intra-individual correlations, we aimed to minimize individual differences and investigate how aging and state-like effects contribute to brain connectivity patterns. Additionally, we compared intra-individual correlations with inter-individual correlations to better understand their relationship. In the first dataset, which included repeated scans from a single individual over 15 years, we found that intra-individual correlations in both regional homogeneity (ReHo) during resting-state and gray matter volumes (GMV) from structural MRI closely resembled resting-state functional connectivity. However, ReHo correlations were primarily driven by state-like variability, whereas GMV correlations were mainly influenced by aging. The second dataset, comprising multiple participants with longitudinal Fludeoxyglucose (18F) FDG-PET and MRI scans, replicated these findings. Both intra- and inter-individual correlations were strongly associated with resting-state functional connectivity, with functional measures (i.e., ReHo and FDG-PET) exhibiting greater similarity to resting-state connectivity than structural measures. This study demonstrated that controlling for various factors can enhance the interpretability of brain correlation structures. While inter- and intra-individual correlation patterns showed similarities, accounting for additional variables may improve our understanding of inter-individual connectivity measures.
从个体间相关性推断脑连接性已应用于各种神经成像模态，包括正电子发射断层扫描（PET）和磁共振成像（MRI）。这些个体间相关性背后的变异性通常归因于遗传、生活经历以及衰老等长期影响因素。本研究利用两个独特的纵向数据集，在较长时间跨度内检查脑结构和功能测量的个体内相关性。通过关注个体内相关性，我们旨在最小化个体差异，并研究衰老和类似状态的效应如何影响脑连接模式。此外，我们比较了个体内相关性和个体间相关性，以更好地理解它们之间的关系。在第一个数据集中，包含对一个个体15年的重复扫描，我们发现静息态区域一致性（ReHo）和结构MRI灰质体积（GMV）的个体内相关性与静息态功能连接性非常相似。然而，ReHo相关性主要由类似状态的变异性驱动，而GMV相关性主要受衰老影响。第二个数据集包括多名参与者的纵向氟脱氧葡萄糖（18F）FDG-PET和MRI扫描，重复了这些发现。个体内和个体间相关性均与静息态功能连接性密切相关，功能测量（即ReHo和FDG-PET）与静息态连接性的相似性高于结构测量。这项研究表明，控制各种因素可以提高脑相关结构的可解释性。虽然个体间和个体内相关模式显示出相似性，但考虑额外变量可能会增进我们对个体间连接性测量的理解。
