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比较来自功能和结构神经影像数据的个体内和个体间相关性脑连接。

Comparing intra- and inter-individual correlational brain connectivity from functional and structural neuroimaging data.

作者信息

Di Xin, Biswal Bharat B

机构信息

Department of Biomedical Engineering, New Jersey Institute of Technology, 604 Fenster Hall, University Height, Newark, NJ, 07102, USA.

Department of Biomedical Engineering, New Jersey Institute of Technology, 607 Fenster Hall, University Height, Newark, NJ, 07102, USA.

出版信息

Brain Struct Funct. 2025 Jul 4;230(6):113. doi: 10.1007/s00429-025-02972-y.

Abstract

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 (18 F) 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.

摘要

从个体间相关性推断大脑连接性已应用于各种神经成像模式,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大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大脑连接模式。此外,我们比较了个体内相关性与个体间相关性,以更好地理解它们之间的关系。在第一个数据集中,包含了对一个个体15年的重复扫描,我们发现静息态区域同质性(ReHo)和结构MRI的灰质体积(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)比结构测量表现出与静息态连接性更高的相似性。这项研究表明,控制各种因素可以提高大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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/565b/12227353/3bd18cd32a15/429_2025_2972_Fig1_HTML.jpg

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