大数据,小偏差:协调扩散核磁共振结构连接体以减轻数据集成中与站点相关的偏差

IF 3.5 2区 医学 Q1 NEUROIMAGING
Rui Sherry Shen, Drew Parker, Andrew An Chen, Benjamin E. Yerys, Birkan Tunç, Timothy P. L. Roberts, Russell T. Shinohara, Ragini Verma
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引用次数: 0

摘要

基于弥散核磁共振的结构连接体越来越多地用于研究与各种疾病相关的大脑连接变化。然而,个体研究的小样本量,以及高度异质性的疾病相关表现,强调了跨多个研究汇集数据集的必要性,以便能够识别与这些疾病相关的连贯和可推广的连接模式。然而,由于扫描仪硬件或采集协议的差异,合并数据集会引入与站点相关的差异。这些差异突出了统计数据协调的必要性,以减轻与结构连接体有关的地点影响,同时保留与参与者人口统计和疾病相关的生物信息。虽然存在几种范式来协调正态分布的神经成像测量,但本文代表了首次努力建立专门为结构连接体量身定制的协调框架。我们对各种统计协调方法进行了彻底的研究,使它们适应结构连接体的独特分布特征和基于图的属性。通过严格的评估,我们表明基于伽马分布模型的MATCH算法在结构连接体建模方面始终优于现有方法,能够在基于边缘和下游图分析中有效去除与位点相关的偏差,同时保留生物可变性。两个现实世界的应用进一步突出了我们的协调框架在解决多位点结构连接体分析中的挑战方面的实用性。具体来说,与MATCH的协调增强了基于连接体的机器学习预测器对新数据集的通用性,并提高了检测组水平差异的统计能力。我们的工作为协调多位点结构连接体提供了重要指导,为团队科学和大数据时代通过合作研究获得更有力的发现铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Big Data, Small Bias: Harmonizing Diffusion MRI-Based Structural Connectomes to Mitigate Site-Related Bias in Data Integration

Big Data, Small Bias: Harmonizing Diffusion MRI-Based Structural Connectomes to Mitigate Site-Related Bias in Data Integration

Diffusion MRI-based structural connectomes are increasingly used to investigate brain connectivity changes associated with various disorders. However, small sample sizes in individual studies, along with highly heterogeneous disorder-related manifestations, underscore the need to pool datasets across multiple studies to be able to identify coherent and generalizable connectivity patterns linked to these disorders. Yet, combining datasets introduces site-related differences due to variations in scanner hardware or acquisition protocols. These differences highlight the necessity for statistical data harmonization to mitigate site-related effects on structural connectomes while preserving the biological information associated with participant demographics and the disorders. While several paradigms exist for harmonizing normally distributed neuroimaging measures, this paper represents the first effort to establish a harmonization framework specifically tailored for the structural connectome. We conduct a thorough investigation of various statistical harmonization methods, adapting them to accommodate the unique distributional characteristics and graph-based properties of structural connectomes. Through rigorous evaluation, we show that our MATCH algorithm, based on the gamma-distributed model, consistently outperforms existing approaches in modeling structural connectomes, enabling the effective removal of site-related biases in both edge-based and downstream graph analyses while preserving biological variability. Two real-world applications further highlight the utility of our harmonization framework in addressing challenges in multi-site structural connectome analysis. Specifically, harmonization with MATCH enhances the generalizability of connectome-based machine learning predictors to new datasets and increases statistical power for detecting group-level differences. Our work provides essential guidelines for harmonizing multi-site structural connectomes, paving the way for more robust discoveries through collaborative research in the era of team science and big data.

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来源期刊
Human Brain Mapping
Human Brain Mapping 医学-核医学
CiteScore
8.30
自引率
6.20%
发文量
401
审稿时长
3-6 weeks
期刊介绍: Human Brain Mapping publishes peer-reviewed basic, clinical, technical, and theoretical research in the interdisciplinary and rapidly expanding field of human brain mapping. The journal features research derived from non-invasive brain imaging modalities used to explore the spatial and temporal organization of the neural systems supporting human behavior. Imaging modalities of interest include positron emission tomography, event-related potentials, electro-and magnetoencephalography, magnetic resonance imaging, and single-photon emission tomography. Brain mapping research in both normal and clinical populations is encouraged. Article formats include Research Articles, Review Articles, Clinical Case Studies, and Technique, as well as Technological Developments, Theoretical Articles, and Synthetic Reviews. Technical advances, such as novel brain imaging methods, analyses for detecting or localizing neural activity, synergistic uses of multiple imaging modalities, and strategies for the design of behavioral paradigms and neural-systems modeling are of particular interest. The journal endorses the propagation of methodological standards and encourages database development in the field of human brain mapping.
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