Shen Rui Sherry, Parker Drew, Chen Andrew An, Yerys Benjamin E, Tunç Birkan, Roberts Timothy P L, Shinohara Russell T, Verma Ragini
Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Diffusion & Connectomics in Precision Healthcare Research, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Hum Brain Mapp. 2025 Jun 15;46(9):e70256. doi: 10.1002/hbm.70256.
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.
基于扩散磁共振成像的结构连接组越来越多地用于研究与各种疾病相关的脑连接变化。然而，个体研究中的样本量较小，以及与疾病相关的表现高度异质性，凸显了整合多个研究数据集的必要性，以便能够识别与这些疾病相关的连贯且可推广的连接模式。然而，合并数据集会由于扫描仪硬件或采集协议的差异而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，以减轻与站点相关的对结构连接组的影响，同时保留与参与者人口统计学和疾病相关的生物学信息。虽然存在几种用于协调正态分布神经成像测量的范式，但本文首次尝试建立一个专门针对结构连接组量身定制的协调框架。我们对各种统计协调方法进行了深入研究，对其进行调整以适应结构连接组独特的分布特征和基于图的属性。通过严格评估，我们表明基于伽马分布模型的MATCH算法在对结构连接组进行建模时始终优于现有方法，能够在基于边的和下游的图分析中有效消除与站点相关的偏差，同时保留生物学变异性。两个实际应用进一步凸显了我们的协调框架在应对多站点结构连接组分析挑战方面的效用。具体而言，使用MATCH进行协调可提高基于连接组的机器学习预测器对新数据集的可推广性，并增加检测组间差异的统计功效。我们的工作为协调多站点结构连接组提供了重要指导方针，为在团队科学和大数据时代通过合作研究实现更可靠的发现铺平了道路。
