Osmanlıoğlu Yusuf, Alappatt Jacob A, Parker Drew, Verma Ragini
Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, United States of America.
J Neural Eng. 2020 Jul 13;17(4):045004. doi: 10.1088/1741-2552/ab947b.
Connectomics, the study of brain connectivity, has become an indispensable tool in neuroscientific research as it provides insights into brain organization. Connectomes are generated using different modalities such as diffusion MRI to capture structural organization of the brain or functional MRI to elaborate brain's functional organization. Understanding links between structural and functional organizations is crucial in explaining how observed behavior emerges from the underlying neurobiological mechanisms. Many studies have investigated how these two organizations relate to each other; however, we still lack a comparative understanding on how much variation should be expected in the two modalities, both between people and within a single person across scans.
In this study, we systematically analyzed the consistency of connectomes, that is the similarity between connectomes in terms of individual connections between brain regions and in terms of overall network topology. We present a comprehensive study of consistency in connectomes for a single subject examined longitudinally and across a large cohort of subjects cross-sectionally, in structure and function separately. Within structural connectomes, we compared connectomes generated by different tracking algorithms, parcellations, edge weighting schemes, and edge pruning techniques. In functional connectomes, we compared full, positive, and negative connectivity separately along with thresholding of weak edges. We evaluated consistency using correlation (incorporating information at the level of individual edges) and graph matching accuracy (evaluating connectivity at the level of network topology). We also examined the consistency of connectomes that are generated using different communication schemes.
Our results demonstrate varying degrees of consistency for the two modalities, with structural connectomes showing higher consistency than functional connectomes. Moreover, we observed a wide variation in consistency depending on how connectomes are generated.
Our study sets a reference point for consistency of connectome types, which is especially important for structure-function coupling studies in evaluating mismatches between modalities.
连接组学作为对大脑连接性的研究，已成为神经科学研究中不可或缺的工具，因为它能为大脑组织提供见解。连接组通过不同的方式生成，如利用扩散磁共振成像（dMRI）来捕捉大脑的结构组织，或利用功能磁共振成像（fMRI）来阐述大脑的功能组织。理解结构和功能组织之间的联系对于解释观察到的行为如何从潜在的神经生物学机制中产生至关重要。许多研究已经探究了这两种组织是如何相互关联的；然而，我们仍然缺乏一种比较性的理解，即在人与人之间以及同一个人在多次扫描中，这两种方式应该预期有多大的变异性。
在本研究中，我们系统地分析了连接组的一致性，即连接组在大脑区域之间的个体连接以及整体网络拓扑方面的相似性。我们分别对纵向研究的单个受试者以及横截面研究的大量受试者队列的结构和功能连接组的一致性进行了全面研究。在结构连接组中，我们比较了由不同追踪算法、脑区划分、边加权方案和边修剪技术生成的连接组。在功能连接组中，我们分别比较了全连接、正连接和负连接以及弱边阈值处理。我们使用相关性（纳入个体边层面的信息）和图匹配准确率（在网络拓扑层面评估连接性）来评估一致性。我们还研究了使用不同通信方案生成的连接组的一致性。
我们的结果表明这两种方式具有不同程度的一致性，结构连接组的一致性高于功能连接组。此外，我们观察到一致性存在很大差异，这取决于连接组的生成方式。
我们的研究为连接组类型的一致性设定了一个参考点，这对于评估方式之间不匹配的结构 - 功能耦合研究尤为重要。
