Quantifying the Link between Anatomical Connectivity, Gray Matter Volume and Regional Cerebral Blood Flow: An Integrative MRI Study

BackgroundIn the graph theoretical analysis of anatomical brain connectivity, the white matter connections between regions of the brain are identified and serve as basis for the assessment of regional connectivity profiles, for example, to locate the hubs of the brain. But regions of the brain can be characterised further with respect to their gray matter volume or resting state perfusion. Local anatomical connectivity, gray matter volume and perfusion are traits of each brain region that are likely to be interdependent, however, particular patterns of systematic covariation have not yet been identified. Methodology/Principal FindingsWe quantified the covariation of these traits by conducting an integrative MRI study on 23 subjects, utilising a combination of Diffusion Tensor Imaging, Arterial Spin Labeling and anatomical imaging. Based on our hypothesis that local connectivity, gray matter volume and perfusion are linked, we correlated these measures and particularly isolated the covariation of connectivity and perfusion by statistically controlling for gray matter volume. We found significant levels of covariation on the group- and regionwise level, particularly in regions of the Default Brain Mode Network. Conclusions/SignificanceConnectivity and perfusion are systematically linked throughout a number of brain regions, thus we discuss these results as a starting point for further research on the role of homology in the formation of functional connectivity networks and on how structure/function relationships can manifest in the form of such trait interdependency.背景 在脑解剖连接的图论分析中，研究人员首先识别脑区间的白质连接，并以此为基础评估区域连接特征，例如定位脑枢纽节点。此外，脑区还可通过灰质体积或静息态灌注进一步表征。局部解剖连接、灰质体积与灌注均为每个脑区的固有特征，且三者大概率存在相互依赖关系，但目前尚未明确其系统性协变的特定模式。 研究方法与主要结果 我们针对23名受试者开展整合磁共振成像（Magnetic Resonance Imaging, MRI）研究，联合使用弥散张量成像（Diffusion Tensor Imaging）、动脉自旋标记（Arterial Spin Labeling）与解剖成像技术，对上述三项特征的协变关系进行量化分析。基于“局部连接、灰质体积与灌注存在关联”的研究假说，我们对这三项指标进行相关性分析，并通过统计学控制灰质体积的影响，分离出连接特征与灌注之间的协变关系。结果显示，我们在组水平与脑区水平均发现了显著的协变效应，尤其在默认模式脑网络（Default Brain Mode Network）的脑区中表现最为突出。 结论与意义 多个脑区的连接特征与灌注均存在系统性关联。据此，我们将本研究结果作为后续研究的起点，进一步探讨同源性在功能连接网络形成中的作用，以及结构-功能关系如何以这类特征相互依赖的形式得以体现。