Wang Maxwell B, Owen Julia P, Mukherjee Pratik, Raj Ashish
Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, United States of America.
Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, California, United States of America.
PLoS Comput Biol. 2017 Jun 22;13(6):e1005550. doi: 10.1371/journal.pcbi.1005550. eCollection 2017 Jun.
Recent research has demonstrated the use of the structural connectome as a powerful tool to characterize the network architecture of the brain and potentially generate biomarkers for neurologic and psychiatric disorders. In particular, the anatomic embedding of the edges of the cerebral graph have been postulated to elucidate the relative importance of white matter tracts to the overall network connectivity, explaining the varying effects of localized white matter pathology on cognition and behavior. Here, we demonstrate the use of a linear diffusion model to quantify the impact of these perturbations on brain connectivity. We show that the eigenmodes governing the dynamics of this model are strongly conserved between healthy subjects regardless of cortical and sub-cortical parcellations, but show significant, interpretable deviations in improperly developed brains. More specifically, we investigated the effect of agenesis of the corpus callosum (AgCC), one of the most common brain malformations to identify differences in the effect of virtual corpus callosotomies and the neurodevelopmental disorder itself. These findings, including the strong correspondence between regions of highest importance from graph eigenmodes of network diffusion and nexus regions of white matter from edge density imaging, show converging evidence toward understanding the relationship between white matter anatomy and the structural connectome.
最近的研究表明，结构连接组作为一种强大的工具，可用于表征大脑的网络架构，并有可能生成神经和精神疾病的生物标志物。特别是，大脑图谱边缘的解剖学嵌入被认为可以阐明白质束对整体网络连通性的相对重要性，解释局部白质病变对认知和行为的不同影响。在此，我们展示了使用线性扩散模型来量化这些扰动对大脑连通性的影响。我们表明，无论皮质和皮质下的分割如何，控制该模型动态的本征模式在健康受试者之间都得到了强烈的保留，但在发育不当的大脑中显示出显著的、可解释的偏差。更具体地说，我们研究了胼胝体发育不全（AgCC）的影响，胼胝体发育不全是最常见的脑畸形之一，以确定虚拟胼胝体切开术的效果与神经发育障碍本身之间的差异。这些发现，包括网络扩散的图谱本征模式中最重要区域与边缘密度成像中白质连接区域之间的强烈对应关系，显示了在理解白质解剖结构与结构连接组之间关系方面的趋同证据。
