Department of Neurobiology, Faculty of Life Sciences, Tel Aviv University, Ramat Aviv, 69978, Tel Aviv, Israel.
Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
Brain Struct Funct. 2019 May;224(4):1457-1467. doi: 10.1007/s00429-019-01848-2. Epub 2019 Feb 19.
The layer composition of the cerebral cortex represents a unique anatomical fingerprint of brain development, function, connectivity, and pathology. Historically, the cortical layers were investigated solely ex-vivo using histological means, but recent magnetic resonance imaging (MRI) studies suggest that T1 relaxation images can be utilized to separate the layers. Despite technological advancements in the field of high-resolution MRI, accurate estimation of whole-brain cortical laminar composition has remained limited due to partial volume effects, leaving some layers far beyond the image resolution. In this study, we offer a simple and accurate method for cortical laminar composition analysis, resolving partial volume effects and cortical curvature heterogeneity. We use a low-resolution 3T MRI echo planar imaging inversion recovery (EPI IR) scan protocol that provides fast acquisition (~ 12 min) and enables extraction of multiple T1 relaxation time components per voxel, which are assigned to types of brain tissue and utilized to extract the subvoxel composition of six T1 layers. While previous investigation of the layers required the estimation of cortical normals or smoothing of layer widths (similar to VBM), here we developed a sphere-based approach to explore the inner mesoscale architecture of the cortex. Our novel algorithm conducts spatial analysis using volumetric sampling of a system of virtual spheres dispersed throughout the entire cortical space. The methodology offers a robust and powerful framework for quantification and visualization of the cortical laminar structure on the cortical surface, providing a basis for quantitative investigation of its role in cognition, physiology and pathology.
大脑皮层的层组成代表了大脑发育、功能、连接和病理学的独特解剖学特征。历史上，皮层层仅通过组织学手段在体外进行研究，但最近的磁共振成像（MRI）研究表明，T1 弛豫图像可用于分离皮层层。尽管高分辨率 MRI 领域的技术取得了进步，但由于部分容积效应，准确估计全脑皮层层组成仍然受到限制，使得一些层远远超出了图像分辨率。在这项研究中，我们提供了一种简单而准确的皮层层组成分析方法，解决了部分容积效应和皮层曲率异质性的问题。我们使用低分辨率 3T MRI 回波平面成像反转恢复（EPI IR）扫描方案，该方案提供了快速采集（~12 分钟），并能够从每个体素中提取多个 T1 弛豫时间分量，这些分量被分配给脑组织类型，并用于提取六个 T1 层的亚体素组成。虽然之前对皮层层的研究需要估计皮层法线或平滑层宽度（类似于 VBM），但在这里，我们开发了一种基于球体的方法来探索皮层的内部中尺度结构。我们的新算法使用系统的体积采样来进行空间分析，该系统由分布在整个皮层空间中的虚拟球体组成。该方法为在皮层表面上量化和可视化皮层层结构提供了一个强大的框架，为定量研究其在认知、生理学和病理学中的作用提供了基础。
