López-López Narciso, Vázquez Andrea, Houenou Josselin, Poupon Cyril, Mangin Jean-François, Ladra Susana, Guevara Pamela
Faculty of Engineering, Universidad de Concepción, Concepción, Chile.
Universidade da Coruña, CITIC, Department of Computer Science and Information Technologies, A Coruña, Spain.
Front Neuroinform. 2020 Sep 10;14:32. doi: 10.3389/fninf.2020.00032. eCollection 2020.
In this article, we present a hybrid method to create fine-grained parcellations of the cortical surface, from a coarse-grained parcellation according to an anatomical atlas, based on cortico-cortical connectivity. The connectivity information is obtained from segmented superficial and deep white matter bundles, according to bundle atlases, instead of the whole tractography. Thus, a direct matching between the fiber bundles and the cortical regions is obtained, avoiding the problem of finding the correspondence of the cortical parcels among subjects. Generating parcels from segmented fiber bundles can provide a good representation of the human brain connectome since they are based on bundle atlases that contain the most reproducible short and long connections found on a population of subjects. The method first processes the tractography of each subject and extracts the bundles of the atlas, based on a segmentation algorithm. Next, the intersection between the fiber bundles and the cortical mesh is calculated, to define the initial and final intersection points of each fiber. A fiber filtering is then applied to eliminate misclassified fibers, based on the anatomical definition of each bundle and the labels of Desikan-Killiany anatomical parcellation. A parcellation algorithm is then performed to create a subdivision of the anatomical regions of the cortex, which is reproducible across subjects. This step resolves the overlapping of the fiber bundle extremities over the cortical mesh within each anatomical region. For the analysis, the density of the connections and the degree of overlapping, is considered and represented with a graph. One of our parcellations, an atlas composed of 160 parcels, achieves a reproducibility across subjects of ≈0.74, based on the average Dice's coefficient between subject's connectivity matrices, rather than ≈0.73 obtained for a macro anatomical parcellation of 150 parcels. Moreover, we compared two of our parcellations with state-of-the-art atlases, finding a degree of similarity with dMRI, functional, anatomical, and multi-modal atlases. The higher similarity was found for our parcellation composed of 185 sub-parcels with another parcellation based on dMRI data from the same database, but created with a different approach, leading to 130 parcels in common based on a Dice's coefficient ≥0.5.
在本文中，我们提出了一种混合方法，根据解剖图谱从粗粒度的脑皮质表面分割开始，基于皮质-皮质连接性创建细粒度的脑皮质表面分割。连接性信息是根据束图谱从分割后的浅部和深部白质束中获得的，而不是从整个纤维束成像中获取。因此，实现了纤维束与皮质区域之间的直接匹配，避免了在不同个体间寻找皮质分割块对应关系的问题。从分割后的纤维束生成分割块能够很好地呈现人类大脑连接组，因为它们基于包含在一组个体中发现的最具可重复性的短连接和长连接的束图谱。该方法首先处理每个个体的纤维束成像，并基于一种分割算法提取图谱中的束。接下来，计算纤维束与皮质网格之间的交集，以定义每条纤维的起始和终止交点。然后基于每个束的解剖学定义和Desikan-Killiany解剖分割的标签应用纤维过滤，以消除误分类的纤维。接着执行一个分割算法来创建皮质解剖区域的细分，该细分在不同个体间具有可重复性。这一步解决了每个解剖区域内纤维束末端在皮质网格上的重叠问题。在分析中，考虑连接的密度和重叠程度并用一个图来表示。我们的一个分割结果，即一个由160个分割块组成的图谱，基于个体连接性矩阵之间的平均骰子系数，在不同个体间的可重复性约为0.74，而一个150个分割块的宏观解剖分割的可重复性约为0.73。此外，我们将我们的两个分割结果与现有最佳图谱进行了比较，发现与扩散磁共振成像（dMRI）、功能、解剖和多模态图谱有一定程度的相似性。在我们由185个子分割块组成的分割结果与基于来自同一数据库的dMRI数据但采用不同方法创建的另一个分割结果之间发现了更高的相似性，基于骰子系数≥0.5，两者共有130个分割块。
