Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
Med Image Anal. 2017 Dec;42:14-25. doi: 10.1016/j.media.2017.07.001. Epub 2017 Jul 15.
One distinct feature of the cerebral cortex is its convex (gyri) and concave (sulci) folding patterns. Due to the remarkable complexity and variability of gyral/sulcal shapes, it has been challenging to quantitatively model their organization patterns. Inspired by the observation that the lines of gyral crests can form a connected graph on each brain hemisphere, we propose a new representation of cortical gyri/sulci organization pattern - gyral net, which models cortical architecture from a graph perspective, starting with nodes and edges obtained from the reconstructed cortical surfaces. A novel computational framework is developed to efficiently and automatically construct gyral nets from surface meshes, and four measurements are devised to quantify the folding patterns. Using an MRI dataset for autism study as a test bed, we identified reduced local connectivity cost and increased curviness of gyral net bilaterally on the parietal lobe, occipital lobe, and temporal lobe in autistic patients. Additionally, we found that the cortical thickness and the gyral straightness of gyral joints are higher than the rest of gyral crest regions. The proposed representation offers a new tool for a comprehensive and reliable characterization of the cortical folding organization. This novel computational framework will enable large-scale analyses of cortical folding patterns in the future.
大脑皮层的一个显著特征是其凸（脑回）和凹（脑沟）折叠模式。由于脑回/脑沟形状的显著复杂性和可变性，对其组织模式进行定量建模一直具有挑战性。受脑回嵴线可以在每个大脑半球上形成一个连通图的观察启发，我们提出了一种新的皮质脑回/脑沟组织模式表示方法——脑回网络，它从图的角度对皮质结构进行建模，从重建的皮质表面获得节点和边。开发了一种新的计算框架，可从表面网格中高效且自动地构建脑回网络，并设计了四个测量指标来量化折叠模式。我们使用自闭症研究的 MRI 数据集作为测试平台，在自闭症患者的顶叶、枕叶和颞叶上发现双侧的局部连接成本降低和脑回网络曲率增加。此外，我们发现脑回关节的皮质厚度和脑回直线度高于脑回嵴区域的其余部分。所提出的表示方法为皮质折叠组织的全面和可靠特征提供了新工具。这个新的计算框架将能够在未来对皮质折叠模式进行大规模分析。
