Jiang Xi, Zhang Tuo, Zhu Dajiang, Li Kaiming, Lv Jinglei, Guo Lei, Liu Tianming
Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
School of Automation, Northwestern Polytechnical University, Xi'an, China.
Med Image Comput Comput Assist Interv. 2013;16(Pt 3):617-25. doi: 10.1007/978-3-642-40760-4_77.
Establishment of structural and functional correspondences across different brains is one of the most fundamental issues in the human brain mapping field. Recently, several multimodal DTI/fMRI studies have demonstrated that consistent white matter fiber connection patterns can predict brain function and represent common brain architectures across individuals and populations, and along this direction, several approaches have been proposed to discover large-scale cortical landmarks with common structural connection profiles. However, an important limitation of previous approaches is that the rich anatomical information such as gyral/sulcal folding patterns has not been incorporated into the landmark discovery procedure yet. In this paper, we present a novel anatomy-guided discovery framework that defines and optimizes a dense map of cortical landmarks that possess group-wise consistent anatomical and fiber connectional profiles. This framework effectively integrates reliable and rich anatomical, morphological, and fiber connectional information for landmark initialization, optimization and prediction, which are formulated and solved as an energy minimization problem. Validation results based on fMRI data demonstrate that the identified 555 cortical landmarks are producible, predictable and exhibit accurate structural and functional correspondences across individuals and populations, offering a universal and individualized brain reference system for neuroimaging research.
在不同大脑之间建立结构和功能对应关系是人类脑图谱领域最基本的问题之一。最近,一些多模态扩散张量成像/功能磁共振成像研究表明,一致的白质纤维连接模式可以预测脑功能,并代表个体和群体间共同的脑结构,沿着这个方向,已经提出了几种方法来发现具有共同结构连接特征的大规模皮质地标。然而,先前方法的一个重要局限性是,诸如脑回/脑沟折叠模式等丰富的解剖学信息尚未纳入地标发现过程。在本文中,我们提出了一种新颖的解剖学引导发现框架,该框架定义并优化了具有组内一致解剖和纤维连接特征的皮质地标密集图谱。该框架有效地整合了可靠且丰富的解剖学、形态学和纤维连接信息,用于地标初始化、优化和预测,这些被表述并求解为一个能量最小化问题。基于功能磁共振成像数据的验证结果表明,所识别出的555个皮质地标是可生成、可预测的,并且在个体和群体间表现出准确的结构和功能对应关系,为神经成像研究提供了一个通用且个性化的脑参考系统。