Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Boyd GSRC 420, Athens, GA, 30602, USA.
School of Automation, Northwestern Polytechnical University, Xi'an, People's Republic of China.
Brain Imaging Behav. 2019 Oct;13(5):1427-1443. doi: 10.1007/s11682-018-9944-7.
Discovery and representation of common structural and functional cortical architectures has been a significant yet challenging problem for years. Due to the remarkable variability of structural and functional cortical architectures in human brain, it is challenging to jointly represent a common cortical architecture which can comprehensively encode both structure and function characteristics. In order to better understand this challenge and considering that macaque monkey brain has much less variability in structure and function compared with human brain, in this paper, we propose a novel computational framework to apply our DICCCOL (Dense Individualized and Common Connectivity-based Cortical Landmarks) and HAFNI (Holistic Atlases of Functional Networks and Interactions) frameworks on macaque brains, in order to jointly represent structural and functional connectome-scale profiles for identification of a set of consistent and common cortical landmarks across different macaque brains based on multimodal DTI and resting state fMRI (rsfMRI) data. Experimental results demonstrate that 100 consistent and common cortical landmarks are successfully identified via the proposed framework, each of which has reasonably accurate anatomical, structural fiber connection pattern, and functional correspondences across different macaque brains. This set of 100 landmarks offer novel insights into the structural and functional cortical architectures in macaque brains.
多年来，发现和表示常见的结构和功能皮质结构一直是一个重要但具有挑战性的问题。由于人类大脑结构和功能皮质结构的显著可变性，很难共同表示一种常见的皮质结构，这种结构可以全面编码结构和功能特征。为了更好地理解这一挑战，并且考虑到猕猴大脑在结构和功能上的可变性比人类大脑要小得多，在本文中，我们提出了一个新的计算框架，将我们的 DICCCOL（基于密集个体化和常见连接的皮质标记）和 HAFNI（功能网络和相互作用的整体图谱）框架应用于猕猴大脑，以联合表示结构和功能连接组规模的图谱，从而根据多模态 DTI 和静息态 fMRI(rsfMRI)数据，在不同的猕猴大脑中识别出一组一致的和常见的皮质标记。实验结果表明，通过所提出的框架成功识别出 100 个一致和常见的皮质标记，每个标记在不同的猕猴大脑中都具有合理准确的解剖学、结构纤维连接模式和功能对应关系。这组 100 个标记为猕猴大脑的结构和功能皮质结构提供了新的见解。
