Peng Limin, Luo Zhiguo, Zeng Ling-Li, Hou Chenping, Shen Hui, Zhou Zongtan, Hu Dewen
College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.
College of Science, National University of Defense Technology, Changsha 410073, China.
Cereb Cortex. 2023 Mar 21;33(7):3575-3590. doi: 10.1093/cercor/bhac293.
Brain cartography has expanded substantially over the past decade. In this regard, resting-state functional connectivity (FC) plays a key role in identifying the locations of putative functional borders. However, scant attention has been paid to the dynamic nature of functional interactions in the human brain. Indeed, FC is typically assumed to be stationary across time, which may obscure potential or subtle functional boundaries, particularly in regions with high flexibility and adaptability. In this study, we developed a dynamic FC (dFC)-based parcellation framework, established a new functional human brain atlas termed D-BFA (DFC-based Brain Functional Atlas), and verified its neurophysiological plausibility by stereo-EEG data. As the first dFC-based whole-brain atlas, the proposed D-BFA delineates finer functional boundaries that cannot be captured by static FC, and is further supported by good correspondence with cytoarchitectonic areas and task activation maps. Moreover, the D-BFA reveals the spatial distribution of dynamic variability across the brain and generates more homogenous parcels compared with most alternative parcellations. Our results demonstrate the superiority and practicability of dFC in brain parcellation, providing a new template to exploit brain topographic organization from a dynamic perspective. The D-BFA will be publicly available for download at https://github.com/sliderplm/D-BFA-618.
在过去十年中,脑图谱学得到了极大的发展。在这方面,静息态功能连接(FC)在确定假定功能边界的位置方面起着关键作用。然而,人们很少关注人类大脑中功能相互作用的动态特性。事实上,FC通常被认为随时间是固定不变的,这可能会掩盖潜在的或细微的功能边界,特别是在具有高灵活性和适应性的区域。在本研究中,我们开发了一种基于动态FC(dFC)的脑区划分框架,建立了一个名为D-BFA(基于动态功能连接的脑功能图谱)的新的人类功能脑图谱,并通过立体脑电图数据验证了其神经生理学合理性。作为首个基于dFC的全脑图谱,所提出的D-BFA描绘了静态FC无法捕捉到的更精细的功能边界,并且与细胞构筑区域和任务激活图谱的良好对应进一步支持了这一点。此外,与大多数其他脑区划分相比,D-BFA揭示了全脑动态变异性的空间分布,并生成了更均匀的脑区。我们的结果证明了dFC在脑区划分中的优越性和实用性,为从动态角度探索脑地形图组织提供了一个新的模板。D-BFA将在https://github.com/sliderplm/D-BFA-618上公开提供下载。