Department of Biomedical Sciences and Imaging, Biomedical Imaging Research Institute (BIRI), Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
Department of Radiology, Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Cereb Cortex. 2018 Apr 1;28(4):1358-1368. doi: 10.1093/cercor/bhx062.
Resting-state functional connectivity studies have dramatically improved our understanding of the early human brain functional development during the past decade. However, one emerging problem that could potentially impede future progresses in the field is the definition of regions of interest (ROI), since it is well known that functional connectivity estimation can be seriously contaminated by within-ROI signal heterogeneity. In this study, based on a large-scale rsfMRI data set in human infants (230 neonates, 143 1-year olds, and 107 2-year olds), we aimed to derive a set of anatomically constrained, infant-specific functional brain parcellations using functional connectivity-based clustering. Our results revealed significantly higher levels of signal homogeneity within the newly defined functional parcellations compared with other schemes. Importantly, the global functional connectivity patterns associated with the newly defined functional subunits demonstrated significantly increasing levels of differentiation with age, confirming increasing levels of local specialization. Subsequent whole brain connectivity analysis revealed intriguing patterns of regional-level functional connectivity developments and system-level hub redistribution during infancy. Overall, the newly derived infant-specific functional brain parcellations and the associated novel developmental patterns will likely prove valuable for future early developmental studies using the functional connectivity technique.
静息态功能连接研究在过去十年中极大地提高了我们对人类大脑早期功能发育的理解。然而,一个新兴的问题可能会阻碍该领域的未来进展,这就是感兴趣区域(ROI)的定义,因为众所周知,功能连接估计可能会受到 ROI 内信号异质性的严重污染。在这项研究中,我们基于人类婴儿的大规模 rsfMRI 数据集(230 名新生儿、143 名 1 岁婴儿和 107 名 2 岁婴儿),旨在使用基于功能连接的聚类方法得出一组解剖约束的、婴儿特有的功能脑区划分。我们的结果显示,与其他方案相比,新定义的功能区划分内的信号同质性水平显著更高。重要的是,与新定义的功能亚区相关的全局功能连接模式表现出与年龄相关的显著分化水平增加,证实了局部专业化程度的增加。随后的全脑连接分析揭示了在婴儿期区域水平功能连接发展和系统水平枢纽重新分配的有趣模式。总体而言,新得出的婴儿特定功能脑区划分和相关的新型发育模式可能对未来使用功能连接技术的早期发育研究非常有价值。