Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States of America.
PLoS One. 2013 May 13;8(5):e63310. doi: 10.1371/journal.pone.0063310. Print 2013.
Improved understanding of how the human brain is "wired" on a macroscale may now be possible due to the emerging field of MRI connectomics. However, mapping the rapidly developing infant brain networks poses challenges. In this study, we applied an automated template-free "baby connectome" framework using diffusion MRI to non-invasively map the structural brain networks in subjects of different ages, including premature neonates, term-born neonates, six-month-old infants, and adults. We observed increasing brain network integration and decreasing segregation with age in term-born subjects. We also explored how the equal area nodes can be grouped into modules without any prior anatomical information--an important step toward a fully network-driven registration and analysis of brain connectivity.
由于磁共振成像连接组学这一新兴领域的出现,我们现在可能可以更好地了解人类大脑在宏观尺度上是如何“连接”的。然而,绘制快速发育的婴儿大脑网络仍然具有挑战性。在这项研究中,我们应用了一种自动的、无模板的“婴儿连接组”框架,使用弥散磁共振成像技术来无创地绘制不同年龄的被试的结构大脑网络,包括早产儿、足月新生儿、六个月大的婴儿和成年人。我们观察到,足月出生的被试的大脑网络随着年龄的增长而逐渐整合,逐渐分化。我们还探索了如何在没有任何先前解剖学信息的情况下,将等面积节点分组到模块中,这是实现大脑连通性的完全网络驱动注册和分析的重要步骤。