Li Gang, Wang Li, Lin Weili, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
Med Image Comput Comput Assist Interv. 2017 Sep;10433:66-74. doi: 10.1007/978-3-319-66182-7_8. Epub 2017 Sep 4.
The human cerebral cortex develops dynamically during the early postnatal stage, reflecting the underlying rapid changes of cortical microstructures and their connections, which jointly determine the functional principles of cortical regions. Hence, the dynamic cortical developmental patterns are ideal for defining the distinct cortical regions in microstructure and function for neurodevelopmental studies. Moreover, given the remarkable inter-subject variability in terms of cortical structure/function and their developmental patterns, the cortical parcellation based on each infant's own developmental patterns is critical for precisely localizing personalized distinct cortical regions and also understanding inter-subject variability. To this end, we propose a novel method for individualized parcellation of the infant cortical surface into distinct and meaningful regions based on each individual's cortical developmental patterns. Specifically, to alleviate the effects of cortical measurement errors and also make the individualized cortical parcellation comparable across subjects, we first create a -based cortical parcellation to capture the general developmental landscape of the cortex in an infant population. Then, this -based parcellation is leveraged to guide the parcellation based on each infant's own cortical developmental patterns in an manner. At each iteration, the individualized parcellation is gradually updated based on 1) the prior information of the -based parcellation, 2) the parcellation at the previous iteration, and also 3) the developmental patterns of all vertices. Experiments on fifteen healthy infants, each with longitudinal MRI scans acquired at six time points (i.e., 1, 3, 6, 9, 12 and 18 months of age), show that our method generates a reliable and meaningful individualized cortical parcellation based on each infant's own developmental patterns.
人类大脑皮层在出生后早期动态发育,反映了皮层微观结构及其连接的潜在快速变化,这些共同决定了皮层区域的功能原理。因此,动态皮层发育模式非常适合在神经发育研究中定义微观结构和功能上不同的皮层区域。此外,鉴于个体间在皮层结构/功能及其发育模式方面存在显著差异,基于每个婴儿自身发育模式的皮层分区对于精确定位个性化的不同皮层区域以及理解个体间差异至关重要。为此,我们提出了一种基于每个个体的皮层发育模式将婴儿皮层表面个性化划分为不同且有意义区域的新方法。具体而言,为了减轻皮层测量误差的影响并使跨个体的个性化皮层分区具有可比性,我们首先创建一个基于图谱的皮层分区,以捕捉婴儿群体中皮层的一般发育情况。然后,利用这种基于图谱的分区以迭代方式指导基于每个婴儿自身皮层发育模式的分区。在每次迭代中,个性化分区会根据以下因素逐步更新:1)基于图谱的分区的先验信息;2)上一次迭代的分区;3)所有顶点的发育模式。对15名健康婴儿进行的实验,每个婴儿在六个时间点(即1、3、6、9、12和18个月大)进行了纵向MRI扫描,结果表明我们的方法基于每个婴儿自身的发育模式生成了可靠且有意义的个性化皮层分区。