School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia; Monash Biomedical Imaging, Monash University, Melbourne, Australia; Department of Biomedical Engineering, University of Melbourne, Melbourne, Australia; The Australian e-Health Research Centre, CSIRO, Melbourne, Australia.
Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; Department of Neurosciences, University of Turin, Italy.
Neuroimage. 2021 Apr 1;229:117751. doi: 10.1016/j.neuroimage.2021.117751. Epub 2021 Jan 15.
An accurate measure of the complexity of patterns of cortical folding or gyrification is necessary for understanding normal brain development and neurodevelopmental disorders. Conventional gyrification indices (GIs) are calculated based on surface curvature (curvature-based GI) or an outer hull surface of the cortex (outer surface-based GI). The latter is dependent on the definition of the outer hull surface and a corresponding function between surfaces. In the present study, we propose the Laplace Beltrami-based gyrification index (LB-GI). This is a new curvature-based local GI computed using the first three Laplace Beltrami eigenfunction level sets. As with outer surface-based GI methods, this method is based on the hypothesis that gyrification stems from a flat surface during development. However, instead of quantifying gyrification with reference to corresponding points on an outer hull surface, LB-GI quantifies the gyrification at each point on the cortical surface with reference to their surrounding gyral points, overcoming several shortcomings of existing methods. The LB-GI was applied to investigate the cortical maturation profile of the human brain from preschool to early adulthood using the PING database. The results revealed more detail in patterns of cortical folding than conventional curvature-based methods, especially on frontal and posterior tips of the brain, such as the frontal pole, lateral occipital, lateral cuneus, and lingual. Negative associations of cortical folding with age were observed at cortical regions, including bilateral lingual, lateral occipital, precentral gyrus, postcentral gyrus, and superior frontal gyrus. The results also indicated positive significant associations between age and the LB-GI of bilateral insula, the medial orbitofrontal, frontal pole and rostral anterior cingulate regions. It is anticipated that the LB-GI will be advantageous in providing further insights in the understanding of brain development and degeneration in large clinical neuroimaging studies.
准确衡量皮质折叠或脑回模式的复杂性对于理解正常大脑发育和神经发育障碍是必要的。传统的脑回指数(GI)是基于表面曲率(基于曲率的 GI)或皮质的外表面(基于外表面的 GI)计算的。后者依赖于外表面的定义和相应的曲面之间的函数。在本研究中,我们提出了基于拉普拉斯 - 贝尔特拉米的脑回指数(LB-GI)。这是一种新的基于曲率的局部 GI,使用前三个拉普拉斯 - 贝尔特拉米特征函数水平集计算。与基于外表面的 GI 方法一样,该方法基于脑回是从发育过程中的平坦表面开始的假设。然而,LB-GI 不是通过参考外表面上的对应点来量化脑回,而是通过参考其周围的脑回点来量化皮质表面上的每个点的脑回,克服了现有方法的几个缺点。LB-GI 被应用于使用 PING 数据库研究从学前到早期成年的人类大脑的皮质成熟谱。结果显示,与传统的基于曲率的方法相比,该方法在大脑的额极、外侧枕叶、外侧楔叶和舌状回等脑回模式上显示出更多的细节。在包括双侧舌状回、外侧枕叶、中央前回、中央后回和额上回在内的皮质区域,皮质折叠与年龄呈负相关。结果还表明,双侧岛叶、内侧眶额回、额极和额前扣带回区域的年龄与 LB-GI 之间存在显著的正相关。预计 LB-GI 将有助于在大型临床神经影像学研究中进一步深入了解大脑发育和退化。