Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, USA.
Neuroimage. 2010 Nov 1;53(2):450-9. doi: 10.1016/j.neuroimage.2010.06.072. Epub 2010 Jul 11.
This paper presents a novel statistical framework for human cortical folding pattern analysis that relies on a rich multivariate descriptor of folding patterns in a region of interest (ROI). The ROI-based approach avoids problems faced by spatial normalization-based approaches stemming from the deficiency of homologous features between typical human cerebral cortices. Unlike typical ROI-based methods that summarize folding by a single number, the proposed descriptor unifies multiple characteristics of surface geometry in a high-dimensional space (hundreds/thousands of dimensions). In this way, the proposed framework couples the reliability of ROI-based analysis with the richness of the novel cortical folding pattern descriptor. This paper presents new mathematical insights into the relationship of cortical complexity with intra-cranial volume (ICV). It shows that conventional complexity descriptors implicitly handle ICV differences in different ways, thereby lending different meanings to "complexity". The paper proposes a new application of a nonparametric permutation-based approach for rigorous statistical hypothesis testing with multivariate cortical descriptors. The paper presents two cross-sectional studies applying the proposed framework to study folding differences between genders and in neonates with complex congenital heart disease. Both studies lead to novel interesting results.
本文提出了一种新颖的统计框架，用于分析人类皮质折叠模式，该框架依赖于感兴趣区域 (ROI) 中折叠模式的丰富多元描述符。基于 ROI 的方法避免了基于空间归一化的方法所面临的问题，这些方法源于典型人类大脑皮质之间同源特征的缺乏。与仅用单个数字总结折叠的典型 ROI 方法不同，所提出的描述符在高维空间 (数百/数千个维度) 中统一了表面几何形状的多个特征。通过这种方式，所提出的框架将基于 ROI 分析的可靠性与新颖的皮质折叠模式描述符的丰富性结合起来。本文提出了一种新的数学方法，研究皮质复杂度与颅内体积 (ICV) 的关系。结果表明，传统的复杂度描述符以不同的方式隐含地处理不同的 ICV 差异，从而赋予“复杂度”不同的含义。本文提出了一种新的应用，即基于非参数置换的方法，用于对多元皮质描述符进行严格的统计假设检验。本文介绍了两项横断面研究，应用所提出的框架研究了性别之间以及患有复杂先天性心脏病的新生儿之间的折叠差异。这两项研究都得出了有趣的新结果。
