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体积越大越复杂:旋度的频谱分析(SPANGY)在成年人大脑尺寸多态性中的应用。

Larger is twistier: spectral analysis of gyrification (SPANGY) applied to adult brain size polymorphism.

机构信息

UMR663, INSERM-Université Paris Descartes, Paris, France.

出版信息

Neuroimage. 2012 Nov 15;63(3):1257-72. doi: 10.1016/j.neuroimage.2012.07.053. Epub 2012 Aug 2.

Abstract

The description of cortical folding pattern (CFP) is challenging because of geometric complexity and inter-subject variability. On a cortical surface mesh, curvature estimation provides a good scalar proxy of CFP. The oscillations of this function can be studied using a Fourier-like analysis to produce a power spectrum representative of the spatial frequency composition of CFP. First, we introduce an original method for the SPectral ANalysis of GYrication (Spangy), which performs a spectral decomposition of the mean curvature of the grey/white interface mesh based on the Laplace-Beltrami operator eigenfunctions. Spangy produces an ordered 7 bands power spectrum of curvature (B0-B6) and provides an anatomically relevant segmentation of CFP based on local spectral composition. A spatial frequency being associated with each eigenfunction, the bandwidth design assumes frequency doubling between consecutive spectral bands. Next, we observed that the last 3 spectral bands (B4, 5 and 6) accounted for 93% of the analyzed spectral power and were associated with fold-related variations of curvature, whereas the lower frequency bands were related to global brain shape. The spectral segmentation of CFP revealed 1st, 2nd and 3rd order elements associated with B4, B5 and B6 respectively. These elements could be related to developmentally-defined primary, secondary and tertiary folds. Finally, we used allometric scaling of frequency bands power and segmentation to analyze the relationship between the spectral composition of CFP and brain size in a large adult dataset. Total folding power followed a positive allometric scaling which did not divide up proportionally between the bands: B4 contribution was constant, B5 increased like total folding power and B6 much faster. Besides, apparition of new elements of pattern with increasing size only concerned the 3rd order. Hence, we demonstrate that large brains are twistier than smaller ones because of an increased number of high spatial frequency folds, ramifications and kinks that accommodate the allometric increase of cortical surface.

摘要

脑回模式(CFP)的描述具有挑战性,因为其具有几何复杂性和个体间变异性。在皮质表面网格上,曲率估计为 CFP 提供了良好的标量代理。可以使用类似于傅里叶的分析来研究该函数的振荡,从而生成代表 CFP 空间频率组成的功率谱。首先,我们引入了一种原始的 SPectral ANalysis of GYrication(Spangy)方法,该方法基于拉普拉斯-贝尔特拉米算子特征函数对灰质/白质界面网格的平均曲率进行谱分解。Spangy 产生了曲率的有序 7 个频带功率谱(B0-B6),并基于局部谱组成提供了 CFP 的解剖相关分割。由于每个特征函数都与一个空间频率相关,因此带宽设计假设在连续的谱带之间进行频率加倍。接下来,我们观察到,最后 3 个谱带(B4、B5 和 B6)占分析的谱功率的 93%,并且与曲率的褶皱相关变化相关,而较低的频带与大脑的整体形状有关。CFP 的谱分割揭示了与 B4、B5 和 B6 分别相关的第 1、2 和 3 阶元素。这些元素可能与发育定义的初级、次级和三级褶皱有关。最后,我们使用频带功率和分割的比例缩放来分析 CFP 的谱组成与大型成人数据集的大脑大小之间的关系。总折叠功率呈正比例缩放,并且各频带之间的比例并不均等:B4 的贡献是恒定的,B5 随着总折叠功率增加而增加,而 B6 增加得更快。此外,随着大脑尺寸的增加,只有 3 阶才会出现新的模式元素。因此,我们证明了大脑越大,其扭曲程度就越高,因为增加了更多的高空间频率褶皱、分支和扭结,以适应皮质表面积的比例增加。

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