Department of Neurology, University of Utah, USA.
Neuroimage. 2010 Nov 1;53(2):471-9. doi: 10.1016/j.neuroimage.2010.06.050. Epub 2010 Jun 25.
Fractal analysis methods are used to quantify the complexity of the human cerebral cortex. Many recent studies have focused on high resolution three-dimensional reconstructions of either the outer (pial) surface of the brain or the junction between the gray and white matter, but ignore the structure between these surfaces. This study uses a new method to incorporate the entire cortical thickness. Data were obtained from the Alzheimer's Disease (AD) Neuroimaging Initiative database (Control N=35, Mild AD N=35). Image segmentation was performed using a semi-automated analysis program. The fractal dimension of three cortical models (the pial surface, gray/white surface and entire cortical ribbon) were calculated using a custom cube-counting triangle-intersection algorithm. The fractal dimension of the cortical ribbon showed highly significant differences between control and AD subjects (p<0.001). The inner surface analysis also found smaller but significant differences (p<0.05). The pial surface dimensionality was not significantly different between the two groups. All three models had a significant positive correlation with the cortical gyrification index (r>0.55, p<0.001). Only the cortical ribbon had a significant correlation with cortical thickness (r=0.832, p<0.001) and the Alzheimer's Disease Assessment Scale cognitive battery (r=-0.513, p=0.002). The cortical ribbon dimensionality showed a larger effect size (d=1.12) in separating control and mild AD subjects than cortical thickness (d=1.01) or gyrification index (d=0.84). The methodological change shown in this paper may allow for further clinical application of cortical fractal dimension as a biomarker for structural changes that accrue with neurodegenerative diseases.
分形分析方法用于量化人类大脑皮层的复杂性。许多最近的研究都集中在大脑外表面(脑皮层表面)或灰质和白质交界处的高分辨率三维重建上,但忽略了这些表面之间的结构。本研究使用一种新方法将整个皮质厚度纳入其中。数据来自阿尔茨海默病(AD)神经影像学倡议数据库(对照组 N=35,轻度 AD 组 N=35)。使用半自动分析程序进行图像分割。使用自定义的立方计数三角形交叉算法计算了三个皮质模型(脑皮层表面、灰白质表面和整个皮质带)的分形维数。皮质带的分形维数在对照组和 AD 患者之间存在显著差异(p<0.001)。内表面分析也发现了较小但显著的差异(p<0.05)。两组之间脑皮层表面的分形维数没有显著差异。所有三种模型与皮质脑回指数均呈显著正相关(r>0.55,p<0.001)。只有皮质带与皮质厚度(r=0.832,p<0.001)和阿尔茨海默病评估量表认知量表(r=-0.513,p=0.002)具有显著相关性。皮质带的分形维数在区分对照组和轻度 AD 患者方面的效果大小(d=1.12)大于皮质厚度(d=1.01)或脑回指数(d=0.84)。本文所示的方法变化可能允许进一步将皮质分形维数作为生物标志物应用于神经退行性疾病累积的结构变化的临床应用。