Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu, Aizuwakamatsu, Fukushima 965-8580, Japan.
J Neurosci Methods. 2013 May 15;215(2):210-7. doi: 10.1016/j.jneumeth.2013.03.018. Epub 2013 Apr 1.
We apply for the first time the sample entropy (SampEn) and regularity dimension model for measuring signal complexity to quantify the structural complexity of the brain on MRI. The concept of the regularity dimension is based on the theory of chaos for studying nonlinear dynamical systems, where power laws and entropy measure are adopted to develop the regularity dimension for modeling a mathematical relationship between the frequencies with which information about signal regularity changes in various scales. The sample entropy and regularity dimension of MRI-based brain structural complexity are computed for early Alzheimer's disease (AD) elder adults and age and gender-matched non-demented controls, as well as for a wide range of ages from young people to elder adults. A significantly higher global cortical structure complexity is detected in AD individuals (p<0.001). The increase of SampEn and the regularity dimension are also found to be accompanied with aging which might indicate an age-related exacerbation of cortical structural irregularity. The provided model can be potentially used as an imaging bio-marker for early prediction of AD and age-related cognitive decline.
我们首次应用样本熵（SampEn）和规则维模型来测量磁共振成像（MRI）信号的复杂度，以量化大脑的结构复杂性。规则维的概念基于混沌理论，用于研究非线性动力系统，其中采用幂律和熵测度来开发规则维，以建立信号规则性随频率变化的数学关系。我们计算了基于 MRI 的大脑结构复杂性的样本熵和规则维，用于早期阿尔茨海默病（AD）老年人和年龄及性别匹配的非痴呆对照组，以及从年轻人到老年人的广泛年龄段。AD 个体的大脑皮质整体结构复杂性显著升高（p<0.001）。我们还发现，SampEn 和规则维的增加伴随着衰老，这可能表明皮质结构不规则性与年龄相关的恶化。该模型可作为 AD 早期预测和与年龄相关的认知能力下降的潜在影像生物标志物。
