Sokunbi Moses O, Cameron George G, Ahearn Trevor S, Murray Alison D, Staff Roger T
Cognitive Neuroscience Sector, International School for Advanced Studies (SISSA), Via Bonomea 265, 34136 Trieste, Italy; MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, UK; Imaging Science, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK.
Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, UK.
Med Eng Phys. 2015 Nov;37(11):1082-90. doi: 10.1016/j.medengphy.2015.09.001. Epub 2015 Oct 21.
In this study, we present a method for measuring functional magnetic resonance imaging (fMRI) signal complexity using fuzzy approximate entropy (fApEn) and compare it with the established sample entropy (SampEn). Here we use resting state fMRI dataset of 86 healthy adults (41 males) with age ranging from 19 to 85 years. We expect the complexity of the resting state fMRI signals measured to be consistent with the Goldberger/Lipsitz model for robustness where healthier (younger) and more robust systems exhibit more complexity in their physiological output and system complexity decrease with age. The mean whole brain fApEn demonstrated significant negative correlation (r = -0.472, p<0.001) with age. In comparison, SampEn produced a non-significant negative correlation (r = -0.099, p = 0.367). fApEn also demonstrated a significant (p < 0.05) negative correlation with age regionally (frontal, parietal, limbic, temporal and cerebellum parietal lobes). There was no significant correlation regionally between the SampEn maps and age. These results support the Goldberger/Lipsitz model for robustness and have shown that fApEn is potentially a sensitive new method for the complexity analysis of fMRI data.
在本研究中，我们提出了一种使用模糊近似熵（fApEn）测量功能磁共振成像（fMRI）信号复杂度的方法，并将其与已确立的样本熵（SampEn）进行比较。这里我们使用了86名年龄在19至85岁之间的健康成年人（41名男性）的静息态fMRI数据集。我们期望所测量的静息态fMRI信号的复杂度与Goldberger/Lipsitz稳健性模型一致，即更健康（更年轻）且更稳健的系统在其生理输出中表现出更高的复杂度，并且系统复杂度会随着年龄增长而降低。全脑平均fApEn与年龄呈显著负相关（r = -0.472，p<0.001）。相比之下，SampEn产生的负相关不显著（r = -0.099，p = 0.367）。fApEn在区域上（额叶、顶叶、边缘叶、颞叶和小脑顶叶）也与年龄呈显著负相关（p < 0.05）。SampEn图谱与年龄在区域上没有显著相关性。这些结果支持了Goldberger/Lipsitz稳健性模型，并表明fApEn可能是一种用于fMRI数据复杂度分析的敏感新方法。
