Statistical physics approach to quantifying differences in myelinated nerve fibers.
Statistical physics approach to quantifying differences in myelinated nerve fibers.
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DOI：
10.1038/srep04511
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发表时间：
2014-03-28
影响因子：
4.6
通讯作者：
Stanley HE
中科院分区：
文献类型：
--
作者：
Comin CH;Santos JR;Corradini D;Morrison W;Curme C;Rosene DL;Gabrielli A;Costa Lda F;Stanley HE
我们提出了一种新的方法来量化有髓神经纤维的差异。这些差异的范围从单个纤维的形态特征到纤维集合的宏观性质的差异。我们的方法使用统计物理工具来改进传统的测量方法，例如纤维尺寸和堆积密度。作为一个案例研究，我们分析了横截面电子显微照片从穹窿的年轻和年老的恒河猴使用半自动检测算法来识别和表征有髓轴突。然后，我们应用一种特征选择方法来识别最能区分年轻和老年人群的特征，在将样本分配给他们的年龄组时，最高准确率达到94%。该分析表明，最好的歧视是使用两个功能的组合：所占轴突面积的分数和有效的局部密度。后者是轴突密度的修正计算，反映了轴突的紧密程度。我们的特征分析方法可以应用于表征由生物过程（如老化、创伤或疾病或发育差异造成的损伤）以及解剖区域（如穹窿和扣带束或胼胝体）之间的差异。
We present a new method to quantify differences in myelinated nerve fibers. These differences range from morphologic characteristics of individual fibers to differences in macroscopic properties of collections of fibers. Our method uses statistical physics tools to improve on traditional measures, such as fiber size and packing density. As a case study, we analyze cross–sectional electron micrographs from the fornix of young and old rhesus monkeys using a semi-automatic detection algorithm to identify and characterize myelinated axons. We then apply a feature selection approach to identify the features that best distinguish between the young and old age groups, achieving a maximum accuracy of 94% when assigning samples to their age groups. This analysis shows that the best discrimination is obtained using the combination of two features: the fraction of occupied axon area and the effective local density. The latter is a modified calculation of axon density, which reflects how closely axons are packed. Our feature analysis approach can be applied to characterize differences that result from biological processes such as aging, damage from trauma or disease or developmental differences, as well as differences between anatomical regions such as the fornix and the cingulum bundle or corpus callosum.
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影响因子：
2
作者：
Ayranci, Ebru;Altunkaynak, Berrin Zuhal;Kaplan, Suleyman
通讯作者：
Kaplan, Suleyman
影响因子：
64.5
作者：
BASTIANI, MJ;HARRELSON, AL;GOODMAN, CS
通讯作者：
GOODMAN, CS
DOI：
10.1073/pnas.0407002101
发表时间：
2004-11-09
影响因子：
11.1
作者：
Cruz, L;Roe, DL;Rosene, DL
通讯作者：
Rosene, DL
DOI：
10.1016/j.compmedimag.2007.01.003
发表时间：
2007-09-01
影响因子：
5.7
作者：
Celebi, M. Emre;Kingravi, Hassan A.;Moss, Randy H.
通讯作者：
Moss, Randy H.
DOI：
10.1037/h0037649
发表时间：
1974-01-01
期刊：
JOURNAL OF COMPARATIVE AND PHYSIOLOGICAL PSYCHOLOGY
影响因子：
--
作者：
GAFFAN, D
通讯作者：
GAFFAN, D