Statistical physics approach to quantifying differences in myelinated nerve fibers
Date
2014-03-28
Authors
Comin, Cesar H.
Santos, Joao R.
Corradini, Dario
Morrison, Will
Curme, Chester
Rosene, Douglas L.
Gabrielli, Andrea
Costa, Luciano da F.
Stanley, H. Eugene
Version
Published version
OA Version
Citation
Cesar H. Comin, Joao R. Santos, Dario Corradini, Will Morrison, Chester Curme, Douglas L. Rosene, Andrea Gabrielli, Luciano da F. Costa, H. Eugene Stanley. 2014. "Statistical physics approach to quantifying differences in myelinated nerve fibers." SCIENTIFIC REPORTS, Volume 4. https://doi.org/10.1038/srep04511
Abstract
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.
Description
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license.