title: Statistical physics approach to quantifying differences in myelinated nerve fibers.
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.
authors.affiliation: 1] Institute of Physics at São Carlos, University of São Paulo, São Carlos, SP 13560-970, Brazil [2].
authors.forename: César H
authors.initials: CH
authors.lastname: Comin
authors.affiliation: 1] Center for Polymer Studies and Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, USA [2].
authors.forename: João R
authors.initials: JR
authors.lastname: Santos
authors.affiliation: 1] Center for Polymer Studies and Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, USA [2].
authors.forename: Dario
authors.initials: D
authors.lastname: Corradini
authors.affiliation: Center for Polymer Studies and Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, USA.
authors.forename: Will
authors.initials: W
authors.lastname: Morrison
authors.affiliation: Center for Polymer Studies and Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, USA.
authors.forename: Chester
authors.initials: C
authors.lastname: Curme
authors.affiliation: Boston University, School of Medicine, Department of Anatomy & Neurobiology, Boston, Massachusetts 02118, USA.
authors.forename: Douglas L
authors.initials: DL
authors.lastname: Rosene
authors.affiliation: 1] Istituto dei Sistemi Complessi (ISC) - CNR, UOS "Sapienza", Dipartimento di Fisica, "Sapienza" Università di Roma, Piazzale Aldo Moro 5, 00185 Rome, Italy [2] IMT Alti Studi Lucca, Piazza S. Ponziano 6, 55100 Lucca, Italy.
authors.forename: Andrea
authors.initials: A
authors.lastname: Gabrielli
authors.affiliation: Institute of Physics at São Carlos, University of São Paulo, São Carlos, SP 13560-970, Brazil.
authors.forename: Luciano da F
authors.initials: Lda F
authors.lastname: Costa
authors.affiliation: Center for Polymer Studies and Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, USA.
authors.forename: H Eugene
authors.initials: HE
authors.lastname: Stanley
mesh_terms: D000367:Age Factors; D000818:Animals; D001369:Axons / Q000502:physiology / Q000648:ultrastructure; D016000:Cluster Analysis; D000882:Haplorhini; D009413:Nerve Fibers, Myelinated / Q000145:classification / Q000648:ultrastructure*