Hierarchical models for epidermal nerve fiber data
Artikel i vetenskaplig tidskrift, 2018

While epidermal nerve fiber (ENF) data have been used to study the effects of small fiber neuropathies through the density and the spatial patterns of the ENFs, little research has been focused on the effects on the individual nerve fibers. Studying the individual nerve fibers might give a better understanding of the effects of the neuropathy on the growth process of the individual ENFs. In this study, data from 32 healthy volunteers and 20 diabetic subjects, obtained from suction induced skin blister biopsies, are analyzed by comparing statistics for the nerve fibers as a whole and for the segments that a nerve fiber is composed of. Moreover, it is evaluated whether this type of data can be used to detect diabetic neuropathy, by using hierarchical models to perform unsupervised classification of the subjects. It is found that using the information about the individual nerve fibers in combination with the ENF counts yields a considerable improvement as compared to using the ENF counts only.

diabetic neuropathy

nerve tree

adjusted Rand index

unsupervised classification

EM-algorithm

Författare

Claes Andersson

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Göteborgs universitet

Tuomas Rajala

University College London (UCL)

Aila Särkkä

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Göteborgs universitet

Statistics in Medicine

0277-6715 (ISSN) 1097-0258 (eISSN)

Vol. 37 3 357-374

Ämneskategorier

Annan data- och informationsvetenskap

Bioinformatik och systembiologi

Sannolikhetsteori och statistik

DOI

10.1002/sim.7516