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Degeneracy in model parameter estimation for multi‐compartmental diffusion in neuronal tissue
Author(s) -
Jelescu Ileana O.,
Veraart Jelle,
Fieremans Els,
Novikov Dmitry S.
Publication year - 2016
Publication title -
nmr in biomedicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.278
H-Index - 114
eISSN - 1099-1492
pISSN - 0952-3480
DOI - 10.1002/nbm.3450
Subject(s) - maxima and minima , degeneracy (biology) , parameter space , estimation theory , spurious relationship , function (biology) , noise (video) , diffusion mri , statistical physics , diffusion , a priori and a posteriori , signal (programming language) , space (punctuation) , mathematics , biological system , computer science , physics , algorithm , statistics , mathematical analysis , magnetic resonance imaging , artificial intelligence , biology , philosophy , image (mathematics) , operating system , epistemology , evolutionary biology , radiology , programming language , thermodynamics , medicine , bioinformatics
The ultimate promise of diffusion MRI (dMRI) models is specificity to neuronal microstructure, which may lead to distinct clinical biomarkers using noninvasive imaging. While multi‐compartment models are a common approach to interpret water diffusion in the brain in vivo , the estimation of their parameters from the dMRI signal remains an unresolved problem. Practically, even when q space is highly oversampled, nonlinear fit outputs suffer from heavy bias and poor precision. So far, this has been alleviated by fixing some of the model parameters to a priori values, for improved precision at the expense of accuracy. Here we use a representative two‐compartment model to show that fitting fails to determine the five model parameters from over 60 measurement points. For the first time, we identify the reasons for this poor performance. The first reason is the existence of two local minima in the parameter space for the objective function of the fitting procedure. These minima correspond to qualitatively different sets of parameters, yet they both lie within biophysically plausible ranges. We show that, at realistic signal‐to‐noise ratio values, choosing between the two minima based on the associated objective function values is essentially impossible. Second, there is an ensemble of very low objective function values around each of these minima in the form of a pipe. The existence of such a direction in parameter space, along which the objective function profile is very flat, explains the bias and large uncertainty in parameter estimation, and the spurious parameter correlations: in the presence of noise, the minimum can be randomly displaced by a very large amount along each pipe. Our results suggest that the biophysical interpretation of dMRI model parameters crucially depends on establishing which of the minima is closer to the biophysical reality and the size of the uncertainty associated with each parameter. Copyright © 2015 John Wiley & Sons, Ltd.