Abstract
We propose and detail a deformation-based morphometry computational framework, called Longitudinal Log-Demons Framework (LLDF), to estimate the longitudinal brain deformations from image data series, transport them in a common space and perform statistical group-wise analyses. It is based on freely available software and tools, and consists of three main steps: (i) Pre-processing, (ii) Position correction, and (iii) Non-linear deformation analysis. It is based on the LCC log-Demons non-linear symmetric diffeomorphic registration algorithm with an additional modulation of the similarity term using a confidence mask to increase the robustness with respect to brain boundary intensity artifacts. The pipeline is exemplified on the longitudinal Open Access Series of Imaging Studies (OASIS) database and all the parameters values are given so that the study can be reproduced. We investigate the group-wise differences between the patients with Alzheimer's disease and the healthy control group, and show that the proposed pipeline increases the sensitivity with no decrease in the specificity of the statistical study done on the longitudinal deformations.
Highlights
An important topic in neuroimaging is to analyse the progression of morphological changes in the brain observed in time series of images, in order to model and quantify normal or pathological biological evolutions (Scahill et al, 2002)
The paper is structured as follows: in Section 2, we develop a comprehensive processing pipeline called Longitudinal LogDemons Framework (LLDF); we present each elementary modules it is based on, and after introducing the mathematical formalism related to Deformation-Based Morphometry (DBM), we modify the Local Correlation Coefficient (LCC) log-Demons to incorporate a confidence mask
After applying the processing pipeline to the database, we obtain the transported subject-specific longitudinal deformation trajectories φTi (t) = exp(t · vTi ) for each subject i in the study-specific template: we get 72 subject-specific longitudinal Stationary Velocity Fields (SVF) vTi for the healthy controls and 64 for the patients with Alzheimer’s disease
Summary
An important topic in neuroimaging is to analyse the progression of morphological changes in the brain observed in time series of images, in order to model and quantify normal or pathological biological evolutions (Scahill et al, 2002). Deformation-Based Morphometry (DBM) (Ashburner et al, 1998) characterizes the morphological changes of the brain in terms of spatial transformations (here called deformations), estimated by means of non-linear registration. Longitudinal DBM main steps can be summarized as (i) quantifying the evolution of the morphology of each subject by estimating the individual’s longitudinal deformation from the time series of images, and (ii) characterizing how this evolution varies among a sample using a suitable normalization for the individual biological variability. A variety of DBM approaches can be found in the literature (e.g., Davatzikos et al, 2001; Cardenas et al, 2007; Lorenzi et al, 2011; Südmeyer et al, 2012), each of them associated to specific non-linear registration methods, and Longitudinal Analysis with Stationary Velocity Fields processing pipelines. In the developing context of reproducible research that has gained interest over the last years (Nature, 2013; McCormick et al, 2014), a good practice should be for researchers to publish the full details of their methodology: source code, data and parameters
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