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Hierarchical modeling of Alzheimer's disease progression from a large longitudinal MRI data set

Authors:
Alexandre Bône at Guerbet Group
Alexandre Bône
Benoit Martin at Sorbonne University
Benoit Martin
Maxime Louis at Paris Brain Institute, French National Centre for Scientific Research
Maxime Louis
Olivier Colliot at French National Centre for Scientific Research
Olivier Colliot
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Abstract

Untangling pathological processes from the natural inter-individual variability is a major challenge in Alzheimer’s disease (AD): natural anatomical variability is often much larger than individual pathological alterations, hindering the identification of relevant disease markers. In a longitudinal MRI data set of subjects progressing from mild cognitive impairment (MCI) to AD, variability not only comes from baseline differences, but also from the individual dynamics of progression. Based on recent computational morphometry tools, [1] proposes to learn a hierarchical model from longitudinal shape data sets, where individual progressions are represented in terms of orthogonal spatial and temporal warps of a common reference progression. In this work, we extend this approach to 3D images to model the spatiotemporal progression of atrophy in AD starting from the prodromal stage. A long-term average scenario of progression to AD is learned, along with interpretable individual parameters of variability that we correlate with genetic and environmental co-factors.
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Hierarchical modeling of Alzheimer's disease
progression from a large longitudinal MRI data set
Alexandre Bône1, Benoît Martin1, Maxime Louis1, Olivier Colliot1, Stanley Durrleman1,
and the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
1ARAMIS Lab, ICM, Inserm U1127, CNRS UMR 7225, Sorbonne University, Inria, Paris,
France
Introduction
Untangling pathological processes from the natural inter-individual variability is a major
challenge in Alzheimer’s disease (AD): natural anatomical variability is often much larger
than individual pathological alterations, hindering the identification of relevant disease
markers. In a longitudinal MRI data set of subjects progressing from mild cognitive
impairment (MCI) to AD, variability not only comes from baseline differences, but also from
the individual dynamics of progression. Based on recent computational morphometry tools,
[1] proposes to learn a hierarchical model from longitudinal shape data sets, where individual
progressions are represented in terms of orthogonal spatial and temporal warps of a
common reference progression. In this work, we extend this approach to 3D images to
model the spatiotemporal progression of atrophy in AD starting from the prodromal stage. A
long-term average scenario of progression to AD is learned, along with interpretable
individual parameters of variability that we correlate with genetic and environmental
co-factors.
Methods
We selected from the ADNI database all subjects meeting the following requirements: (i)
diagnosed as MCI at a visit, (ii) diagnosed as AD at a later visit, (iii) never reverted from AD
to MCI or from MCI to cognitively normal (CN). A total of 322 subjects fulfilled those criteria,
representing 1993 visits with T1-weighted MRI (average number of visits: 5.8 (±2.4); age:
74.0 (±6.7) years; education level: 15.9 (±2.8) years; 41.2% females; 65.2% APOE-ε4
carriers; 80.9% married). Images were first processed with the longitudinal FreeSurfer
pipeline [5] through the Clinica platform [6]. The resulting skull-removed intensity-normalized
images were then affinely aligned onto the Colin27 reference brain [4] with the FSL software
[7]. To reduce the computational burden, images were finally subsampled to a size of 643
voxels. The result was given as input to the longitudinal atlas pipeline of the Deformetrica
software [2, 3] that implements the approach introduced in [1]. Outputs are composed of an
average long-term scenario, along with low-dimensional parameters encoding how each
individual differ from this normative scenario. The variability in the dynamics is represented
for each individual by: (i) an estimated onset age that encodes the variability in the onset of
the alterations, (ii) an estimated acceleration factor that encodes the variability in the pace of
progression of those alterations.
Results
Figure 1 displays the estimated long-term scenario of progression to AD. The ventricles
clearly increase in size and atrophy is visible in the insula and parietal sulci regions. Figure 2
plots the estimated individual onset ages against the actual ages at which AD diagnosis was
made. The high correlation suggests that the model successfully captured the relative stages
of development of the disease across patients. We studied the association between
estimated onset ages and co-factors (gender, APOE-ε4 carriership, marital status and
education level) through multivariate linear regression. Analysis of the estimated coefficients
revealed that female subjects develop AD significantly earlier (by 31.2 (±18.4) months,
p=0.0019), similarly to APOE-ε4 carriers vs. non-carriers (by 19.8 (±17.5) months, p=0.036)
and married subjects vs. non-married (by 45.9 (±22.8) months, p=0.00038). The same
analysis was performed for the estimated individual acceleration factors: we found that
APOE-ε4 carriers progress significantly faster than non-carriers (by a factor of 1.14 (±0.09),
p=0.010). Confidence intervals are at 95%, and p-values are FDR-corrected.
Conclusions
The learned hierarchical model provides both a normative long-term scenario of AD
progression at the population level, and interpretable parameters encoding its dynamical
variability at the individual level. This scenario is in line with current medical knowledge, and
significant co-factors of progression can be identified from the individual parameters.
Acknowledgments
This work has been partly funded by grants ERC 678304, H2020 EU project 666992,
ANR-10-IAIHU-06.
References
[1]. Bône, A. et al. (2018, March). Learning distributions of shape trajectories from
longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Proceedings
of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 9271-9280).
[2]. Bône, A. et al. (2018, September). Deformetrica 4: an open-source software for
statistical shape analysis. In International Workshop on Shape in Medical Imaging. Springer.
[3]. Durrleman, S. et al. (2014). Morphometry of anatomical shape complexes with dense
deformations and sparse parameters. NeuroImage, 101, 35-49.
[4]. Holmes, C. J. et al. (1998). Enhancement of MR images using registration for signal
averaging. Journal of computer assisted tomography, 22(2), 324-333.
[5]. Reuter, M. et al. (2012). Within-subject template estimation for unbiased longitudinal
image analysis. Neuroimage, 61(4), 1402-1418.
[6]. Samper-González, J. et al. (2018). Reproducible evaluation of classification methods in
Alzheimer's disease: framework and application to MRI and PET data. bioRxiv, 274324.
[7]. Woolrich, M. W. et al. (2009). Bayesian analysis of neuroimaging data in FSL.
Neuroimage, 45(1), S173-S186.
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