Abstract
In the context of Alzheimer's disease, two challenging issues are (1) the characterization of local hippocampal shape changes specific to disease progression and (2) the identification of mild-cognitive impairment patients likely to convert. In the literature, (1) is usually solved first to detect areas potentially related to the disease. These areas are then considered as an input to solve (2). As an alternative to this sequential strategy, we investigate the use of a classification model using logistic regression to address both issues (1) and (2) simultaneously. The classification of the patients therefore does not require any a priori definition of the most representative hippocampal areas potentially related to the disease, as they are automatically detected. We first quantify deformations of patients' hippocampi between two time points using the large deformations by diffeomorphisms framework and transport these deformations to a common template. Since the deformations are expected to be spatially structured, we perform classification combining logistic loss and spatial regularization techniques, which have not been explored so far in this context, as far as we know. The main contribution of this paper is the comparison of regularization techniques enforcing the coefficient maps to be spatially smooth (Sobolev), piecewise constant (total variation) or sparse (fused LASSO) with standard regularization techniques which do not take into account the spatial structure (LASSO, ridge and ElasticNet). On a dataset of 103 patients out of ADNI, the techniques using spatial regularizations lead to the best classification rates. They also find coherent areas related to the disease progression.
Highlights
Large scale population studies aim to improve the understanding of the causes of diseases, define biomarkers for early diagnosis, and develop preventive treatments
Since coefficients are expected to be locally correlated in space, we investigate the Sobolev semi-norm, total variation semi-norm and fused-LASSO regularizations, respectively defined as
We studied deformation models for longitudinal population analysis, regularizations and machine learning strategies
Summary
Large scale population studies aim to improve the understanding of the causes of diseases, define biomarkers for early diagnosis, and develop preventive treatments. For Alzheimer's disease, several classification strategies have. Been proposed to separate patients according to their diagnosis. These methods can be split into three categories: voxel-based (Fan et al, 2007, 2008a,b; Klöppel et al, 2008; Lao et al, 2004; Magnin et al, 2009; Vemuri et al, 2008), cortical-thickness-based (Desikan et al, 2009; Klöppel et al, 2008; Querbes et al, 2009) and hippocampusbased (Chupin et al, 2007, 2009; Gerardin et al, 2009) methods. A recent review comparing these methods can be found in Cuingnet et al (2011)
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