  The prediction of subjects with mild cognitive impairment (MCI) who will
progress to Alzheimer's disease (AD) is clinically relevant, and may above all
have a significant impact on accelerate the development of new treatments. In
this paper, we present a new MRI-based biomarker that enables us to predict
conversion of MCI subjects to AD accurately. In order to better capture the AD
signature, we introduce two main contributions. First, we present a new
graph-based grading framework to combine inter-subject similarity features and
intra-subject variability features. This framework involves patch-based grading
of anatomical structures and graph-based modeling of structure alteration
relationships. Second, we propose an innovative multiscale brain analysis to
capture alterations caused by AD at different anatomical levels. Based on a
cascade of classifiers, this multiscale approach enables the analysis of
alterations of whole brain structures and hippocampus subfields at the same
time. During our experiments using the ADNI-1 dataset, the proposed multiscale
graph-based grading method obtained an area under the curve (AUC) of 81% to
predict conversion of MCI subjects to AD within three years. Moreover, when
combined with cognitive scores, the proposed method obtained 85% of AUC. These
results are competitive in comparison to state-of-the-art methods evaluated on
the same dataset.
