  Analysis and quantification of brain structural changes, using Magnetic
resonance imaging (MRI), are increasingly used to define novel biomarkers of
brain pathologies, such as Alzheimer's disease (AD). Network-based models of
the brain have shown that both local and global topological properties can
reveal patterns of disease propagation. On the other hand, intra-subject
descriptions cannot exploit the whole information context, accessible through
inter-subject comparisons. To address this, we developed a novel approach,
which models brain structural connectivity atrophy with a multiplex network and
summarizes it within a classification score. On an independent dataset
multiplex networks were able to correctly segregate, from normal controls (NC),
AD patients and subjects with mild cognitive impairment that will convert to AD
(cMCI) with an accuracy of, respectively, $0.86 \pm 0.01$ and $0.84 \pm 0.01$.
The model also shows that illness effects are maximally detected by parceling
the brain in equal volumes of $3000$ $mm^3$ ("patches"), without any $a$
$priori$ segmentation based on anatomical features. A direct comparison to
standard voxel-based morphometry on the same dataset showed that the multiplex
network approach had higher sensitivity. This method is general and can have
twofold potential applications: providing a reliable tool for clinical trials
and a disease signature of neurodegenerative pathologies.
