Abstract
We developed a multiplex network approach for the description and recognition of structural brain changes in the context of the early diagnosis of Alzheimer disease (AD). Our techniques can supply a convenient mathematical framework to model structural inter- and intra-subject brain similarities in magnetic resonance images (MRI) within Alzheimer disease studies. We used a set of 100 structural T1 brain scans, from subjects of the Alzheimer’s Disease Neuroimaging Initiative, including AD patients, normal controls (NC) and mild cognitive impairment (MCI) subjects. We evaluated the classification performances including the comparison of two state-of-the-art techniques, Random Forests (RF) and Support Vector Machines (SVM) . Our results show that multiplex networks can significantly improve the classification performance obtained only with the use of structural features. They can also effectively distinguish NC, MCI and AD patterns with an area under the receiver-operating-characteristic curve (AUC) \(\ge 0.89 \pm 0.04\).
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La Rocca, M. et al. (2017). A Multiplex Network Model to Characterize Brain Atrophy in Structural MRI. In: Mantica, G., Stoop, R., Stramaglia, S. (eds) Emergent Complexity from Nonlinearity, in Physics, Engineering and the Life Sciences. Springer Proceedings in Physics, vol 191. Springer, Cham. https://doi.org/10.1007/978-3-319-47810-4_15
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DOI: https://doi.org/10.1007/978-3-319-47810-4_15
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