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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 5271627, 10 pages
https://doi.org/10.1155/2017/5271627
Research Article

Topological Measurements of DWI Tractography for Alzheimer’s Disease Detection

1Università degli Studi di Bari “A. Moro”, Via Orabona 4, 70123 Bari, Italy
2Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via Orabona 4, 70123 Bari, Italy

Correspondence should be addressed to Nicola Amoroso; ti.nfni.ab@osoroma.alocin

Received 4 August 2016; Accepted 27 October 2016; Published 2 March 2017

Academic Editor: Ayman El-Baz

Copyright © 2017 Nicola Amoroso et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Neurodegenerative diseases affect brain morphology and connectivity, making complex networks a suitable tool to investigate and model their effects. Because of its stereotyped pattern Alzheimer’s disease (AD) is a natural benchmark for the study of novel methodologies. Several studies have investigated the network centrality and segregation changes induced by AD, especially with a single subject approach. In this work, a holistic perspective based on the application of multiplex network concepts is introduced. We define and assess a diagnostic score to characterize the brain topology and measure the disease effects on a mixed cohort of 52 normal controls (NC) and 47 AD patients, from Alzheimer’s Disease Neuroimaging Initiative (ADNI). The proposed topological score allows an accurate NC-AD classification: the average area under the curve (AUC) is 95% and the 95% confidence interval is 92%–99%. Besides, the combination of topological information and structural measures, such as the hippocampal volumes, was also investigated. Topology is able to capture the disease signature of AD and, as the methodology is general, it can find interesting applications to enhance our insight into disease with more heterogeneous patterns.