Neurodegenerative diseases affect brain morphology and connectivity, making complex networks a suitable tool to investigate andmodel their effects. Because of its stereotyped pattern Alzheimer’s disease (AD) is a natural benchmark for the study of novelmethodologies. Several studies have investigated the network centrality and segregation changes induced by AD, especially with asingle 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 52normal controls (NC) and 47 AD patients, from Alzheimer’s Disease Neuroimaging Initiative (ADNI). The proposed topologicalscore 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, wasalso investigated. Topology is able to capture the disease signature of AD and, as the methodology is general, it can find interestingapplications to enhance our insight into disease with more heterogeneous patterns.
Topological measurements of DWI tractography for Alzheimer's disease detection
Amoroso N;Tangaro S
2017-01-01
Abstract
Neurodegenerative diseases affect brain morphology and connectivity, making complex networks a suitable tool to investigate andmodel their effects. Because of its stereotyped pattern Alzheimer’s disease (AD) is a natural benchmark for the study of novelmethodologies. Several studies have investigated the network centrality and segregation changes induced by AD, especially with asingle 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 52normal controls (NC) and 47 AD patients, from Alzheimer’s Disease Neuroimaging Initiative (ADNI). The proposed topologicalscore 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, wasalso investigated. Topology is able to capture the disease signature of AD and, as the methodology is general, it can find interestingapplications to enhance our insight into disease with more heterogeneous patterns.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
