Multiplex Networks for Early Diagnosis of Alzheimer's Disease

• Published in: Frontiers in aging neuroscience Vol. 10; p. 365
• Main Authors: Amoroso, Nicola, La Rocca, Marianna, Bruno, Stefania, Maggipinto, Tommaso, Monaco, Alfonso, Bellotti, Roberto, Tangaro, Sabina
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 14.11.2018; Frontiers Media S.A
• Subjects: Aging; Alzheimer's disease; Atrophy; Brain architecture; Clinical trials; Cognitive ability; Dementia; diagnosis support system; Image processing; machine learning; Magnetic resonance imaging; magnetic resonance imaging (MRI); Medical imaging; mild cognitive impairment; Morphometry; multiplex networks; Neurodegeneration; Neurodegenerative diseases; Neuroimaging; Neuroscience; NMR; Nuclear magnetic resonance; Segmentation; Italy; magnetic resonance imaging (MRI); diagnosis support system; mild cognitive impairment; brain Connectivity; machine learning; multiplex networks; Alzheimer's disease
• ISSN: 1663-4365; 1663-4365
• DOI: 10.3389/fnagi.2018.00365

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). Several studies have suggested that brain topological organization can reveal early signs of AD. Here, we propose a novel brain model which captures both intra- and inter-subject information within a multiplex network approach. This model localizes brain atrophy effects and summarizes them with a diagnostic score. On an independent test set, our multiplex-based score segregates (i) normal controls (NC) from AD patients with a 0.86±0.01 accuracy and (ii) NC from mild cognitive impairment (MCI) subjects that will convert to AD (cMCI) with an accuracy of 0.84±0.01. The model shows that illness effects are maximally detected by parceling the brain in equal volumes of 3, 000 mm ("patches"), without any segmentation based on anatomical features. The multiplex approach shows great sensitivity in detecting anomalous changes in the brain; the robustness of the obtained results is assessed using both voxel-based morphometry and FreeSurfer morphological features. Because of its generality this method can provide a reliable tool for clinical trials and a disease signature of many neurodegenerative pathologies.