Abstract
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 mm3 (“patches”), without any a priori 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.
Highlights
Alzheimer’s disease (AD) is a progressive, neurodegenerative disease accounting for most cases of dementia after the age of 65
We employed an independent test set of 148 subjects Dtest, composed by 52 normal controls (NC), 48 AD and 48 subjects with mild cognitive impairment converting to AD
In this paper we propose a novel approach based on multiplex networks to characterize brain structural variations related to AD
Summary
Alzheimer’s disease (AD) is a progressive, neurodegenerative disease accounting for most cases of dementia after the age of 65. Illness related brain changes can be detected in vivo with Magnetic Resonance Imaging (MRI) and neuroimaging has been playing an increasingly important role for the diagnosis of neurodegenerative disorders (Bron et al, 2015; Wei et al, 2016; Lebedeva et al, 2017) to the extent that it has been incorporated in the diagnostic criteria for AD (McKhann et al, 2011). It is accepted that the neurodegenerative cascade in AD begins in the brain years, decades even, before the clinical and radiological manifestations of the illness. The dementia is preceded by a prodromal phase of mild cognitive impairment (Albert et al, 2011), and this, in turn, by a pre-clinical phase (Sperling et al, 2011) of variable duration. Understanding the biological changes, occurring in these early phases, is of paramount importance, as it would open a window
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