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Current Alzheimer Research


ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

Integration of 18FDG-PET Metabolic and Functional Connectomes in the Early Diagnosis and Prognosis of the Alzheimer's Disease

Author(s): Antonio Giuliano Zippo and Isabella Castiglioni

Volume 13, Issue 5, 2016

Page: [487 - 497] Pages: 11

DOI: 10.2174/1567205013666151116142451

Price: $65


Alzheimer's Disease (AD) is an invalidating neurodegenerative disorders frequently affecting the aging population. In view of the increase of elderlies, not only in western countries, the related growing societal problems urge for identifying clinical biomarkers in view of potential treatments interfering or blocking the disease course. Among the plenty of anatomo-functional in vivo imaging techniques to inspect brain circuits and physiology, the Magnetic Resonance Imaging (MRI), the functional MRI (fMRI), the Electroencephalography (EEG) and Magnetoencephalography (MEG), have been extensively used for the study of AD, with different achievements and limitations. Eventually, the methodologies summoned by brain connectomics further strengthen the expectations in this field, as shown by recent results obtained with [18F]2-fluoro-2-deoxyglucose 18FDG-PET and fMRI in the prediction of the AD in early stages. However, the inherent complexity of the pathophysiology of the AD suggests that only integrative approaches combining different techniques and methodologies of brain scanning could produce significant breakthroughs in the study of AD. This review proposes a formal framework able to combine brain connectomic data from multimodal acquisitions by means of different in vivo neuroimaging techniques, briefly reporting their different advantages and drawbacks. Indeed, a specialized complex multiplex network, where nodes interact in layers linking the same pair of nodes and each layer reflects a distinct type of brain acquisition, can model the plurality of connectomes recommended in this framework.

Keywords: Alzheimer’s Disease, fMRI, MRI, PET, EEG, multiplex networks, brain connectomes, high-resolution multimodal scanner.

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