Title:Integration of <sup>18</sup>FDG-PET Metabolic and Functional Connectomes in the Early Diagnosis and Prognosis of the Alzheimer's Disease
VOLUME: 13 ISSUE: 5
Author(s):Antonio Giuliano Zippo and Isabella Castiglioni
Affiliation:Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council, Via Fratelli Cervi 93, 20090, Segrate (Milan), Italy
Keywords:Alzheimer’s Disease, fMRI, MRI, PET, EEG, multiplex networks, brain connectomes, high-resolution multimodal
scanner.
Abstract: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.
