Predicting the transition from normal aging to Alzheimer's disease: A statistical mechanistic evaluation of FDG-PET data

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2016-11, Vol.141, p.282-290
• Main Authors: Pagani, Marco, Giuliani, Alessandro, Öberg, Johanna, Chincarini, Andrea, Morbelli, Silvia, Brugnolo, Andrea, Arnaldi, Dario, Picco, Agnese, Bauckneht, Matteo, Buschiazzo, Ambra, Sambuceti, Gianmario, Nobili, Flavio
• Format: Article
• Language: English
• Subjects: Age; Aged; Aging; Aging - metabolism; Aging - pathology; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - metabolism; Alzheimer's disease; Biomarkers - metabolism; Brain; Brain - diagnostic imaging; Brain - metabolism; Cognitive ability; Cognitive Dysfunction - diagnostic imaging; Cognitive Dysfunction - metabolism; Computer Simulation; Data Interpretation, Statistical; Degree of order; Dementia; Disease Progression; FDG-Pet; Female; Fluorodeoxyglucose F18 - pharmacokinetics; Humans; Hypotheses; Image Interpretation, Computer-Assisted; Male; Metabolism; Mild cognitive impairment; Models, Statistical; Neurodegenerative diseases; Normal aging; Pathology; Patients; Positron emission tomography; Principal component analysis; Prognosis; Radiopharmaceuticals - pharmacokinetics; Reproducibility of Results; Sensitivity and Specificity; Statistics
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2016.07.043

The assessment of the degree of order of brain metabolism by means of a statistical mechanistic approach applied to FDG-PET, allowed us to characterize healthy subjects as well as patients with mild cognitive impairment and Alzheimer's Disease (AD). The intensity signals from 24 volumes of interest were submitted to principal component analysis (PCA) giving rise to a major first principal component whose eigenvalue was a reliable cumulative index of order. This index linearly decreased from 77 to 44% going from normal aging to AD patients with intermediate conditions between these values (r=0.96, p