How early can we predict Alzheimer's disease using computational anatomy?

Détails

ID Serval
serval:BIB_CEDAB91D1112
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
How early can we predict Alzheimer's disease using computational anatomy?
Périodique
Neurobiology of Aging
Auteur(s)
Adaszewski S., Dukart J., Kherif F., Frackowiak R., Draganski B.
Collaborateur(s)
Alzheimer's Disease Neuroimaging Initiative
ISSN
1558-1497 (Electronic)
ISSN-L
0197-4580
Statut éditorial
Publié
Date de publication
2013
Peer-reviewed
Oui
Volume
34
Numéro
12
Pages
2815-2826
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Résumé
Computational anatomy with magnetic resonance imaging (MRI) is well established as a noninvasive biomarker of Alzheimer's disease (AD); however, there is less certainty about its dependency on the staging of AD. We use classical group analyses and automated machine learning classification of standard structural MRI scans to investigate AD diagnostic accuracy from the preclinical phase to clinical dementia. Longitudinal data from the Alzheimer's Disease Neuroimaging Initiative were stratified into 4 groups according to the clinical status-(1) AD patients; (2) mild cognitive impairment (MCI) converters; (3) MCI nonconverters; and (4) healthy controls-and submitted to a support vector machine. The obtained classifier was significantly above the chance level (62%) for detecting AD already 4 years before conversion from MCI. Voxel-based univariate tests confirmed the plausibility of our findings detecting a distributed network of hippocampal-temporoparietal atrophy in AD patients. We also identified a subgroup of control subjects with brain structure and cognitive changes highly similar to those observed in AD. Our results indicate that computational anatomy can detect AD substantially earlier than suggested by current models. The demonstrated differential spatial pattern of atrophy between correctly and incorrectly classified AD patients challenges the assumption of a uniform pathophysiological process underlying clinically identified AD.
Pubmed
Web of science
Création de la notice
09/08/2013 20:23
Dernière modification de la notice
20/08/2019 16:49
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