Predicting clinical scores from magnetic resonance scans in Alzheimer's disease.
Détails
Etat: Public
Version: de l'auteur⸱e
Licence: CC BY 4.0
ID Serval
serval:BIB_6321B7682B90
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Predicting clinical scores from magnetic resonance scans in Alzheimer's disease.
Périodique
NeuroImage
Collaborateur⸱rice⸱s
Alzheimer Disease Neuroimaging Initiative
ISSN
1095-9572 (Electronic)
ISSN-L
1053-8119
Statut éditorial
Publié
Date de publication
15/07/2010
Peer-reviewed
Oui
Volume
51
Numéro
4
Pages
1405-1413
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Publication Status: ppublish
Résumé
Machine learning and pattern recognition methods have been used to diagnose Alzheimer's disease (AD) and mild cognitive impairment (MCI) from individual MRI scans. Another application of such methods is to predict clinical scores from individual scans. Using relevance vector regression (RVR), we predicted individuals' performances on established tests from their MRI T1 weighted image in two independent data sets. From Mayo Clinic, 73 probable AD patients and 91 cognitively normal (CN) controls completed the Mini-Mental State Examination (MMSE), Dementia Rating Scale (DRS), and Auditory Verbal Learning Test (AVLT) within 3months of their scan. Baseline MRI's from the Alzheimer's disease Neuroimaging Initiative (ADNI) comprised the other data set; 113 AD, 351 MCI, and 122 CN subjects completed the MMSE and Alzheimer's Disease Assessment Scale-Cognitive subtest (ADAS-cog) and 39 AD, 92 MCI, and 32 CN ADNI subjects completed MMSE, ADAS-cog, and AVLT. Predicted and actual clinical scores were highly correlated for the MMSE, DRS, and ADAS-cog tests (P<0.0001). Training with one data set and testing with another demonstrated stability between data sets. DRS, MMSE, and ADAS-Cog correlated better than AVLT with whole brain grey matter changes associated with AD. This result underscores their utility for screening and tracking disease. RVR offers a novel way to measure interactions between structural changes and neuropsychological tests beyond that of univariate methods. In clinical practice, we envision using RVR to aid in diagnosis and predict clinical outcome.
Mots-clé
Aged, Alzheimer Disease/pathology, Alzheimer Disease/psychology, Cognition/physiology, Data Interpretation, Statistical, Female, Humans, Image Processing, Computer-Assisted, Likelihood Functions, Magnetic Resonance Imaging, Male, Middle Aged, Neuropsychological Tests, Predictive Value of Tests, Psychomotor Performance/physiology, Regression Analysis, Reproducibility of Results, Verbal Learning/physiology
Pubmed
Web of science
Open Access
Oui
Création de la notice
14/06/2010 11:38
Dernière modification de la notice
20/08/2019 15:19