Document Detail


Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment.
MedLine Citation:
PMID:  22890700     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Automated structural magnetic resonance imaging (MRI) processing pipelines are gaining popularity for Alzheimer's disease (AD) research. They generate regional volumes, cortical thickness measures and other measures, which can be used as input for multivariate analysis. It is not clear which combination of measures and normalization approach are most useful for AD classification and to predict mild cognitive impairment (MCI) conversion. The current study includes MRI scans from 699 subjects [AD, MCI and controls (CTL)] from the Alzheimer's disease Neuroimaging Initiative (ADNI). The Freesurfer pipeline was used to generate regional volume, cortical thickness, gray matter volume, surface area, mean curvature, gaussian curvature, folding index and curvature index measures. 259 variables were used for orthogonal partial least square to latent structures (OPLS) multivariate analysis. Normalisation approaches were explored and the optimal combination of measures determined. Results indicate that cortical thickness measures should not be normalized, while volumes should probably be normalized by intracranial volume (ICV). Combining regional cortical thickness measures (not normalized) with cortical and subcortical volumes (normalized with ICV) using OPLS gave a prediction accuracy of 91.5 % when distinguishing AD versus CTL. This model prospectively predicted future decline from MCI to AD with 75.9 % of converters correctly classified. Normalization strategy did not have a significant effect on the accuracies of multivariate models containing multiple MRI measures for this large dataset. The appropriate choice of input for multivariate analysis in AD and MCI is of great importance. The results support the use of un-normalised cortical thickness measures and volumes normalised by ICV.
Authors:
Eric Westman; Carlos Aguilar; J-Sebastian Muehlboeck; Andrew Simmons
Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2012-08-14
Journal Detail:
Title:  Brain topography     Volume:  26     ISSN:  1573-6792     ISO Abbreviation:  Brain Topogr     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-01-04     Completed Date:  2013-06-11     Revised Date:  2013-07-12    
Medline Journal Info:
Nlm Unique ID:  8903034     Medline TA:  Brain Topogr     Country:  United States    
Other Details:
Languages:  eng     Pagination:  9-23     Citation Subset:  IM    
Affiliation:
Department of Neuroimaging, Institute of Psychiatry, King's College London, De Crespigny Park, London, UK. eric.westman@ki.se
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MeSH Terms
Descriptor/Qualifier:
Aged
Aged, 80 and over
Alzheimer Disease / pathology*
Brain Mapping
Cerebral Cortex / pathology*
Female
Humans
Image Interpretation, Computer-Assisted
Least-Squares Analysis
Longitudinal Studies
Magnetic Resonance Imaging*
Male
Mild Cognitive Impairment / pathology*
Multivariate Analysis
Grant Support
ID/Acronym/Agency:
K01 AG030514/AG/NIA NIH HHS; P30 AG010129/AG/NIA NIH HHS; U01 AG024904/AG/NIA NIH HHS
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