Tags

Type your tag names separated by a space and hit enter

Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment.

Abstract

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.

Links

  • PMC Free PDF
  • PMC Free Full Text
  • Publisher Full Text
  • Authors+Show Affiliations

    ,

    Department of Neuroimaging, Institute of Psychiatry, King's College London, De Crespigny Park, London, UK. eric.westman@ki.se

    , ,

    Source

    Brain topography 26:1 2013 Jan pg 9-23

    MeSH

    Aged
    Aged, 80 and over
    Alzheimer Disease
    Brain Mapping
    Cerebral Cortex
    Cognitive Dysfunction
    Female
    Humans
    Image Interpretation, Computer-Assisted
    Least-Squares Analysis
    Longitudinal Studies
    Magnetic Resonance Imaging
    Male
    Multivariate Analysis

    Pub Type(s)

    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.

    Language

    eng

    PubMed ID

    22890700

    Citation

    Westman, Eric, et al. "Regional Magnetic Resonance Imaging Measures for Multivariate Analysis in Alzheimer's Disease and Mild Cognitive Impairment." Brain Topography, vol. 26, no. 1, 2013, pp. 9-23.
    Westman E, Aguilar C, Muehlboeck JS, et al. Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment. Brain Topogr. 2013;26(1):9-23.
    Westman, E., Aguilar, C., Muehlboeck, J. S., & Simmons, A. (2013). Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment. Brain Topography, 26(1), pp. 9-23. doi:10.1007/s10548-012-0246-x.
    Westman E, et al. Regional Magnetic Resonance Imaging Measures for Multivariate Analysis in Alzheimer's Disease and Mild Cognitive Impairment. Brain Topogr. 2013;26(1):9-23. PubMed PMID: 22890700.
    * Article titles in AMA citation format should be in sentence-case
    TY - JOUR T1 - Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment. AU - Westman,Eric, AU - Aguilar,Carlos, AU - Muehlboeck,J-Sebastian, AU - Simmons,Andrew, Y1 - 2012/08/14/ PY - 2012/05/14/received PY - 2012/07/21/accepted PY - 2012/8/15/entrez PY - 2012/8/15/pubmed PY - 2013/6/12/medline SP - 9 EP - 23 JF - Brain topography JO - Brain Topogr VL - 26 IS - 1 N2 - 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. SN - 1573-6792 UR - https://www.unboundmedicine.com/medline/citation/22890700/Regional_magnetic_resonance_imaging_measures_for_multivariate_analysis_in_Alzheimer's_disease_and_mild_cognitive_impairment_ L2 - https://doi.org/10.1007/s10548-012-0246-x DB - PRIME DP - Unbound Medicine ER -