Tags

Type your tag names separated by a space and hit enter

AddNeuroMed and ADNI: similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America.

Abstract

The European Union AddNeuroMed program and the US-based Alzheimer Disease Neuroimaging Initiative (ADNI) are two large multi-center initiatives designed to collect and validate biomarker data for Alzheimer's disease (AD). Both initiatives use the same MRI data acquisition scheme. The current study aims to compare and combine magnetic resonance imaging (MRI) data from the two study cohorts using an automated image analysis pipeline and a multivariate data analysis approach. We hypothesized that the two cohorts would show similar patterns of atrophy, despite demographic differences and could therefore be combined. MRI scans were analyzed from a total of 1074 subjects (AD=295, MCI=444 and controls=335) using Freesurfer, an automated segmentation scheme which generates regional volume and regional cortical thickness measures which were subsequently used for multivariate analysis (orthogonal partial least squares to latent structures (OPLS)). OPLS models were created for the individual cohorts and for the combined cohort to discriminate between AD patients and controls. The ADNI cohort was used as a replication dataset to validate the model created for the AddNeuroMed cohort and vice versa. The combined cohort model was used to predict conversion to AD at baseline of MCI subjects at 1 year follow-up. The AddNeuroMed, the ADNI and the combined cohort showed similar patterns of atrophy and the predictive power was similar (between 80 and 90%). The combined model also showed potential in predicting conversion from MCI to AD, resulting in 71% of the MCI converters (MCI-c) from both cohorts classified as AD-like and 60% of the stable MCI subjects (MCI-s) classified as control-like. This demonstrates that the methods used are robust and that large data sets can be combined if MRI imaging protocols are carefully aligned.

Links

  • Publisher Full Text
  • Authors+Show Affiliations

    ,

    Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden. eric.westman@ki.se

    , , , , , , , , , , , ,

    Source

    NeuroImage 58:3 2011 Oct 01 pg 818-28

    MeSH

    Aged
    Aged, 80 and over
    Alzheimer Disease
    Atrophy
    Brain
    Europe
    Female
    Humans
    Image Interpretation, Computer-Assisted
    Magnetic Resonance Imaging
    Male
    Middle Aged
    North America
    Predictive Value of Tests

    Pub Type(s)

    Journal Article
    Multicenter Study
    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

    21763442

    Citation

    Westman, Eric, et al. "AddNeuroMed and ADNI: Similar Patterns of Alzheimer's Atrophy and Automated MRI Classification Accuracy in Europe and North America." NeuroImage, vol. 58, no. 3, 2011, pp. 818-28.
    Westman E, Simmons A, Muehlboeck JS, et al. AddNeuroMed and ADNI: similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America. Neuroimage. 2011;58(3):818-28.
    Westman, E., Simmons, A., Muehlboeck, J. S., Mecocci, P., Vellas, B., Tsolaki, M., ... Wahlund, L. O. (2011). AddNeuroMed and ADNI: similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America. NeuroImage, 58(3), pp. 818-28. doi:10.1016/j.neuroimage.2011.06.065.
    Westman E, et al. AddNeuroMed and ADNI: Similar Patterns of Alzheimer's Atrophy and Automated MRI Classification Accuracy in Europe and North America. Neuroimage. 2011 Oct 1;58(3):818-28. PubMed PMID: 21763442.
    * Article titles in AMA citation format should be in sentence-case
    TY - JOUR T1 - AddNeuroMed and ADNI: similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America. AU - Westman,Eric, AU - Simmons,Andrew, AU - Muehlboeck,J-Sebastian, AU - Mecocci,Patrizia, AU - Vellas,Bruno, AU - Tsolaki,Magda, AU - Kłoszewska,Iwona, AU - Soininen,Hilkka, AU - Weiner,Michael W, AU - Lovestone,Simon, AU - Spenger,Christian, AU - Wahlund,Lars-Olof, AU - ,, AU - ,, Y1 - 2011/07/01/ PY - 2011/02/14/received PY - 2011/06/21/revised PY - 2011/06/23/accepted PY - 2011/7/19/entrez PY - 2011/7/19/pubmed PY - 2011/12/30/medline SP - 818 EP - 28 JF - NeuroImage JO - Neuroimage VL - 58 IS - 3 N2 - The European Union AddNeuroMed program and the US-based Alzheimer Disease Neuroimaging Initiative (ADNI) are two large multi-center initiatives designed to collect and validate biomarker data for Alzheimer's disease (AD). Both initiatives use the same MRI data acquisition scheme. The current study aims to compare and combine magnetic resonance imaging (MRI) data from the two study cohorts using an automated image analysis pipeline and a multivariate data analysis approach. We hypothesized that the two cohorts would show similar patterns of atrophy, despite demographic differences and could therefore be combined. MRI scans were analyzed from a total of 1074 subjects (AD=295, MCI=444 and controls=335) using Freesurfer, an automated segmentation scheme which generates regional volume and regional cortical thickness measures which were subsequently used for multivariate analysis (orthogonal partial least squares to latent structures (OPLS)). OPLS models were created for the individual cohorts and for the combined cohort to discriminate between AD patients and controls. The ADNI cohort was used as a replication dataset to validate the model created for the AddNeuroMed cohort and vice versa. The combined cohort model was used to predict conversion to AD at baseline of MCI subjects at 1 year follow-up. The AddNeuroMed, the ADNI and the combined cohort showed similar patterns of atrophy and the predictive power was similar (between 80 and 90%). The combined model also showed potential in predicting conversion from MCI to AD, resulting in 71% of the MCI converters (MCI-c) from both cohorts classified as AD-like and 60% of the stable MCI subjects (MCI-s) classified as control-like. This demonstrates that the methods used are robust and that large data sets can be combined if MRI imaging protocols are carefully aligned. SN - 1095-9572 UR - https://www.unboundmedicine.com/medline/citation/21763442/AddNeuroMed_and_ADNI:_similar_patterns_of_Alzheimer's_atrophy_and_automated_MRI_classification_accuracy_in_Europe_and_North_America_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1053-8119(11)00711-7 DB - PRIME DP - Unbound Medicine ER -