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Combining Two Large MRI Data Sets (AddNeuroMed and ADNI) Using Multivariate Data Analysis to Distinguish between Patients with Alzheimer's Disease and Healthy Controls

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  • Eric Westman, Karolinska Institutet, Sweden
  • Andrew Simmons, Kings College London, United Kingdom
  • J.-Sebastian Muehlboeck, Karolinska Institutet, Sweden
  • Femida Gwadry-Sridhar, Lawson Health Research Institute, Canada
  • Simon Fristed Eskildsen
  • Per Julin, AstraZeneca R&D, Sweden
  • Niclas Sjögren, AstraZeneca R&D, Sweden
  • D. Louis Collins, McGill University, Canada
  • Alan Evans, McGill University, Canada
  • Patrizia Mecocci, University of Perugia, Italy
  • Bruno Vellas, University of Toulouse, France
  • Magda Tsolaki, Aristotle University of Thessaloniki, Greece
  • Iwona Kłoszewska, Medical University of Lodz, Poland
  • Hilkka Soininen, University and University Hospital of Kuopio, Finland
  • Michael Weiner, University of California, United States
  • Lars-Olof Wahlund, Karolinska Institutet, Sweden
Background: The European Union AddNeuroMed project and the US-based Alzheimer Disease Neuroimaging Initiative (ADNI) are two large multi-centre initiatives designed to analyse and validate biomarkers for AD. This study aims to compare and combine magnetic resonance imaging (MRI) data from the two study cohorts using an automated image analysis pipeline and multivariate data analysis. Methods: A total of 664 subjects were included in this study (AddNeuroMed: 126 AD, 115 CTL, ADNI: 194 AD, 229 CTL) Data acquisition for the AddNeuroMed project was set up to be compatible with the ADNI study and the high resolution sagital 3D T1w MP-RAGE datasets used for image analysis. Regional segmentation of the brain was carried out using the multi-scale ANIMAL image analysis technique (Automated Non-linear Image Matching and Anatomical Labeling). Cortical thickness measurements were performed using CLASP. A total of 24 measures were pooled together for multivariate analysis using the OPLS method (orthogonal partial least squares). Models were created for the two cohorts and for the combined cohorts to discriminate between AD patients and controls. Finally the ADNI cohort was used as a replication dataset to validate the model created for the AddNeuroMed cohort. Results: Using cross-validation, we achieved the following values: AddNeuroMed cohort: sensitivity = 79%, specificity = 86%; ADNI cohort: sensitivity = 79%, specificity = 87%; both cohorts combined: sensitivity = 83%, specificity = 83%. Using the AddNeuroMed cohort as a training set and validating the model with the ADNI cohort resulted in a sensitivity of 78% and specificity of 87%. All three models created showed very similar results. Examples of important variables for discriminating between AD and CTL included temporal lobe grey matter volume, total CSF volume and mean cortical thickness. Conclusions: Multivariate data analysis is a powerful tool for distinguishing between different patient groups. The AddNeuroMed, ADNI and combined cohorts showed similar patterns of atrophy and the predictive power was very similar. This demonstrates that the methods used are robust and that large data sets can be combined if MRI imaging protocols are carefully aligned.
Original languageEnglish
Publication yearJul 2010
Number of pages1
Publication statusPublished - Jul 2010
EventInternational Conference on Alzheimer's Disease - Honolulu, United States
Duration: 10 Jul 201015 Jul 2010


ConferenceInternational Conference on Alzheimer's Disease
CountryUnited States

    Research areas

  • Alzheimer, MRI

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