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Classification of Major Depressive Disorder via Multi-site Weighted LASSO Model

  • Dajiang ZhuEmail author
  • Brandalyn C. Riedel
  • Neda Jahanshad
  • Nynke A. Groenewold
  • Dan J. Stein
  • Ian H. Gotlib
  • Matthew D. Sacchet
  • Danai Dima
  • James H. Cole
  • Cynthia H. Y. Fu
  • Henrik Walter
  • Ilya M. Veer
  • Thomas Frodl
  • Lianne Schmaal
  • Dick J. Veltman
  • Paul M. Thompson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

Abstract

Large-scale collaborative analysis of brain imaging data, in psychiatry and neurology, offers a new source of statistical power to discover features that boost accuracy in disease classification, differential diagnosis, and outcome prediction. However, due to data privacy regulations or limited accessibility to large datasets across the world, it is challenging to efficiently integrate distributed information. Here we propose a novel classification framework through multi-site weighted LASSO: each site performs an iterative weighted LASSO for feature selection separately. Within each iteration, the classification result and the selected features are collected to update the weighting parameters for each feature. This new weight is used to guide the LASSO process at the next iteration. Only the features that help to improve the classification accuracy are preserved. In tests on data from five sites (299 patients with major depressive disorder (MDD) and 258 normal controls), our method boosted classification accuracy for MDD by 4.9% on average. This result shows the potential of the proposed new strategy as an effective and practical collaborative platform for machine learning on large scale distributed imaging and biobank data.

Keywords

MDD Weighted LASSO 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Dajiang Zhu
    • 1
    Email author
  • Brandalyn C. Riedel
    • 1
  • Neda Jahanshad
    • 1
  • Nynke A. Groenewold
    • 2
    • 3
    • 4
  • Dan J. Stein
    • 4
  • Ian H. Gotlib
    • 5
  • Matthew D. Sacchet
    • 6
  • Danai Dima
    • 7
    • 8
  • James H. Cole
    • 9
  • Cynthia H. Y. Fu
    • 10
  • Henrik Walter
    • 11
  • Ilya M. Veer
    • 12
  • Thomas Frodl
    • 12
    • 13
  • Lianne Schmaal
    • 14
    • 15
    • 16
  • Dick J. Veltman
    • 16
  • Paul M. Thompson
    • 1
  1. 1.Keck School of Medicine, Imaging Genetics Center, USC Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.BCN NeuroImaging Center and Department of NeuroscienceUniversity of GroningenGroningenThe Netherlands
  3. 3.University Medical Center GroningenGroningenThe Netherlands
  4. 4.Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
  5. 5.Neurosciences Program, and Department of PsychologyStanford UniversityStanfordUSA
  6. 6.Department of Psychiatry and Behavioral SciencesStanford UniversityStanfordUSA
  7. 7.Department of Neuroimaging, Institute of Psychiatry, Psychology and NeuroscienceKing’s College LondonLondonUK
  8. 8.Department of Psychology, School of Arts and Social Science, CityUniversity of LondonLondonUK
  9. 9.Department of MedicineImperial College LondonLondonUK
  10. 10.Department of Psychological MedicineKing’s College LondonLondonUK
  11. 11.Department of Psychiatry and PsychotherapyCharité Universitätsmedizin BerlinBerlinGermany
  12. 12.Department of PsychiatryTrinity College DublinDublinIreland
  13. 13.Department of Psychiatry and PsychotherapyOtto von Guericke University MagdeburgMagdeburgGermany
  14. 14.Department of Psychiatry and Neuroscience Campus AmsterdamVU University Medical CenterAmsterdamThe Netherlands
  15. 15.Orygen, The National Centre of Excellence in Youth Mental HealthParkvilleAustralia
  16. 16.Center for Youth Mental HealthThe University of MelbourneMelbourneAustralia

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