Abstract

Voxel based morphometry (VBM) is widely used in the neuroimaging community to infer group differences in brain morphology. VBM is effective in quantifying group differences highly localized in space. However it is not equally effective when group differences might be based on interactions between multiple brain networks. We address this by proposing a new framework called pattern based morphometry (PBM). PBM is a data driven technique. It uses a dictionary learning algorithm to extract global patterns that characterize group differences. We test this approach on simulated and real data obtained from ADNI . In both cases PBM is able to uncover complex global patterns effectively.

Keywords

machine learning pattern based morphometry voxel based morphometry 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bilwaj Gaonkar
    • 1
  • Kilian Pohl
    • 1
  • Christos Davatzikos
    • 1
  1. 1.Section for Biomedical Image AnalysisUniversity of PennsylvaniaUSA

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