Pattern based norphometry

Bilwaj Gaonkar, Kilian Pohl, Christos Davatzikos

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)459-466
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6892 LNCS
Issue numberPART 2
DOIs
StatePublished - 2011
Externally publishedYes

Keywords

  • machine learning
  • pattern based morphometry
  • voxel based morphometry

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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