Extraction of discriminative functional MRI activation patterns and an application to Alzheimer's disease

Despina Kontos, Vasileios Megalooikonomou, Dragoljub Pokrajac, Alexandar Lazarevic, Zoran Obradovic, Orest B. Boyko, James Ford, Filia Makedon, Andrew J. Saykin

Research output: Contribution to journalConference article

16 Scopus citations

Abstract

We propose a novel Dynamic Recursive Partitioning approach for discovering discriminative patterns of functional MRI activation. The goal is to efficiently identify spatial regions that are associated with non-spatial variables through adaptive recursive partitioning of the 3D space into a number of hyper-rectangles utilizing statistical tests. As a case study, we analyze fMRI datasets obtained from a study that explores neuroanatomical correlates of semantic processing in Alzheimer's disease. We seek to discover brain activation areas that discriminate controls from patients. We evaluate the results by presenting classification experiments that utilize information extracted from these regions. The discovered areas elucidated large hemispheric and lobar differences being consistent with prior findings. The overall classification accuracy based on activation patterns in these areas exceeded 90%. The proposed approach being general enough has great potential for elucidating structure-function relationships and can be valuable to human brain mapping.

Original languageEnglish (US)
Pages (from-to)727-735
Number of pages9
JournalLecture Notes in Computer Science
Volume3217
Issue number1 PART 2
StatePublished - Dec 1 2004
EventMedical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings - Saint-Malo, France
Duration: Sep 26 2004Sep 29 2004

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Kontos, D., Megalooikonomou, V., Pokrajac, D., Lazarevic, A., Obradovic, Z., Boyko, O. B., Ford, J., Makedon, F., & Saykin, A. J. (2004). Extraction of discriminative functional MRI activation patterns and an application to Alzheimer's disease. Lecture Notes in Computer Science, 3217(1 PART 2), 727-735.