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A Review of Challenges in the Use of fMRI for Disease Classification / Characterization and A Projection Pursuit Application from A Multi-site fMRI Schizophrenia Study

  • Demirci, Oguz1, 2
  • Clark, Vincent P.1, 3
  • Magnotta, Vincent A.4
  • Andreasen, Nancy C.5
  • Lauriello, John6
  • Kiehl, Kent A.1, 3
  • Pearlson, Godfrey D.7, 8
  • Calhoun, Vince D.1, 7, 9, 10
  • 1 MIND Research Network, Albuquerque, NM, 87131, USA , Albuquerque (United States)
  • 2 Sony Electronics Inc., A-cubed Research Center, San Jose, CA, 95112, USA , San Jose (United States)
  • 3 UNM, Department of Psychology, Albuquerque, NM, 87131, USA , Albuquerque (United States)
  • 4 University of Iowa, Department of Radiology, Iowa City, IA, 52242, USA , Iowa City (United States)
  • 5 University of Iowa, Department of Psychiatry, Iowa City, IA, 52242, USA , Iowa City (United States)
  • 6 UNM, Department of Psychiatry, Albuquerque, NM, 87131, USA , Albuquerque (United States)
  • 7 Olin Neuropsychiatry Research Center, Hartford, CT, 06106, USA , Hartford (United States)
  • 8 Yale University School of Medicine, Department of Psychiatry, New Haven, CT, 06511, USA , New Haven (United States)
  • 9 UNM, Department of Electrical and Computer Engineering, Albuquerque, NM, 87131, USA , Albuquerque (United States)
  • 10 Yale University, Department of Psychiatry, New Haven, CT, 06511, USA , New Haven (United States)
Published Article
Brain Imaging and Behavior
Publication Date
Aug 09, 2008
DOI: 10.1007/s11682-008-9028-1
Springer Nature


Functional magnetic resonance imaging (fMRI) is a fairly new technique that has the potential to characterize and classify brain disorders such as schizophrenia. It has the possibility of playing a crucial role in designing objective prognostic/diagnostic tools, but also presents numerous challenges to analysis and interpretation. Classification provides results for individual subjects, rather than results related to group differences. This is a more complicated endeavor that must be approached more carefully and efficient methods should be developed to draw generalized and valid conclusions out of high dimensional data with a limited number of subjects, especially for heterogeneous disorders whose pathophysiology is unknown. Numerous research efforts have been reported in the field using fMRI activation of schizophrenia patients and healthy controls. However, the results are usually not generalizable to larger data sets and require careful definition of the techniques used both in designing algorithms and reporting prediction accuracies. In this review paper, we survey a number of previous reports and also identify possible biases (cross-validation, class size, e.g.) in class comparison/prediction problems. Some suggestions to improve the effectiveness of the presentation of the prediction accuracy results are provided. We also present our own results using a projection pursuit algorithm followed by an application of independent component analysis proposed in an earlier study. We classify schizophrenia versus healthy controls using fMRI data of 155 subjects from two sites obtained during three different tasks. The results are compared in order to investigate the effectiveness of each task and differences between patients with schizophrenia and healthy controls are investigated.

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