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

Abstract

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 were investigated.

Fig. 1

Auditory oddball experiment. Three different stimuli are represented with different colors and unevenly spaced to indicate the pseudorandom generation (Demirci et al. 2008)

Fig. 2

Representation of a block in SIRP (Sternberg Item Recognition Paradigm). Two blocks of each of the three conditions with {1, 3, 5} digits (in a pseudorandom order) constitute a run

Fig. 3

Representation of a block in Sensorimotor (SM) task

Fig. 4

Performance comparison of 3 different tasks (AOD, SIRP and SM) with temporal lobe activation networks for varying (M/2,M). P_All, P_FA and P_D are reported for predicted P_FA = 10% threshold. A t-test was employed to eliminate the number of voxels to consider on a group of 70 subjects from NM site

Fig. 5

Performance comparison of 3 different tasks (AOD, SIRP and SM) with temporal lobe activation networks. P_All, P_FA and P_D are reported for predicted P_FA = 10% threshold. A t-test was employed to eliminate the number of voxels to consider on a group of 138 subjects from NM and Iowa sites. 6000 voxels were used after elimination

Fig. 6

Performance comparison of 3 different tasks (AOD, SIRP and SM) with temporal lobe activation networks. P_All, P_FA and P_D are reported for predicted P_FA = 10% threshold. A t-test was employed to eliminate the number of voxels to consider on a group of 138 subjects from NM and Iowa sites. 8000 voxels were used after elimination

Fig. 7

Variability in the Performance of SM 18th component with 84 subjects (42 chronic patients, 42 healthy controls). The chronic patient set was kept the same but 15 different healthy control sets were determined (each with different 42 controls) using a total of 56 healthy controls. Variability in P_All, P_FA and P_D are reported for predicted P_FA = 10% threshold. A t-test was employed to eliminate the number of voxels

Fig. 8

Spatial representation of the maximum separation direction,ŭ, in the reduced dimensional space. Points A-C are used to illustrate difference(s) in the activation of patient with schizophrenia, average and healthy control (from top to bottom) with 3/6 principal components at slices 19, 20, 29 and 36 (right to left) among the 46. Point A represents schizophrenia, Point B represents average, and Point C represents Healthy Control. a 3D distribution. b Regenerated slices