Diffusion tensor imaging reliably differentiates patients with schizophrenia from healthy volunteers

Abstract

The objective of this research was to determine whether fractional anisotropy (FA) and mean diffusivity (MD) maps derived from diffusion tensor imaging (DTI) of the brain are able to reliably differentiate patients with schizophrenia from healthy volunteers. DTI and high resolution structural magnetic resonance scans were acquired in 50 patients with schizophrenia and 50 age- and sex-matched healthy volunteers. FA and MD maps were estimated from the DTI data and spatially normalized to the Montreal Neurologic Institute standard stereotactic space. Individuals were divided randomly into two groups of 50, a training set, and a test set, each comprising 25 patients and 25 healthy volunteers. A pattern classifier was designed using Fisher's linear discriminant analysis (LDA) based on the training set of images to categorize individuals in the test set as either patients or healthy volunteers. Using the FA maps, the classifier correctly identified 94% of the cases in the test set (96% sensitivity and 92% specificity). The classifier achieved 98% accuracy (96% sensitivity and 100% specificity) when using the MD maps as inputs to distinguish schizophrenia patients from healthy volunteers in the test dataset. Utilizing FA and MD data in combination did not significantly alter the accuracy (96% sensitivity and specificity). Patterns of water self-diffusion in the brain as estimated by DTI can be used in conjunction with automated pattern recognition algorithms to reliably distinguish between patients with schizophrenia and normal control subjects.

Figure 1

Scatter plot of FA and MD discriminant scores for the 50 training subjects. The vertical axis is the decision boundary for the FA score. The horizontal axis is the decision boundary of the MD score. The diagonal line is the decision boundary of the second level linear classifier that combines the FA and MD scores. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

Figure 2

Scatter plot of FA and MD discriminant scores for the 50 test subjects. The decision boundaries are the same as in Figure 1. One patient (circle in first quadrant) is misclassified based on both their FA and MD features. Two healthy volunteers (crosses in the second quadrant) are misclassified based on their FA but not MD. When the bimodal classifier is used, one of the two healthy volunteers is classified correctly (represented by the cross in the second quadrant to the right of the diagonal line). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

Figure 3

Selected slices from the FA (a–e) and MD (f–j) “Fisher brains” superimposed on the average SPGR images of all subjects. From left to right, the axial slices represent sections −10, −5, 0, 5, and 10 mm above the AC–PC plane. The Fisher brains are thresholded at 30% of their maximum absolute value. In both the FA and MD images, higher values in yellow regions favor classification of an individual as a healthy volunteer whereas higher values in blue regions favor classification of an individual as a patient.