A Voxel-Based Method for the Statistical Analysis of Gray and White Matter Density Applied to Schizophrenia

We describe a novel technique for characterizing regional cerebral gray and white matter differences in structural magnetic resonance images by the application of methods derived from functional imaging. The technique involves automatic scalp-editing of images followed by segmentation, smoothing, and spatial normalization to a symmetrical template brain in stereotactic Talairach space. The basic idea is (i) to convert structural magnetic resonance image data into spatially normalized images of gray (or white) matter density, effected by segmenting the images and smoothing, and then (ii) to use Statistical Parametric Mapping to make inferences about the relationship between gray (or white) matter density and symptoms (or other pathophysiological measures) in a regionally specific fashion. Because the whole brain sum of gray (or white) matter indices is treated as a confound, the analysis reduces to a characterization of relative gray (or white) matter density on a voxel by voxel basis. We suggest that this is a powerful approach to voxel-based statistical anatomy. Using the technique, we constructed maps of the regional cerebral gray and white matter density correlates of syndrome scores (distinct psychotic symptoms) in a group of 15 schizophrenic patients. There was a negative correlation between the score for the reality distortion syndrome and regional gray matter density in the left superior temporal lobe (P = 0.01) and regional white matter density in the corpus callosum (P < 0.001). These abnormalities may be associated with functional changes predisposing to auditory hallucinations and delusions. This method permits the detection of structural differences within the entire brain (as opposed to selected regions of interest) and may be of value in the investigation of structural gray and white matter abnormalities in a variety of brain diseases.