Abstract

In recent years, a whole-brain unbiased objective technique, known as voxel-based morphometry (VBM), has been developed to characterise brain differences in vivo using structural magnetic resonance images. The present review provides a brief description of VBM and then focuses on exemplar applications in healthy and diseased subjects. The procedure involves normalising high-resolution structural magnetic resonance images to a standard template in stereotactic space. Normalised images are then segmented into gray and white matter and smoothed using an isotropic Gaussian kernel. Finally, a series of voxel-wise comparisons of gray and white matter in different groups of subjects are performed, using Random Field theory to correct for multiple comparisons. VBM has been useful in characterizing subtle changes in brain structure in a variety of diseases associated with neurological and psychiatric dysfunction. These include schizophrenia, developmental and congenital disorders, temporal lobe epilepsy and even cluster headache. In addition, VBM has been successful in identifying gross structural abnormalities, such as those observed in herpes simplex encephalitis, multiple sclerosis and Alzheimers disease. Studies of normal subjects, on the other hand, have focussed on the impact of learning and practice on brain structure. These studies have led to the finding that environmental demands may be associated with changes in gray and white matter. For instance, it has been reported that the structure of the brain alters when human beings learn to navigate, read music, speak a second language and even perform a complex motor task such as juggling. We conclude the present review by discussing the potential limitations of the technique.

Keywords: voxel-based morphometry, magnetic resonance imaging, statistical parametric mapping, gray matter, white matter