Voxel-based morphometry

Over the past two decades, hundreds of studies have shed light on the neuroanatomical structural correlates of neurological and psychiatric disorders. Many of these studies were performed using voxel-based morphometry (VBM), a whole-brain technique for characterizing between groups' regional volume and tissue concentration differences from structural magnetic resonance imaging (MRI) scans. One of the first VBM studies and one that came to attention in mainstream media was a study on the hippocampus brain structure of London taxicab drivers. The VBM analysis showed the back part of the posterior hippocampus was on average larger in the taxi drivers compared to control subjects while the anterior hippocampus was smaller. London taxi drivers need good spatial navigational skills and scientists have usually associated hippocampus with this particular skill. Another famous VBM paper was a study on the effect of age on gray and white matter and CSF of 465 normal adults. The VBM analysis showed global gray matter was decreased linearly with age, especially for men, whereas global white matter did not decline with age. A key description of the methodology of voxel-based morphometry is Voxel-Based Morphometry—The Methods—one of the most cited articles in the journal NeuroImage. The usual approach for statistical analysis is mass-univariate (analysis of each voxel separately), but pattern recognition may also be used, e.g., for classifying patients from healthy. == For brain asymmetry ==

Usually VBM is performed for examining differences across subjects, but it may also be used to examine neuroanatomical differences between hemispheres detecting brain asymmetry. A technical procedure for such an investigation may use the following steps: • Construction of a study-specific brain image template with a balanced set of left and right handed and males and females. • Construction of white and grey matter templates from segmentation. • Construction of symmetric grey and white matter templates by averaging right and left cerebral hemispheres. • Segmentation and extraction of brain image, e.g., removal of scalp tissue in the image. • Spatial normalization to the symmetric templates • Correction for volume change (applying a Jacobian determinant) • Spatial smoothing (intensity in each voxel is a local weighted average generally expressed as GM, WM, CSF concentration). • Actual statistical analysis by the general linear model, i.e., statistical parametric mapping. The outcome of these steps is a statistical parametric map, highlighting all voxels of the brain where intensities (volume or GM concentration depending on whether the modulation step has been applied or not) in a group images are significantly lower/higher than those in the other group under investigation. == Compared to the region of interest approach ==

Before the advent of VBM, the manual delineation of region of interest was the gold standard for measuring the volume of brain structures. However, compared to the region of interest approach, VBM presents a large number of advantages that explain its wide popularity within the neuroimaging community. Indeed, it is an automated and relatively easy-to–use, time-efficient, whole-brain tool that could detect the focal microstructural differences in brain anatomy in vivo between groups of individuals without requiring any a priori decision concerning which structure to evaluate. Moreover, VBM exhibits comparable accuracy to manual volumetry. Indeed, several studies have shown good correspondence between the two techniques, providing confidence in the biological validity of the VBM approach. == See also ==