Voxel-based morphometry (VBM) is a whole-brain unbiased objective technique that uses structural magnetic resonance imaging (MRI) to characterize brain differences in vivo. The method involves spatially normalizing high-resolution MRI images to a standard template, segmenting the images into gray and white matter, smoothing them, and performing voxel-wise statistical analysis to detect differences between groups. VBM has been applied to study structural brain differences in various diseases, including schizophrenia, developmental disorders, temporal lobe epilepsy, and Alzheimer's disease. It has also been used to investigate the impact of learning and practice on brain structure, revealing that environmental demands can lead to changes in gray and white matter. VBM can detect both subtle and gross structural abnormalities, but it has limitations, such as potential confounds from spatial normalization and segmentation errors. The method has been used to compare different brain structures and to assess the relationship between brain structure and variables such as language proficiency and age of acquisition. However, VBM may not always accurately detect changes in gray or white matter volume, and its results can be influenced by factors such as the size of the brain and the degree of spatial normalization. VBM is a valuable tool for studying brain structure and function, but it requires careful interpretation to avoid misinterpretation of results.Voxel-based morphometry (VBM) is a whole-brain unbiased objective technique that uses structural magnetic resonance imaging (MRI) to characterize brain differences in vivo. The method involves spatially normalizing high-resolution MRI images to a standard template, segmenting the images into gray and white matter, smoothing them, and performing voxel-wise statistical analysis to detect differences between groups. VBM has been applied to study structural brain differences in various diseases, including schizophrenia, developmental disorders, temporal lobe epilepsy, and Alzheimer's disease. It has also been used to investigate the impact of learning and practice on brain structure, revealing that environmental demands can lead to changes in gray and white matter. VBM can detect both subtle and gross structural abnormalities, but it has limitations, such as potential confounds from spatial normalization and segmentation errors. The method has been used to compare different brain structures and to assess the relationship between brain structure and variables such as language proficiency and age of acquisition. However, VBM may not always accurately detect changes in gray or white matter volume, and its results can be influenced by factors such as the size of the brain and the degree of spatial normalization. VBM is a valuable tool for studying brain structure and function, but it requires careful interpretation to avoid misinterpretation of results.