The article discusses the importance of accurate lesion localization in neuropsychological studies and the challenges associated with traditional methods of presenting anatomical data. Traditional methods include using standard brain templates, text-based descriptions, or raw slices from CT or MRI scans. These methods have limitations, such as difficulty in comparison across patients and lack of alignment with standard neuroimaging atlases. An alternative method, called spatial normalization, is introduced, which aligns patient scans to a standard stereotaxic space, allowing for direct comparison of lesion position and volume across patients. This method is more accurate and facilitates integration with other neuroimaging techniques like fMRI, PET, and ERP. Spatial normalization involves scaling, rotating, and warping the patient's MRI scan to align it with a standard atlas. This process is computationally intensive but has become more automated and accessible. The article highlights the benefits of this technique, including improved lesion identification, more meaningful lesion volume measurements, and easier comparison across patients and studies. It also discusses the technical challenges that have hindered the adoption of this method, such as the availability of high-quality MRI scans, the complexity of the algorithms, and the need for specialized software. Recent advances have addressed these challenges, making spatial normalization more feasible. Free software like MRIcro and SPM99 have been developed, allowing neuropsychologists to perform normalization on desktop computers. These tools enable the conversion of proprietary medical image formats to standard scientific formats, facilitating the use of normalized scans in research. The article concludes that spatial normalization is a robust method for lesion studies and can enhance the understanding of the anatomical basis of neurological syndromes in cognitive neuroscience.The article discusses the importance of accurate lesion localization in neuropsychological studies and the challenges associated with traditional methods of presenting anatomical data. Traditional methods include using standard brain templates, text-based descriptions, or raw slices from CT or MRI scans. These methods have limitations, such as difficulty in comparison across patients and lack of alignment with standard neuroimaging atlases. An alternative method, called spatial normalization, is introduced, which aligns patient scans to a standard stereotaxic space, allowing for direct comparison of lesion position and volume across patients. This method is more accurate and facilitates integration with other neuroimaging techniques like fMRI, PET, and ERP. Spatial normalization involves scaling, rotating, and warping the patient's MRI scan to align it with a standard atlas. This process is computationally intensive but has become more automated and accessible. The article highlights the benefits of this technique, including improved lesion identification, more meaningful lesion volume measurements, and easier comparison across patients and studies. It also discusses the technical challenges that have hindered the adoption of this method, such as the availability of high-quality MRI scans, the complexity of the algorithms, and the need for specialized software. Recent advances have addressed these challenges, making spatial normalization more feasible. Free software like MRIcro and SPM99 have been developed, allowing neuropsychologists to perform normalization on desktop computers. These tools enable the conversion of proprietary medical image formats to standard scientific formats, facilitating the use of normalized scans in research. The article concludes that spatial normalization is a robust method for lesion studies and can enhance the understanding of the anatomical basis of neurological syndromes in cognitive neuroscience.