Brain normalization is an important step for whole-brain multi-subject (group) analyses allowing averaging data across subjects by establishing spatial correspondence between brains. Brain normalization is, however, also important for individual subject data since it allows to report observed activated regions in a standard spatial coordinate system. Brain normalization in volume space is usually performed by warping each brain into a (more or less) common space. After brain normalization, a point in the common space identified by its x, y, z coordinates is assumed to refer to a similar region in any brain normalized according to the same procedure. The most commonly used target space for normalization is the MNI space and the Talairach space (Talairach & Tournaux, 1988) that are closely related. Unfortunately warping brains in a common space does not solve the anatomical correspondence problem very well, i.e. macroanatomical structures, such as banks of prominent sulci are often still misaligned with deviations in the order of 5-10 mm. In order to increase the chance that corresponding regions overlap, functional data is therefore often smoothed with a Gaussian kernel with a full width at half maximum of about 1 cm. More advanced anatomical matching schemes, e.g. cortex-based alignment, attempt to directly align macroanatomical structures such as gyri and sulci.
Brain normalization is a challenging fundamental problem of neuroscience going beyond pure anatomical correspondence since it includes the question about the consistency of structure-function relationships. At the anatomical level, the correspondence problem refers to the differences in brain shape, and more specifically, to differences in the gyral and sulcal pattern varying substantially across subjects. At this macroanatomical level, the correspondence problem would be solved, if brains could be matched in such a way that each macroanatomical structure in one brain would be mapped to the same marcoanatomical structure in any other brain. The deeper version of the correspondence problem addresses the fundamental question about the consistency of relationships between certain brain functions and macroanatomical structures across brains. While neuroimaging has successfully demonstrated that there are common structure-function relationships across brains, a high level of variability has also been observed. A more satisfying answer to this fundamental question might only emerge after systematic investigations of the question how well functionally localized regions do respect specific macroanatomical landmarks (e.g. Frost & Goebel, 2011). An interesting approach to the functional correspondence problem has been proposed that does circumvent the whole-brain anatomical alignment and "aligns" data from brain regions that are activated by the same task across particpants; in this region-of-interest (ROI) alignment approach, the functional data in roughly similar regions are used to define homologue brain areas.
BrainVoyager provides both conventional alignment procedures in volume space as well as advanced alignment in cortex space. For basic data analysis, the MNI template-based normalization or classical Talairach brain normalization procedures are provided. MNI/Talairach normalized brains are used also as the starting point of the more precise cortex-based macroanatomical alignment procedure.
Talairach & Tournaux (1988)
Frost, M. & Goebel, R. (2011).
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