Processing pediatric neuroimaging data is a challenge due to pervasive morphological changes that occur in the human brain during normal development. This is of special relevance when reference data is used as part of the processing approach, as in spatial normalization and tissue segmentation. Current approaches construct reference data (templates) by averaging brain images from a control group of subjects, or by creating custom templates from the group under study. In this technical note, we describe a new, and generalized method of constructing such appropriate reference data by statistically analyzing a large sample (n=404) of healthy children, as acquired during the NIH MRI study of normal brain development. After eliminating non-contributing demographic variables, we modeled the effects of age (first, second, and third-order terms) and gender, for each voxel in gray matter and white matter. By appropriate weighting with the parameter estimates from these analyses, complete tissue maps can be generated automatically from this database to match a pediatric population selected for study. The algorithm is implemented in the form of a toolbox for the SPM5 image data processing suite, which we term Template-O-Matic. We compare the performance of this approach with the current method of template generation and discuss the implications of our approach.
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