Assessment of parameter settings for SPM5 spatial normalization of structural MRI data: application to type 2 diabetes.

Spatial normalization is the process of standardizing images of different subjects into the same anatomical space. The goal of this work was to assess standard and unified methods in SPM5 for the normalization of structural magnetic resonance imaging (MRI) data acquired in mid-life/elderly subjects with diabetes. In this work, we examined the impact of different parameters (i.e. nonlinear frequency cutoff, nonlinear regularization and nonlinear iterations) on the normalization, in terms of the residual variability. Total entropy was used to assess the residual anatomical variability after spatial normalization in a sample of 14 healthy mid-life/elderly control subjects and 24 mid-life/elderly subjects with type 2 diabetes. Spatial normalization was performed using default settings and by varying a single parameter or a combination of parameters. Descriptive statistics and nonparametric tests were used to examine differences in total entropy. Statistical parametric mapping analyses were performed to evaluate the influence of parameter settings on the spatial normalization. Total entropy results and SPM analyses suggest that the best parameters for the spatial normalization of mid-life/elderly image data to the MNI template, when applying the standard approach, correspond to the default cutoff (25 mm), heavy regularization, and the default number of nonlinear iterations (16). On the other hand, when applying the unified approach, the default parameters were the best for spatial normalization of mid-life/elderly image data to the MNI priors. These findings are relevant for studies of structural brain alterations that may occur in normal aging, chronic medical conditions, neuropsychiatric disorders, and neurodegenerative disorders.