Statistical analysis The sulcal shape descriptors

  • The influence of the global BMN-673 measurements such as total brain volume on local morphometric descriptors is a crucial issue that needs further clarification, with only few studies investigating this point so far. In this paper, we deal with four descriptors that may not be treated the same way when including global covariates. For instance, including the total surface as a covariate when analyzing surface-related descriptors seems natural but may not be appropriate when analyzing depth or length measurements. Therefore, different covariates might be included according to the type of descriptors considered, then hampering a between-descriptors comparison.
    Moreover, it seems plausible that the interaction between global measures and local sulcus descriptors may be different between ASD children and healthy subjects. Under this assumption, including global measures in the statistical model would bias the analysis and impede the sensitivity to the syndrome. This is why we chose an alternate strategy that consists in matching brain tissue volumes across the two groups of subjects in order to minimize such potential bias (see Section 2.1 and Table 2), thus allowing to apply the same statistical model to all four descriptors. Under this statement, spatial normalization is a way to compensate for global shape variations across individuals that has the advantage to correct for shape differences separately in each of the 3 directions (in 3D), thus yielding a more accurate correction than what would be done by including a global volumetric measurement in the statistical model, which is by essence isotropic, with a potential gain in sensitivity.