We also showcase our methodological contributions regarding corrections for common method variance (i.e., endogeneity) bias using two-stage least squares (2SLS) regression and Monte Carlo split-sample designs.
The discrepancy regarding the extent of cognitive deficits between previous studies and our results might be at least partially explained by methodological differences as correction for fine motor performance.
Moreover, methodological details regarding temperature corrections of arterial blood gas are missing in most of the studies.
Although a comparison between the early study of Jacobsson et al. [ 25] and our study reveals significant methodological differences (e.g., correction for attenuation, the number of time points studied), their effective dose estimates based on measurements with I-MIBG are of the same order of magnitude as those presented here.
Despite these correction factors, several methodological limitations persist [ 3, 22].
The discrepancy with our data could result from methodological differences (data preprocessing, artefacts correction and diffusion tensor imaging models applied) and small groups (n = 15) of patients with GTS included in these studies.
In this project, we will systematically review the methodological literature on measurement error correction methods and the nutritional epidemiological literature on calibration studies of dietary assessment, a field of central importance in the study of G × E interactions and for which harmonisation is greatly needed.
While minimizing NADs cannot be a sufficient criterion for gene tree correction, it should rather be seen as one among others, such as statistical (Wu et al., 2012), syntenic (Lafond et al., 2013) or based on reconciliation with the species tree (Chaudhary et al., 2011; Lafond et al., 2013; Swenson et al., 2010), that can be integrated in a methodological framework for gene tree correction.
Correction for head size is a controversial area [ 11, 12], and we suggest that it should be seen not only as a methodological or statistical issue, but that corrections may in fact obscure the very relationships they are attempting to investigate.
We also emphasize relevant methodological challenges, such as motion artifact correction, which are particularly important to consider when applying network tools to developmental neuroimaging data.
