On the Spectrum

Chapter 4
data were aligned into a common space (FMRIB58 FA map) using the nonlinear registration
tool FNIRT (Andersson 2007; Andersson, Jenkinson and Smith 2007). The resulting non linear
warp fields for the FA maps were then applied to MD maps. Next, the mean FA image was
created and thresholded to create a mean FA skeleton, which is the area representing the
geometric center of all tracts common to the sample. A threshold of FA > 0.2 was applied to
the skeleton to include only major fiber bundles. Each subject’s aligned FA/MD data was then
projected onto this skeleton and the resulting data were fed into voxel-wise cross-subject
statistics (see below for details).
Covariates
Covariates were chosen a priori based on confounding effects in a previous study of gyrification
and autistic traits in this sample (Blanken et al. 2015). Sex, age at MRI scanning and age at
administration of the SRS were default covariates. Maternal education and history of smoking
and alcohol during pregnancy were assessed by questionnaires. The child’s ethnic background
was defined based on the country of birth of both parents. Maternal education was defined
by the highest completed education. Maternal smoking was assessed at enrollment and in
mid- and late pregnancy.
Non-verbal intelligence of the child was assessed at approximately 6 years of age using
two subtests of the Snijders-Oomen Niet-verbale intelligentie Test – Revisie (SON-R 2.5–7),
a non-verbal intelligence test suited for children between 2.5–7 years of age (Tellegen,
Wijnberg-Williams and Laros 2005). Child attention problems, which often occur with autistic
traits (Grzadzinski, Di Martino, Brady, Mairena, O’Neale et al. 2011), were assessed at age 6
using the CBCL/ 1.5-5 (Achenbach and Rescorla 2000).
Statistical analysis
Statistical analyses were conducted using the R Statistical Software version 3.1.3 (R Core Team,
2014), SPSS version 21 (IBM Corp., Armonk, NY, USA), and FSL’s built-in tool “Randomise”
(Winkler, Ridgway, Webster, Smith and Nichols 2014). For the tractography approach, the
Lavaan package (Rosseel 2012) was used to compute global DTI measures using confirmatory
factor analysis. The details of this approach have been described elsewhere (Muetzel et al.
2015). Briefly, separately for FA and MD, multiple tracts were summarized as a single latent
factor, and the predicted factor scores for each subject were generated.
In TBSS, FSL’s tool “Randomise” was used to perform voxel-wise analyses (Winkler et al.
2014). A total of 5000 permutations were run per analysis, and family-wise error corrected
p-values were used to evaluate significant clusters (p FWE