2.5. Data pre-processing and statistical analyses
Preprocessing and statistical analyses were conducted using SPM12
(http://www.l.ion.ucl.ac.uk/spm/software/spm12/). Functional data
preprocessing included slice time correction, realignment to the first
volume, and spatial normalization to the University of North Carolina at
Chapel Hill neonate atlas (Shi et al., 2011). Motion artifacts were
examined using the Artifact Detection Toolbox (ART) (https://
www.nitrc.org/projects/artifact_detect/). Volumes where global signal
deviated more than two standard deviations from the mean signal or where
the difference in motion between two neighboring volumes exceeded 1 mm
were classified as outlier volumes. Subjects were excluded if the number
of outliers in the fMRI data exceeded 30% of either rest or brushing
blocks. The stimuli were modeled as one predictor convolved with the
standard SPM12 hemodynamic response function. A fixed effects general
linear model (GLM) analysis, including motion parameters and outlier
volumes as regressors of no interest, was performed in each individual
infant. The images of this first-level analysis were then used for the
second-level group statistics in a new GLM. First, we ran a one-sample t
test, controlling for infants’ gestational weeks and age at scan from
birth, to test the effect of gentle stroking stimulation in the whole
sample (N = 18). An a priori primary threshold for voxel-level
statistical significance was set to p < 0.01 and
results were FDR corrected at the cluster level (pFDR < 0.05),
and a secondary threshold was set at p < 0.05, FDR corrected
at the cluster level.
All the models were tested with the same thresholds also in a smaller
group (N = 13), which is part of the whole sample (N = 18), in order to
test the same models in slightly different samples and see if results
would change. Results from the smaller sample (N = 13) are reported in
Supplementary Material. Data from this smaller sample size was collected
between the data included in the previous study conducted by Tuulari et
al. (2019) and the final sample size (N = 18) presented here.