Feasibility of FreeSurfer Processing for T1-Weighted Brain Images of 5-Year-Olds: Semiautomated Protocol of FinnBrain Neuroimaging Lab - Pulli, Elmo P.; Silver, Eero; Kumpulainen, Venla; Copeland, Anni; Merisaari, Harri; Saunavaara, Jani; Parkkola, Riitta; Lähdesmäki, Tuire; Saukko, Ekaterina; Nolvi, Saara; Kataja, Eeva-Leena; Korja, Riikka; et al. (Frontiers in Neuroscience, 2022)
Pediatric neuroimaging is a quickly developing field that still faces important methodological challenges. Pediatric images usually have more motion artefact than adult images. The artefact can cause visible errors in brain segmentation, and one way to address it is to manually edit the segmented images. Variability in editing and quality control protocols may complicate comparisons between studies. In this article, we describe in detail the semiautomated segmentation and quality control protocol of structural brain images that was used in FinnBrain Birth Cohort Study and relies on the well-established FreeSurfer v6.0 and ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium tools. The participants were typically developing 5-year-olds (n = 134, 5.34 (SD 0.06) years, 62 girls). Following a dichotomous quality rating scale for inclusion and exclusion of images, we explored the quality on a region of interest level to exclude all regions with major segmentation errors. The effects of manual edits on cortical thickness values were relatively minor: less than 2% in all regions. Supplementary materials cover registration and additional edit options in FreeSurfer and comparison to the computational anatomy toolbox (CAT12). Overall, we conclude that despite minor imperfections FreeSurfer can be reliably used to segment cortical metrics from T1-weighted images of 5-year-old children with appropriate quality assessment in place. However, custom templates may be needed to optimize the results for the subcortical areas. Through visual assessment on a level of individual regions of interest, our semiautomated segmentation protocol is hopefully helpful for investigators working with similar data sets, and for ensuring high quality pediatric neuroimaging data.