Investigating the Added Value of FreeSurfer's Manual Editing Procedure for the Study of the Reading Network in a Pediatric Population

Abstract

Insights into brain anatomy are important for the early detection of neurodevelopmental disorders, such as dyslexia. FreeSurfer is one of the most frequently applied automatized software tools to study brain morphology. However, quality control of the outcomes provided by FreeSurfer is often ignored and could lead to wrong statistical inferences. Additional manual editing of the data may be a solution, although not without a cost in time and resources. Past research in adults on comparing the automatized method of FreeSurfer with and without additional manual editing indicated that although editing may lead to significant differences in morphological measures between the methods in some regions, it does not substantially change the sensitivity to detect clinical differences. Given that automated approaches are more likely to fail in pediatric-and inherently more noisy-data, we investigated in the current study whether FreeSurfer can be applied fully automatically or additional manual edits of T1-images are needed in a pediatric sample. Specifically, cortical thickness and surface area measures with and without additional manual edits were compared in six regions of interest (ROIs) of the reading network in 5-to-6-year-old children with and without dyslexia. Results revealed that additional editing leads to statistical differences in the morphological measures, but that these differences are consistent across subjects and that the sensitivity to reveal statistical differences in the morphological measures between children with and without dyslexia is not affected, even though conclusions of marginally significant findings can differ depending on the method used. Thereby, our results indicate that additional manual editing of reading-related regions in FreeSurfer has limited gain for pediatric samples.

Keywords: FreeSurfer; automated processing; developmental neuroimaging; manual editing; pediatric T1-weighted images; reading network.

Figure 1

(A) Example of a subject (B34) with a clear difference in mean surface area of the left fusiform gyrus between the methods. Red lines represent the pial surface [i.e., the border between gray matter and cerebrospinal fluid (CSF)] and dark blue lines the white surface (i.e., the border between white matter and gray matter, the surface area). The subject has a lower mean surface area of the left fusiform gyrus (see the blue arrow) after full-automatic processing (left) as opposed to after additional manual editing (right). (B) The mean surface area of the left fusiform gyrus per subject for the fully automated method and the automated method with additional edits. The intra-class correlation (ICC) corresponds to the difference in surface area between the methods across subjects (ICC value = 0.939, see Table 2A). The red arrow and circle indicate for both methods the mean surface area of the left fusiform gyrus for subject B34. (C) Effect sizes (Hedges g’) of the mean difference in surface area of the left fusiform gyrus between children with (DR) and without (TR) dyslexia for the fully automated method (g = 0.994) and the automated method with additional edits (g = 1.059).