Other studies suggest that the temporal lobe and nearby regions are troublesome areas of the brain for FS to measure accurately ( Desikan et al., 2006 Oguz et al., 2008). Closer inspection revealed that this was due to inclusions of surrounding high intensity voxel structures as well as misidentification of pockets of cerebrospinal fluid as hippocampal tissue ( Cherbuin et al., 2009). (2009) showed that absolute hippocampal volumes measured with FS were significantly larger than those of manual tracings, with reported 23 and 29% overestimation of left and right hippocampal volumes, respectively. However, strictly implementing the automated procedures in FS can result in variability in the accuracy of segmentation for some ROIs. FS has also been shown to be a highly reliable method for automated cortical thickness measurements across scanner strength and pulse sequence in all regions of the brain, with minor variability being attributed to cytoarchitectural differences of certain ROIs and difficulties with surface reconstructions in temporal lobe regions ( Han et al., 2006 Fjell et al., 2009). In general, validation studies have demonstrated that FS can produce measurements that are comparable to those derived from manual tracing of brain regions ( Fischl et al., 2002 Tae et al., 2008 Bhojraj et al., 2011). FS offers consistency in its fully automated processing, which is ideal for either single- or multi-site studies with large sample sizes. Manual measurement of the volumes of specific ROIs is an arduous, labor-intensive task, and is subject to inter-rater variability. Another critical function that FS provides is the ability to construct surface-based representations of the cortex, from which cortical thickness, neuroanatomic volume, and surface area can be derived. The first stage performs skull stripping and motion artifact correction, the second performs gray-white matter segmentation ( Fischl et al., 2002), and the third segments 34 ROIs based on anatomic landmarks ( Desikan et al., 2006). FS was designed around an automated workflow that encompasses several standard image processing steps necessary to achieve a final brain parcellation within the subject's space however, manual image editing is allowed after each stage to ensure quality control. Potential exceptions to and limitations of these conclusions are discussed.įreeSurfer 1 (FS) is a freely available fully automated brain image morphometric software package that allows for the measurement of neuroanatomic volume, cortical thickness, surface area, and cortical gyrification of regions of interest (ROIs) throughout the brain. Taken together, these results suggest that although the application of control points may increase the validity of intensity normalization and, ultimately, segmentation, it may not affect the final, extracted metrics that FS generates. ![]() Mean effect sizes for edited ROIs did not differ from most unedited ROIs for either 1.5 or 3 tesla data. For cortical thickness measures however, seven edited ROI measures, primarily in frontal and temporal regions, differed significantly from their unedited counterparts, and three additional ROI measures approached significance. Results were similar for surface area and white matter volumes generated from the 3 tesla scanner. ![]() For 1.5 tesla data, mean area, volume, and thickness measures did not differ significantly between edited and unedited regions, with the exception of rostral anterior cingulate thickness, lateral orbitofrontal white matter, superior parietal white matter, and precentral gyral thickness. Analyses were conducted with data that were generated from both a 1.5 tesla and a 3 tesla scanner. ![]() To determine whether applying control points would alter the detection of significant differences between patient and typical groups, effect sizes between edited and unedited conditions in individuals with the genetic disorder, 22q11.2 deletion syndrome (22q11DS) were compared to neurotypical controls. ![]() In a sample of 30 individuals, we compared the summary statistics of surface area, white matter volumes, and cortical thickness derived from edited and unedited datasets for the 34 regions of interest (ROIs) that FreeSurfer (FS) generates. This paper examined whether FreeSurfer-generated data differed between a fully-automated, unedited pipeline and an edited pipeline that included the application of control points to correct errors in white matter segmentation.
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