Dataset Information

Gray matter volume correlates of adolescent posttraumatic stress disorder: A comparison of manual intervention and automated segmentation in FreeSurfer.

ABSTRACT: Exposure to early life trauma is common and confers risk for psychological disorders in adolescence, including posttraumatic stress disorder (PTSD). Trauma exposure and PTSD are also consistently linked to alterations in gray matter volume (GMV). Despite the quantity of structural neuroimaging research in trauma-exposed populations, little consensus exists amongst research groups on best practices for image processing method and manual editing procedures. The purpose of this report is to evaluate the utility of manual editing of magnetic resonance (MR) images for detecting PTSD-related group differences in GMV. Here, T1-weighted MR images from adolescent girls aged 11-17 were obtained and analyzed. Two datasets were created from the FreeSurfer reconall pipeline, one of which was manually edited by trained research assistants. Gray matter regions of interest were selected and total volume estimates were entered into linear mixed effects models with method (manual edits or automated) as a within-subjects factor and group dummy-coded with PTSD as the reference group. Consistent with prior literature, individuals with PTSD demonstrated reduced GMV of the amygdala compared to trauma-exposed and non-trauma exposed controls, independent of editing method. Our results demonstrate that amygdala GMV reductions in PTSD are robust to certain methodological choices and do not suggest a benefit to the time-intensive manual editing pipeline in FreeSurfer for quantifying PTSD-related GMV.

SUBMITTER: Ross MC

PROVIDER: S-EPMC8205994 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Gray matter volume correlates of adolescent posttraumatic stress disorder: A comparison of manual intervention and automated segmentation in FreeSurfer.

Ross Marisa C MC Dvorak Delaney D Sartin-Tarm Anneliis A Botsford Chloe C Cogswell Ian I Hoffstetter Ashley A Putnam Olivia O Schomaker Chloe C Smith Penda P Stalsberg Anna A Wang Yunling Y Xiong Megan M Cisler Josh M JM

Psychiatry research. Neuroimaging 20210424

Exposure to early life trauma is common and confers risk for psychological disorders in adolescence, including posttraumatic stress disorder (PTSD). Trauma exposure and PTSD are also consistently linked to alterations in gray matter volume (GMV). Despite the quantity of structural neuroimaging research in trauma-exposed populations, little consensus exists amongst research groups on best practices for image processing method and manual editing procedures. The purpose of this report is to evaluat ...[more]

PMID: 33962164

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Project description:Background and purposeWM lesion segmentation is often performed with the use of subjective rating scales because manual methods are laborious and tedious; however, automated methods are now available. We compared the performance of total lesion volume grading computed by use of an automated WM lesion segmentation algorithm with that of subjective rating scales and expert manual segmentation in a cohort of subjects with type 2 diabetes.Materials and methodsStructural T1 and FLAIR MR imaging data from 50 subjects with diabetes (age, 67.7 ± 7.2 years) and 50 nondiabetic sibling pairs (age, 67.5 ± 9.4 years) were evaluated in an institutional review board-approved study. WM lesion segmentation maps and total lesion volume were generated for each subject by means of the Statistical Parametric Mapping (SPM8) Lesion Segmentation Toolbox. Subjective WM lesion grade was determined by means of a 0-9 rating scale by 2 readers. Ground-truth total lesion volume was determined by means of manual segmentation by experienced readers. Correlation analyses compared manual segmentation total lesion volume with automated and subjective evaluation methods.ResultsCorrelation between average lesion segmentation and ground-truth total lesion volume was 0.84. Maximum correlation between the Lesion Segmentation Toolbox and ground-truth total lesion volume (ρ = 0.87) occurred at the segmentation threshold of k = 0.25, whereas maximum correlation between subjective lesion segmentation and the Lesion Segmentation Toolbox (ρ = 0.73) occurred at k = 0.15. The difference between the 2 correlation estimates with ground-truth was not statistically significant. The lower segmentation threshold (0.15 versus 0.25) suggests that subjective raters overestimate WM lesion burden.ConclusionsWe validate the Lesion Segmentation Toolbox for determining total lesion volume in diabetes-enriched populations and compare it with a common subjective WM lesion rating scale. The Lesion Segmentation Toolbox is a readily available substitute for subjective WM lesion scoring in studies of diabetes and other populations with changes of leukoaraiosis.

Project description:BackgroundDespite decades of research, there is continued uncertainty regarding whether autism is associated with a specific profile of gray matter (GM) structure. This inconsistency may stem from the widespread use of voxel-based morphometry (VBM) methods that combine indices of GM density and GM volume. If GM density or volume, but not both, prove different in autism, the traditional VBM approach of combining the two indices may obscure the difference. The present study measures GM density and volume separately to examine whether autism is associated with alterations in GM volume, density, or both.MethodsDifferences in MRI-based GM density and volume were examined in 6-25 year-olds with a diagnosis of autism spectrum disorder (n = 213, 80.8% male, IQ 47-154) and a typically developing (TD) sample (n = 190, 71.6% male, IQ 67-155). High-resolution T1-weighted anatomical images were collected on a single MRI scanner. Regional density and volume were estimated via whole-brain parcellation comprised of 1625 parcels. Parcel-wise analyses were conducted using generalized additive models while controlling the false discovery rate (FDR, q < 0.05). Volume differences in the 68-region Desikan-Killiany atlas derived from Freesurfer were also examined, to establish the generalizability of findings across methods.ResultsNo density differences were observed between the autistic and TD groups, either in individual parcels or whole brain mean density. Increased volume was observed in autism compared to the TD group when controlling for Age, Sex, and IQ, both at the level of the whole brain (total volume) and in 45 parcels (2.8% of total parcels). Parcels with greater volume included inferior, middle, and superior temporal gyrus, inferior and superior frontal gyrus, precuneus, and fusiform gyrus. Converging evidence from a standard Freesurfer pipeline also identified greater volume in a number of overlapping regions.LimitationsThe method for determining "density" is not a direct measure of neuronal density, and this study cannot reveal underlying cellular differences. While this study represents possibly the largest single-site sample of its kind, it is underpowered to detect very small differences.ConclusionsThese results provide compelling evidence that autism is associated with regional GM volumetric differences, which are more prominent than density differences. This underscores the importance of examining volume and density separately, and suggests that direct measures of volume (e.g. region-of-interest or tensor-based morphometry approaches) may be more sensitive to autism-relevant differences in neuroanatomy than concentration/density-based approaches.

Dataset Information

Gray matter volume correlates of adolescent posttraumatic stress disorder: A comparison of manual intervention and automated segmentation in FreeSurfer.

Publications

Gray matter volume correlates of adolescent posttraumatic stress disorder: A comparison of manual intervention and automated segmentation in FreeSurfer.

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