Computational protocol: Personality disorder symptomatology is associated with anomalies in striatal and prefrontal morphology

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Protocol publication

[…] Striatal morphology was estimated using MAGeT Brain (, ; ), a novel multi-atlas technique that bootstraps segmentation using Multiple Automatically Generated Templates. This technique has been optimized and validated for striatal structures, so that analyses focused on bilateral caudate, putamen, and ventral striatum (nucleus accumbens). However, since a recent variant of the MAGeT Brain algorithm was shown to reliably segment the hippocampus and amygdala using additional high-resolution atlases as input (; ; ), these structures were additionally examined in exploratory analyses.All segmentations were manually checked by an expert observer (MTMP) prior to analysis. Outcome measures were (voxel-based) volume and vertex-wise measures of shape and surface area (SA; only volume was available for the exploratory hippocampus and amygdala analyses). Shape is measured as a series of surface displacement metrics that describe the inward or outward displacement along a surface normal required for the atlas () to match each subject (see ; ) and corresponding local SA differences (as recently reported in and ). SA at each vertex was divided by the total SA of each given structure in an individual to account for global effects of volume on local vertex-wise measures.For volume normalization, total brain volume (TBV) was obtained using the brain extraction based on non-local segmentation technique (BEaST) pipeline (), which allows for accurate and robust brain extraction. [...] To determine prefrontal/orbitofrontal contributions to PD-Sx, cortical thickness (CT) and cortical SA were estimated on the T1-weighted images using the fully automated CIVET 1.1.10 pipeline (). In brief, the images were linearly registered to standard stereotaxic space defined by the MNI ICBM 152 model (; ). The images were then corrected for intensity non-uniformity using non-parametric non-uniform intensity normalization (N3, ) and a non-linear registration to the model was applied. Tissue classification was then performed using INSECT (), classifying each voxel as white matter (WM), gray matter (GM), or cerebrospinal fluid (CSF). The images were then mapped to a probabilistic atlas using the ANIMAL algorithm (Automatic Non-linear Image Matching and Anatomical Labeling, ). Finally, the WM surface was generated by using an ellipsoid polygonal model that deforms to fit the WM/GM interface and the pial surface (). To generate the GM surface, the WM surface was expanded until it reached the GM/CSF interface (). The resulting surfaces were composed of 40,962 vertices for each hemisphere, and CT was estimated as the distance, in mm, between homologous vertices in the WM and GM matter surfaces. SA was estimated at each vertex as the average value of all adjoining vertices. CT and SA data were blurred using a surface-based diffusion smoothing kernels of 20 and 40 mm full-width at half-maximum (FWHM), respectively, to preserve the concordance between quantitative values and cortical topology () and non-linearly aligned with a surface-based registration ().Region-of-interest (ROI)-based CT and SA were also estimated, using the intersection of the cortical surfaces estimated by CIVET (above) and the LPBA40 atlas (), resulting in a total of 40 cortical regions per hemisphere providing a single output value per region (see, e.g., ). Among these, we focused on a priori PFC and OFC ROIs, as these have been consistently linked to PD-Sx, along with additional exploratory analyses in less consistently identified but potentially relevant ROIs: cingulate cortex and insula. […]

Pipeline specifications

Software tools MAGeT, BEaST, N3
Application Magnetic resonance imaging