Human computer models represent a useful tool for investigating the human body response to external static/dynamic loads or for human-centred design. Articulated Total Body (ATB) models are the simplest human multibody models, where body segments are represented by ellipsoids joined at skeletal articulations. Over the years, regression models on both living subjects’ and cadavers’ data have been developed to predict body segments properties. These models are affected by two main limitations: the only inputs are the subject’s weight and height, not considering that for the same combination different morphologies can exist; secondly, regression analyses were performed over a specific population not including peculiar morphologies (under-weight or obese). A novel methodology for developing anthropomorphic ATB models is here presented: a statistical shape model able to predict the external geometry of the human body from a limited set of anthropometric measurements was implemented and body segments were obtained by segmentation; the respective inertial properties were computed from volumes, assuming a constant density value. The properties of this new anthropomorphic ATB model were compared to those calculated by GEBOD (Generator of Body Data), a well-known programme for ATB data calculation. A virtual population of twenty subjects was analysed: with reference to the inertial properties the most relevant differences occurred at the abdomen and the thighs segments (60% relative error), while the trunk, the shoulder and the calves represent the most critical areas for the geometry reconstruction (50 mm average error). The significance of these outcomes was investigated performing multibody simulations with various scenarios.@en

Objective: Neuroimaging studies show structural alterations of various brain regions in children and adults with attention deficit hyperactivity disorder (ADHD), although nonrepli-cations are frequent. The authors sought to identify cortical characteristics related to ADHD using large-scale studies. Methods: Cortical thickness and surface area (based on the Desikan–Killiany atlas) were compared between case subjects with ADHD (N=2,246) and control subjects (N=1,934) for children, adolescents, and adults separately in ENIGMA-ADHD, a consortium of 36 centers. To assess familial effects on cortical measures, case subjects, unaffected siblings, and control subjects in the NeuroIMAGE study (N=506) were compared. Associations of the attention scale from the Child Behavior Checklist with cortical measures were determined in a pediatric population sample (Generation-R, N=2,707). Results: In the ENIGMA-ADHD sample, lower surface area values were found in children with ADHD, mainly in frontal, cingulate, and temporal regions; the largest significant effect was for total surface area (Cohen’s d=20.21). Fusiform gyrus and temporal pole cortical thickness was also lower in children with ADHD. Neither surface area nor thickness differences were found in the adolescent or adult groups. Familial effects were seen for surface area in several regions. In an overlapping set of regions, surface area, but not thickness, was associated with attention problems in the Generation-R sample. Conclusions: Subtle differences in cortical surface area are widespread in children but not adolescents and adults with ADHD, confirming involvement of the frontal cortex and highlighting regions deserving further attention. Notably, the alterations behave like endophenotypes in families and are linked to ADHD symptoms in the population, extending evidence that ADHD behaves as a continuous trait in the population. Future longitudinal studies should clarify individual lifespan trajectories that lead to nonsignificant findings in adolescent and adult groups despite the presence of an ADHD diagnosis.

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