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Abstract Although teamwork is being integrated throughout engineering education because of the perceived benefits of teams, the construct of psychological safety has been largely ignored in engineering research. This omission is unfortunate because psychological safety reflects collective perceptions about how comfortable team members feel in sharing their perspectives, and it has been found to positively impact team performance in samples outside of engineering. While prior research has indicated that psychological safety is positively related to team performance and outcomes, research related to psychological safety in engineering teams is less established. There is also a lack of comprehensive methodologies that capture the dynamic changes that occur throughout the design process and at each time point. In light of this, the goal of the current study was to understand how psychological safety might be measured practically and reliably in engineering student teams over time. In addition, we sought to identify factors that impact the building and waning of psychological safety in these teams over time. This was accomplished through a study with 260 engineering students in 68 teams in a first-year engineering design class. The psychological safety of the teams was captured for each team over five time points over the course of a semester long design project. The results of this study provide some of the first evidence on the reliability of psychological safety in engineering teams and offer insights as to how to support and improve psychological safety. 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age.