Neural habituation during acute stress signals a blunted endocrine response and poor resilience.

BACKGROUND: A blunted hypothalamic-pituitary-adrenal (HPA) axis response to acute stress is associated with psychiatric symptoms. Although the prefrontal cortex and limbic areas are important regulators of the HPA axis, whether the neural habituation of these regions during stress signals both blunted HPA axis responses and psychiatric symptoms remains unclear. In this study, neural habituation during acute stress and its associations with the stress cortisol response, resilience, and depression were evaluated. METHODS: Seventy-seven participants (17-22 years old, 37 women) were recruited for a ScanSTRESS brain imaging study, and the activation changes between the first and last stress blocks were used as the neural habituation index. Meanwhile, participants' salivary cortisol during test was collected. Individual-level resilience and depression were measured using questionnaires. Correlation and moderation analyses were conducted to investigate the association between neural habituation and endocrine data and mental symptoms. Validated analyses were conducted using a Montreal Image Stress Test dataset in another independent sample (48 participants; 17-22 years old, 24 women). RESULTS: Neural habituation of the prefrontal cortex and limbic area was negatively correlated with cortisol responses in both datasets. In the ScanSTRESS paradigm, neural habituation was both positively correlated with depression and negatively correlated with resilience. Moreover, resilience moderated the relationship between neural habituation in the ventromedial prefrontal cortex and cortisol response. CONCLUSIONS: This study suggested that neural habituation of the prefrontal cortex and limbic area could reflect motivation dysregulation during repeated failures and negative feedback, which might further lead to maladaptive mental states.

Static and Temporal Dynamic in Functional Connectivity of Large-scale Brain Networks During Acute Stress Regulate Stress Resilience Differently: The Promotion Role of Trait Resilience.

Stress resilience has been largely regarded as a process in which individuals actively cope with and recover from stress. Over the past decade, the emergence of large-scale brain networks has provided a new perspective for the study of the neural mechanisms of stress. However, the role of inter-network functional-connectivity (FC) and its temporal fluctuations in stress resilience is still unclear. To bridge this knowledge gap, seventy-seven participants (age, 17-22 years, 37 women) were recruited for a ScanSTRESS brain imaging study. A static perspective was initially adopted, using changes in FC that obtained from stress vs. control condition during the entire stress induction phase as a static indicator. Further, changes in FC between different stress runs were analyzed as an index of temporal dynamics. Stress resilience was gauged using salivary cortisol levels, while trait resilience was measured via behavioral-activation-system (BAS) sensitivity. Results found that, for the static index, enhanced FC between the salience-network (SN), default-mode-network (DMN) and limbic-network (LBN) during acute stress could negatively signal stress resilience. For the temporal dynamics index, FC among the dorsal-attention-network (DAN), central-executive-network (CEN) and visual-network (VN) decreased significantly during repeated stress induction. Moreover, the decline of FC positively signaled stress resilience, and this relationship only exist in people with high BAS. The current research elucidates the intricate neural underpinnings of stress resilience, offering insights into the adaptive mechanisms underlying effective stress responses.

A new dataset of oral panoramic x-ray images and parallel network using transformers for medical image segmentation.

INTRODUCTION: Accurate segmentation of the key mandibular region in the oral panoramic X-ray image is crucial for the diagnosis of the mandibular region and the planning of implant surgery. Because the oral panoramic X-ray image contains many important anatomical information for implant treatment evaluation. However, the fuzzy boundary between each region in the image makes the segmentation task very challenging. In data-driven segmentation methods, corresponding datasets are often required. Due to the limited oral data set at present, there is a bottleneck in clinical application. MATERIALS AND METHODS: In this paper, we build a panoramic X-ray image dataset for the mandibular region. The dataset has a total of 711 images. The dataset is divided into 8 categories based on the number of teeth and treatment conditions. The annotations include mandible, normal teeth, treated teeth and implants. In terms of network segmentation. According to the local and global characteristics of the dataset, we designed a CBTrans partition network by paralleling the convolution block and the Swin-transform block of the bottleneck structure. RESULTS: The experimental results show that our proposed network achieves excellent performance on the mandibular region segmentation dataset and the common retina dataset DRIVE. CONCLUSION: CBTrans can better extract features locally and globally by combining CNN of the bottleneck structure and Swin Transformer in parallel. CBTrans demonstrates performance advantages over other similar hybrid architecture models.