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Anxiety can alter an individual's perception of their external sensory environment. Previous studies suggest that anxiety can increase the magnitude of neural responses to unexpected (or surprising) stimuli. Additionally, surprise responses are reported to be boosted during stable compared to volatile environments. Few studies, however, have examined how learning is impacted by both threat and volatility. To investigate these effects, we used threat-of-shock to transiently increase subjective anxiety in healthy adults while they performed an auditory oddball task under stable and volatile environments and while undergoing functional Magnetic Resonance Imaging (fMRI) scanning. We then used Bayesian Model Selection (BMS) mapping to identify the brain areas where different models of anxiety displayed the highest evidence. Behaviourally, we found that threat-of-shock eliminated the accuracy advantage conferred by environmental stability over volatility. Neurally, we found that threat-of-shock led to attenuation and loss of volatility-attuning of brain activity evoked by surprising sounds across most subcortical and limbic regions including the thalamus, basal ganglia, claustrum, insula, anterior cingulate, hippocampal gyrus and the superior temporal gyrus. Taken together, our findings suggest that threat eliminates learning advantages conferred by statistical stability compared to volatility. Thus, we propose that anxiety disrupts behavioural adaptation to environmental statistics, and that multiple subcortical and limbic regions are implicated in this process.
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http://dx.doi.org/10.1002/hbm.26230 | DOI Listing |
Curr Biol
August 2025
Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada.
Humans and other primates are capable of learning to recognize new visual stimuli throughout their lifetimes. Most theoretical models assume that such learning occurs through the adjustment of the large number of synaptic weights connecting the visual cortex to downstream decision-making areas. While this approach to learning can optimize performance on behavioral tasks, it can also be costly in terms of time and energy.
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September 2025
The National University Hospital of Iceland, Iceland; Faculty of Nursing and Midwifery, University of Iceland, Iceland.
Background: Midwives in labour wards at high-tech hospitals have witnessed significant technological advancements. Ultrasound devices for assessing labour progress may offer advantages over traditional vaginal examinations. However, it is important to examine the views of care providers before introducing this new technology.
View Article and Find Full Text PDFChirurgie (Heidelb)
September 2025
Chirurgische Klinik der Universitätsmedizin Mannheim, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Deutschland.
Background: Minimally invasive liver surgery has rapidly evolved in recent years. In addition to the laparoscopic liver resection (LLR), robot-assisted liver resection (RLR) is increasingly gaining in importance; however, although the robotic-assisted approach offers clinical benefits, particularly in complex procedures, it remains a matter of debate.
Objective: The aim of this study was to present the development, perioperative outcomes, key challenges, and insights from over 500 minimally invasive liver resections performed at a specialized high-volume center.
Ann Plast Surg
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Department of Anatomy, Amrita Hospitals and School of Medicine, Kochi, Kerala, India.
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View Article and Find Full Text PDFVasc Health Risk Manag
September 2025
Department of Information Technology, Kennesaw State University, Marietta, GA, USA.
Cardiac arrhythmias are a major health concern around the world, causing morbidity and mortality in a wide range of people. The timely and accurate diagnosis of arrhythmias is critical for optimal clinical management and intervention. Deep learning techniques have developed as powerful tools for detecting arrhythmias in recent years, taking advantage of advances in signal processing and machine learning.
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