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 |
Cureus
December 2024
Mathematics, Keio University, Yokohama, JPN.
Context: Accurate prognosis prediction for cancer patients in palliative care is critical for clinical decision-making and personalized care. Traditional statistical models have been complemented by machine learning approaches; however, their comparative effectiveness remains underexplored.
Objectives: To assess the prognostic accuracy of statistical and machine learning models in predicting 30-day survival in patients with advanced cancer using objective data, such as the result of the blood test.
Front Cell Dev Biol
January 2025
Cixi Biomedical Research Institute, Wenzhou Medical University, Ningbo, China.
Background: Vessel segmentation in fundus photography has become a cornerstone technique for disease analysis. Within this field, Ultra-WideField (UWF) fundus images offer distinct advantages, including an expansive imaging range, detailed lesion data, and minimal adverse effects. However, the high resolution and low contrast inherent to UWF fundus images present significant challenges for accurate segmentation using deep learning methods, thereby complicating disease analysis in this context.
View Article and Find Full Text PDFHeliyon
January 2025
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China.
Objective And Rationale: Children's clinical pain phenotypes are complex, and there is a lack of objective biological diagnostic markers and cognitive patterns. Detecting physiological signals through wearable devices simplifies disease diagnosis and holds the potential for remote medical applications.
Method And Results: This research established a pain recognition model based on AI skin potential (SP) signal analysis.
Curr Cancer Drug Targets
January 2025
Department of Pharmacology, Sri Shanmugha College of Pharmacy, Sankari, Salem, 637304, Tamil Nadu, India.
Liver metastases from Gastrointestinal (GI) cancers present significant challenges in oncology, often signaling poor prognosis. Traditional detection methods like imaging and tissue biopsies have limitations in sensitivity, specificity, and tumor heterogeneity represen-tation. The advent of artificial intelligence (AI) in healthcare, driven by advancements in ma-chine learning, algorithms, and data science, offers a promising frontier for early detection and management of liver metastases.
View Article and Find Full Text PDFJ Orthop Surg Res
January 2025
Department of Orthopedic, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China.
Knee arthritis is a common degenerative joint disease, usually with joint swelling, chronic pain, and dysfunction as the main clinical manifestations. At present, the conservative treatment for knee arthritis is mostly using anti-inflammatory and analgesic drugs, but the effect is mostly temporary, and can not prevent its progress and surgery is usually the last treatment method. Total knee arthroplasty, also known as TKA, is one of the most effective treatments for osteoarthritis of the knee that has progressed to the end stage.
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