Although self-other behavioral differences in decision making under risk have been observed in some contexts, little is known about the neural mechanisms underlying such differences. Using functional magnetic resonance imaging (fMRI) and the cups task, in which participants choose between risky and sure options for themselves and others in gain and loss situations, we found that people were more risk-taking when making decisions for themselves than for others in loss situations but were equally risk-averse in gain situations. Significantly stronger activations were observed in the dorsomedial prefrontal cortex (dmPFC) and anterior insula (AI) when making decisions for the self than for others in loss situations but not in gain situations. Furthermore, the activation in the dmPFC was stronger when people made sure choices for others than for themselves in gain situations but not when they made risky choices, and was both stronger when people made sure and risky choices for themselves than for others in loss situations. These findings suggest that gain-loss situation modulates self-other differences in decision making under risk, and people are highly likely to differentiate the self from others when making decisions in loss situations.
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http://dx.doi.org/10.1038/s41598-018-37236-9 | DOI Listing |
EClinicalMedicine
October 2024
Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Center, St. Michael's Hospital, Unity Health Toronto, Toronto, ON M5B 1W8, Canada.
Background: Use of health applications (apps) to support healthy lifestyles has intensified. Different app features may support effectiveness, including gamification defined as the use of game elements in a non-game situation. Whether health apps with gamification can impact behaviour change and cardiometabolic risk factors remains unknown.
View Article and Find Full Text PDFCardiol Young
January 2025
Department of Pediatric Cardiology, Intensive Care Medicine and Congenital Heart Disease, Justus Liebig University, Giessen, Germany.
Background: A subgroup of CHDs can only be treated palliatively through a Fontan circulation. In case of a failing Fontan situation, serum proteins are lost unspecifically and can also lead to a loss of vaccine antibodies. In a failing Fontan situation, heart transplantation may be the only feasible option.
View Article and Find Full Text PDFPhys Eng Sci Med
January 2025
Faculty of Engineering, Department of Biomedical Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
Neointimal coverage and stent apposition, as assessed from intravascular optical coherence tomography (IVOCT) images, are crucial for optimizing percutaneous coronary intervention (PCI). Existing state-of-the-art computer algorithms designed to automate this analysis often treat lumen and stent segmentations as separate target entities, applicable only to a single stent type and overlook automation of preselecting which pullback segments need segmentation, thus limit their practicality. This study aimed for an algorithm capable of intelligently handling the entire IVOCT pullback across different phases of PCI and clinical scenarios, including the presence and coexistence of metal and bioresorbable vascular scaffold (BVS), stent types.
View Article and Find Full Text PDFHeliyon
December 2024
Department of Computer Science & Engineering, K L E F Deemed To Be University, Green Fields, Vaddeswaram, Guntur (dt), Andhra Pradesh, 521230, India.
Real-time monitoring and anomaly detection are essential in healthcare to ensure safe conditions for patients and maintain the integrity of medical data samples. The majority of existing systems, despite improvements in healthcare technologies, cannot capture the spatial and temporal patterns of multimodal data simultaneously, process high Volume data in real-time, and ensure the privacy of patients' identity effectively. In this work, we handle these limitations by proposing a complete approach that uses state-of-the-art deep learning and data processing architectures to realize resilient anomaly detection in healthcare systems.
View Article and Find Full Text PDFData Brief
February 2025
MARS Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka 1216, Bangladesh.
Anemia is a critical medical condition in public health concern in tropical and subtropical areas, and understanding its hematological changes is crucial for improving diagnosis, treatment, and prognosis.It manifests through symptoms like weakness, fatigue, pale skin, and shortness of breath due to insufficient hemoglobin or red blood cells to carry adequate oxygen, with severe cases leading to complications such as chest pain. Common causes include blood loss, chronic diseases, and iron and vitamin deficiencies.
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