The neural mechanisms of identifiable victim effect in prosocial decision-making.

The phenomenon known as the "identifiable victim effect" describes how individuals tend to offer more assistance to victims they can identify with than to those who are vague or abstract. The neural underpinnings of this effect, however, remain elusive. Our study utilized functional magnetic resonance imaging to delve into how the "identifiable victim effect" influences prosocial decision-making, considering different types of helping costs, across two distinct tasks. Participants were instructed to decide whether to help a victim with personal information shown (i.e., the identifiable victim) and an unidentifiable one by costing their money (task 1) or physical effort (task 2). Behaviorally, we observed a pronounced preference in both tasks for aiding identifiable victims over anonymous ones, highlighting a robust "identifiable victim effect." On a neural level, this effect was associated with heightened activity in brain areas like the bilateral temporoparietal junction (TPJ) when participants confronted anonymous victims, potentially indicating a more intensive mentalizing process for less concrete victims. Additionally, we noted that the TPJ's influence on value judgment processes is mediated through its functional connectivity with the medial prefrontal cortex. These insights contribute significantly to our understanding of the psychological and neural dynamics underlying the identifiable victim effect.

The application of machine learning methods for predicting the progression of adolescent idiopathic scoliosis: a systematic review.

Predicting curve progression during the initial visit is pivotal in the disease management of patients with adolescent idiopathic scoliosis (AIS)-identifying patients at high risk of progression is essential for timely and proactive interventions. Both radiological and clinical factors have been investigated as predictors of curve progression. With the evolution of machine learning technologies, the integration of multidimensional information now enables precise predictions of curve progression. This review focuses on the application of machine learning methods to predict AIS curve progression, analyzing 15 selected studies that utilize various machine learning models and the risk factors employed for predictions. Key findings indicate that machine learning models can provide higher precision in predictions compared to traditional methods, and their implementation could lead to more personalized patient management. However, due to the model interpretability and data complexity, more comprehensive and multi-center studies are needed to transition from research to clinical practice.

Automated 3D Cobb Angle Measurement Using U-Net in CT Images of Preoperative Scoliosis Patients.

To propose a deep learning framework "SpineCurve-net" for automated measuring the 3D Cobb angles from computed tomography (CT) images of presurgical scoliosis patients. A total of 116 scoliosis patients were analyzed, divided into a training set of 89 patients (average age 32.4 ± 24.5 years) and a validation set of 27 patients (average age 17.3 ± 5.8 years). Vertebral identification and curve fitting were achieved through U-net and NURBS-net and resulted in a Non-Uniform Rational B-Spline (NURBS) curve of the spine. The 3D Cobb angles were measured in two ways: the predicted 3D Cobb angle (PRED-3D-CA), which is the maximum value in the smoothed angle map derived from the NURBS curve, and the 2D mapping Cobb angle (MAP-2D-CA), which is the maximal angle formed by the tangent vectors along the projected 2D spinal curve. The model segmented spinal masks effectively, capturing easily missed vertebral bodies. Spoke kernel filtering distinguished vertebral regions, centralizing spinal curves. The SpineCurve Network method's Cobb angle (PRED-3D-CA and MAP-2D-CA) measurements correlated strongly with the surgeons' annotated Cobb angle (ground truth, GT) based on 2D radiographs, revealing high Pearson correlation coefficients of 0.983 and 0.934, respectively. This paper proposed an automated technique for calculating the 3D Cobb angle in preoperative scoliosis patients, yielding results that are highly correlated with traditional 2D Cobb angle measurements. Given its capacity to accurately represent the three-dimensional nature of spinal deformities, this method shows potential in aiding physicians to develop more precise surgical strategies in upcoming cases.

Three stages of perceiving consecutively moral behaviors: Neurophysiological effect of agent and valence on the moral judgments.

The present study investigates how agents and the moral valence of the acts affect moral judgments when two consecutively behaviors are perceived, with each describing morally salient behaviors done by the same or different agent(s). Participants had to rate the likableness/pleasantness of the agents/behaviors. Behavioral results indicated that rating the likableness of the agent was mainly depended on the morally diagnostic character of the agent while rating the pleasantness of the behaviors was mainly depended on the moral valence of the behaviors per se. ERP results showed: 1) larger N1 was found in response to the agent consistently acting immorally, indicating an early detection of social threatening information. 2) Compared with agents who consistently act morally which provided no norm- or expectation-violation information, other conditions induced larger N400, indicating greater cognitive effort was recruited when the present moral information violated the participants' prior knowledge to the agent. 3) Increased LPP was found in response to the agent consistently acting morally (vs. moral behaviors acted by different agents), representing that people may allocate more attention to positive information during this stage. We suggest that this three-stage scheme is a common model when people encounter consecutive moral events.