Hao Wu

Z-curve analysis is intended to diagnose the credibility of research results, but its interpretation and statistical properties are often misunderstood. We clarify that the Expected Discovery Rate (EDR; i.e. average observed power) is conceptually distinct from average pre-data power and lacks a clear link to credibility because it reflects both the average pre-data power and the estimated average population effect size. In our review of 37 articles reporting 278 Z-curve applications, 77.3% concluded publication bias, yet 48.2% did not state whether the p-values analyzed reflected focal findings, and 69.1% may have violated the assumption of independent p-values. Simulations further demonstrate that Z-curve estimators can be biased and inconsistent, failing to follow the Law of Large Numbers and potentially producing misleading conclusions. We also question claims made by Soto and Schimmack [Credibility of results in emotion science: A Z-curve analysis of results in the journals Cognition & Emotion and Emotion. Cognition and Emotion, (2025), 39(8), 1803–1819], regarding the credibility of emotion-science findings, noting that such conclusions should be interpreted cautiously given the limitations of Z-curve estimates. Overall, we do not recommend using Z-curve to evaluate research findings. Traditional meta-analytic methods remain more appropriate and reliable for statistical conclusions about focal research findings.

We present a new tool called Cyclone that can be used for verifying and testing graph-based models. Different from other verification tools, Cyclone provides users with a unique way of building models and specifying their properties, by explicitly constructing and reasoning about graphs. As graphs can naturally capture both static and dynamic behaviours of a system, this allows users to represent many challenging problems. In this tool paper, we describe Cyclone’s basic features through different examples. Our preliminary evaluation results show that Cyclone has a promising potential in handling verification tasks from various domains and good usability for those who are not familiar with using verification tools.

IntroductionRace-walking is a sport that mimics normal walking and running. Previous studies on sports science mainly focused on the cardiovascular and musculoskeletal systems. However, there is still a lack of research on the central nervous system, especially the real-time changes in brain network characteristics during race-walking exercise. This study aimed to use a network perspective to investigate the effects of different exercise intensities on brain functional connectivity.Materials and methodsA total of 16 right-handed healthy young athletes were recruited as participants in this study. The cerebral cortex concentration of oxyhemoglobin was measured by functional near-infrared spectroscopy in the bilateral prefrontal cortex (PFC), the motor cortex (MC) and occipital cortex (OC) during resting and race-walking states. Three specific periods as time windows corresponding to different exercise intensities were divided from the race-walking time of participants, including initial, intermediate and sprint stages. The brain activation and functional connectivity (FC) were calculated to describe the 0.01-0.1 Hz frequency-specific cortical activities.ResultsCompared to the resting state, FC changes mainly exist between MC and OC in the initial stage, while PFC was involved in FC changes in the intermediate stage, and FC changes in the sprint stage were widely present in PFC, MC and OC. In addition, from the initial-development to the sprint stage, the significant changes in FC were displayed in PFC and MC.ConclusionThis brain functional connectivity-based study confirmed that hemodynamic changes at different exercise intensities reflected different brain network-specific characteristics. During race-walking exercise, more extensive brain activation might increase information processing speed. Increased exercise intensity could facilitate the integration of neural signals such as proprioception, motor control and motor planning, which may be an important factor for athletes to maintain sustained motor coordination and activity control at high intensity. This study was beneficial to understanding the neural mechanisms of brain networks under different exercise intensities.

Eye-tracking technology has become a powerful tool for biomedical-related applications due to its simplicity of operation and low requirements on patient language skills. This study aims to use the machine-learning models and deep-learning networks to identify key features of eye movements in Alzheimer's Disease (AD) under specific visual tasks, thereby facilitating computer-aided diagnosis of AD. Firstly, a three-dimensional (3D) visuospatial memory task is designed to provide participants with visual stimuli while their eye-movement data are recorded and used to build an eye-tracking dataset. Then, we propose a novel deep-learning-based model for identifying patients with Alzheimer's Disease (PwAD) and healthy controls (HCs) based on the collected eye-movement data. The proposed model utilizes a nested autoencoder network to extract the eye-movement features from the generated fixation heatmaps and a weight adaptive network layer for the feature fusion, which can preserve as much useful information as possible for the final binary classification. To fully verify the performance of the proposed model, we also design two types of models based on traditional machine-learning and typical deep-learning for comparison. Furthermore, we have also done ablation experiments to verify the effectiveness of each module of the proposed network. Finally, these models are evaluated by four-fold cross-validation on the built eye-tracking dataset. The proposed model shows 85% average accuracy in AD recognition, outperforming machine-learning methods and other typical deep-learning networks.