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The current registered report investigated the effects of source credibility in relation to one’s own worldviews (i.e. supernatural beliefs and belief in science) in a spiritual and scientific context. We asked people to rate the truthfulness of ambiguous auditory statements about the cosmos attributed to a scientist or a spiritual guru and analyzed this using hierarchical Bayesian modeling. In line with our hypotheses, we found that the scientist was seen as more credible than the spiritual guru. The overall credibility of the statements was positively related to supernatural beliefs. These beliefs also interacted with the source of the statement, which was reflected in a tendency for supernatural believers to rate statements from both the scientist and the guru as credible. In contrast, with increasing belief in science, the credibility of the sources diverged with higher ratings for the scientist compared to the guru. The study involved a conceptual replication of previous research and increased the confidence in the robustness of source credibility effects and their interaction with people’s worldviews.

We present the Amsterdam Open MRI Collection (AOMIC): three datasets with multimodal (3 T) MRI data including structural (T1-weighted), diffusion-weighted, and (resting-state and task-based) functional BOLD MRI data, as well as detailed demographics and psychometric variables from a large set of healthy participants (N = 928, N = 226, and N = 216). Notably, task-based fMRI was collected during various robust paradigms (targeting naturalistic vision, emotion perception, working memory, face perception, cognitive conflict and control, and response inhibition) for which extensively annotated event-files are available. For each dataset and data modality, we provide the data in both raw and preprocessed form (both compliant with the Brain Imaging Data Structure), which were subjected to extensive (automated and manual) quality control. All data is publicly available from the OpenNeuro data sharing platform., Measurement(s) T2*-weighted images • T1-weighted images • diffusion-weighted images • Respiration • cardiac trace Technology Type(s) Gradient Echo MRI • Magnetization-Prepared Rapid Gradient Echo MRI • Diffusion Weighted Imaging • breathing belt • Pulse Oximetry Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13606946

Chronic pain is an endemic problem involving both peripheral and brain pathophysiology. Although biomarkers have revolutionized many areas of medicine, biomarkers for pain have remained controversial and relatively underdeveloped. With the realization that biomarkers can reveal pain-causing mechanisms of disease in brain circuits and in the periphery, this situation is poised to change. In particular, brain pathophysiology may be diagnosable with human brain imaging, particularly when imaging is combined with machine learning techniques designed to identify predictive measures embedded in complex data sets. In this review, we explicate the need for brain-based biomarkers for pain, some of their potential uses, and some of the most popular machine learning approaches that have been brought to bear. Then, we evaluate the current state of pain biomarkers developed with several commonly used methods, including structural magnetic resonance imaging, functional magnetic resonance imaging and electroencephalography. The field is in the early stages of biomarker development, but these complementary methodologies have already produced some encouraging predictive models that must be tested more extensively across laboratories and clinical populations.