Pain emerges from the integration of sensory information about threats and contextual information such as an individual’s expectations. However, how sensory and contextual effects on pain are served by the brain is not fully understood so far. To address this question, we applied brief painful stimuli to 40 healthy human participants and independently varied stimulus intensity and expectations. Concurrently, we recorded electroencephalography. We assessed local oscillatory brain activity and interregional functional connectivity in a network of six brain regions playing key roles in the processing of pain. We found that sensory information predominantly influenced local brain oscillations. In contrast, expectations exclusively influenced interregional connectivity. Specifically, expectations altered connectivity at alpha (8 to 12 hertz) frequencies from prefrontal to somatosensory cortex. Moreover, discrepancies between sensory information and expectations, i.e., prediction errors, influenced connectivity at gamma (60 to 100 hertz) frequencies. These findings reveal how fundamentally different brain mechanisms serve sensory and contextual effects on pain.

Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing and analysis of EEG data is time-consuming and mostly subjective. Therefore, we developed DISCOVER-EEG, an open and fully automated pipeline that enables easy and fast preprocessing, analysis and visualization of resting state EEG data. Data in the standard EEG-BIDS structure are automatically preprocessed and physiologically meaningful features of brain function (including oscillatory power, connectivity and network characteristics) are extracted and visualized using two open-source and widely used Matlab toolboxes (EEGlab and FieldTrip). We exemplify the use of the pipeline for biomarker discovery in healthy ageing in the LEMON dataset, containing 212 healthy participants. We demonstrate its utility to speed up biomarker discovery in a clinical setting with a new dataset containing 74 patients with chronic pain. Thus, the DISCOVER-EEG pipeline facilitates the aggregation, reuse and analysis of large EEG datasets, promoting open and reproducible research on brain function.

Pain emerges from the integration of sensory information about threats and contextual information such as an individual's expectations. However, how sensory and contextual effects on pain are served by the brain is not fully understood so far. To address this question, we applied brief painful stimuli to 40 healthy human participants and independently varied stimulus intensity and expectations. Concurrently, we recorded electroencephalography. We assessed local oscillatory brain activity and inter-regional functional connectivity in a network of six brain regions playing key roles in the processing of pain. We found that sensory information predominantly influenced local brain oscillations. In contrast, expectations exclusively influenced inter-regional connectivity. Specifically, expectations altered connectivity at alpha (8-12 Hz) frequencies from prefrontal to somatosensory cortex. Moreover, discrepancies between sensory information and expectations, i.e., prediction errors, influenced connectivity at gamma (60-100 Hz) frequencies. These findings reveal how fundamentally different brain mechanisms serve sensory and contextual effects on pain.

In this article, a novel mesh‐free, moving Kriging (MK) based collocation scheme for the numerical solution of partial differential equations (PDEs) is introduced. In contrast to methods that are based on a Galerkin weak form of the governing PDEs, the MK collocation (MKC) approach, which is strong form based, is truly mesh‐free in the sense that no background mesh is required for numerical integration. In fact, the presented approach does not require the evaluation of any integrals. Since the approximation function in the MK framework can be conditioned on point value‐ as well as derivative‐information, the pointwise exact imposition of essential as well as natural boundary conditions is rendered straightforward. By incorporating an explicit linear basis into the MK framework, the first‐order consistency condition is fulfilled, and thus rigid body motions are captured accurately. Moreover, Kriging functions may be conceived that comply with constraints on higher order derivatives such as the PDE at hand at certain locations. This possibility proves useful in improving the solution accuracy in the vicinity of Dirichlet boundaries. This article provides a study of the method's characteristics by means of 2D linear elasticity examples. It concludes with a suggestion on how to apply MKC to nonlinear PDEs.