Quantifying cerebral contributions to pain beyond nociception

Action Contro

Bottom-Up and Top-Down Processes in Emotion Generation: Common and Distinct Neural Mechanisms

Emotions are generally thought to arise through the interaction of bottom-up and top-down processes. However, prior work has not delineated their relative contributions. In a sample of 20 females, we used functional magnetic resonance imaging to compare the neural correlates of negative emotions generated by the bottom-up perception of aversive images and by the top-down interpretation of neutral images as aversive. We found that (a) both types of responses activated the amygdala, although bottom-up responses did so more strongly; (b) bottom-up responses activated systems for attending to and encoding perceptual and affective stimulus properties, whereas top-down responses activated prefrontal regions that represent high-level cognitive interpretations; and (c) self-reported affect correlated with activity in the amygdala during bottom-up responding and with activity in the medial prefrontal cortex during top-down responding. These findings provide a neural foundation for emotion theories that posit multiple kinds of appraisal processes and help to clarify mechanisms underlying clinically relevant forms of emotion dysregulation.National Institutes of Health (U.S.) (Grant MH58147)National Institutes of Health (U.S.) (Grant MH076137

Hemodynamic-informed parcellation of fMRI data in a Joint Detection Estimation framework

International audienceIdentifying brain hemodynamics in event-related functional MRI (fMRI) data is a crucial issue to disentangle the vascular response from the neuronal activity in the BOLD signal. This question is usually addressed by estimating the so-called Hemodynamic Response Function (HRF). Voxelwise or region-/parcelwise inference schemes have been proposed to achieve this goal but so far all known contributions commit to pre-specified spatial supports for the hemodynamic territories by defining these supports either as individual voxels or a priori fixed brain parcels. In this paper, we introduce a Joint Parcellation-Detection-Estimation (JPDE) procedure that incorporates an adaptive parcel identification step based upon local hemodynamic properties. Efficient inference of both evoked activity, HRF shapes and supports is then achieved using variational approximations. Validation on synthetic and real fMRI data demonstrate the JPDE performance over standard detection estimation schemes and suggest it as a new brain exploration tool

European Headache Federation recommendations for placebo and nocebo terminology

Background and aim Despite recent publications, practitioners remain unfamiliar with the current terminology related to the placebo and nocebo phenomena observed in clinical trials and practice, nor with the factors that modulate them. To cover the gap, the European Headache Federation appointed a panel of experts to clarify the terms associated with the use of placebo in clinical trials. Methods The working group identified relevant questions and agreed upon recommendations. Because no data were required to ans

Group-regularized individual prediction: Theory and application to pain

Multivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods can be used to predict or ‘decode’ psychological states in individual subjects. Single-subject MVPA approaches, however, are limited by the amount and quality of individual-subject data. In spite of higher spatial resolution, predictive accuracy from single-subject data often does not exceed what can be accomplished using coarser, group-level maps, because single-subject patterns are trained on limited amounts of often-noisy data. Here, we present a method that combines population-level priors, in the form of biomarker patterns developed on prior samples, with single-subject MVPA maps to improve single-subject prediction. Theoretical results and simulations motivate a weighting based on the relative variances of biomarker-based prediction—based on population-level predictive maps from prior groups—and individual-subject, cross-validated prediction. Empirical results predicting pain using brain activity on a trial-by-trial basis (single-trial prediction) across 6 studies (N = 180 participants) confirm the theoretical predictions. Regularization based on a population-level biomarker—in this case, the Neurologic Pain Signature (NPS)—improved single-subject prediction accuracy compared with idiographic maps based on the individuals' data alone. The regularization scheme that we propose, which we term group-regularized individual prediction (GRIP), can be applied broadly to within-person MVPA-based prediction. We also show how GRIP can be used to evaluate data quality and provide benchmarks for the appropriateness of population-level maps like the NPS for a given individual or study.FSW – Publicaties zonder aanstelling Universiteit Leide

''Consensus on placebo and nocebo effects connects science with practice:'' reply to ''questioning the consensus on placebo and nocebo effects”

Health and self-regulatio

What should clinicians tell patients about placebo and nocebo effects? Practical considerations based on expert consensus

Science Communication and Societ

Conceptual conditioning: Mechanisms mediating conditioning effects on pain

Action Contro