Pain, Learning, and Nocebo

Chronic Pain, Pain Syndrome

Nocebo, Pain, BOLD, fMRI, Conditioning

Nocebo effects, negative responses to inert or active treatments which are putatively induced by negative outcome expectations, have been shown to play a significant role in pain perception. The underlying neurobiological mechanisms of these effects remain largely unexplored. The primary objective of this study is to test the role of N-methyl-D-aspartate (NMDA) receptor-dependent learning in an experimental model of conditioned nocebo effects on self-reported pain. Secondary objectives are to examine the role of the NMDA manipulation and related neural correlates during the acquisition and extinction of nocebo effects using statistical learning models. This study will utilize a placebo controlled, double-blind design with respect to the pharmacological administration of 80 mg D-Cycloserine (DCS), an NMDA agonist, or placebo. Validated conditioning and verbal suggestion (VS) paradigms will induce nocebo effects on pain in a random sample of 50 healthy adults. The primary endpoint of the study is the magnitude of the induced nocebo effect on pain measured as the difference between self-reported pain, between the first conditioned and control extinction trials. Secondary endpoints include the classification analysis of the Blood Oxygen Level Dependent (BOLD) responses of participants into pharmacological groups with multivariate pattern analysis. This study will be conducted at Leiden University and the Leiden University Medical Center (LUMC), The Netherlands.

Main outcome variable: \- The magnitude of induced nocebo hyperalgesia is defined as the difference in pain ratings for the first nocebo trial compared to the first control trial of the extinction phase. A significant difference here is assessed within the mixed model analysis of variance (ANOVA), comparing within-subjects differences for control and nocebo trials between DCS and placebo groups. Time frame: On the day of the experimental session, during the extinction phase Secondary outcome variables: * The difference in BOLD response at a series of a priori ROIs between pharmacological groups during the acquisition of nocebo effects. * The classification accuracy (into pharmacological groups), indicating that patterns of activation in the network of a priori ROIs form a model that can detect differences in neural activations during the acquisition of nocebo effects. * The difference in BOLD response at a series of a priori ROIs between pharmacological groups during the extinction of nocebo effects. * The classification accuracy (into pharmacological groups), indicating that patterns of activation in the network of a priori ROIs form a model that can detect differences in neural activations during the first trials of the extinction phase. * The difference in BOLD response at a series of a priori ROIs between pain at baseline and nocebo-augmented pain. * The classification accuracy, indicating that patterns of activation in the network of a priori ROIs form a model that can detect commonalities and differences in neural activations between the experience of pain at baseline and nocebo-augmented pain. * The prediction accuracy, indicating that patterns of activation in the network of a priori ROIs form a model that can predict the magnitude of induced nocebo effects based on patterns of activations during the acquisition of nocebo effects. * The moderation of the magnitude of induced nocebo effects in the first trials of the extinction phase by scores on the psychological questionnaires. 0\. Manipulation checks: Pain intensity responses during the acquisition phase To assess the effectiveness of the conditioning paradigm, pain ratings during acquisition will be analysed using a 2x1 mixed model ANOVA with group as a between-subjects factor (DCS, placebo), and pain intensity scores as a within-subjects, repeated measure with two levels (conditioned and unconditioned trials). Effect of DCS on learning The Wechsler Memory Scale-Fourth Edition (WMS-IV) subtest Verbal Paired Associates will be used to assess whether DCS enhanced learning. A 2x1 mixed model ANOVA with group as the between-subjects factor and WMS-IV scores as the within-subjects repeated measure with two measurements, before administering DCS or placebo versus at two hours post-administration, before the beginning of the conditioning paradigm. 1. Primary hypothesis: The magnitude of the induced nocebo effect on pain is hypothesized to be larger in the DCS group relative to the placebo group. The magnitude of the nocebo effect is measured as the difference between self-reported pain on a Numeric Rating Scale (NRS) between the first conditioned and control extinction phase trials. 2. Secondary hypotheses: 2.1. The magnitude of the conditioned nocebo effects still present after extinction is measured as the change from the average nocebo effect reported in the first trials of the extinction phase (after acquisition) and the average nocebo effect reported in last trials of the extinction phase (after extinction). 2.2. DCS and placebo groups will be characterized by divergent neural activity across a set of a priori regions of interest (ROIs) during acquisition. ROI analysis of differences in BOLD responses will be performed on periaqueductal grey, (PAG), ventrolateral prefrontal cortex, (vlPFC), and dorsolateral prefrontal cortex (dlPFC), amygdala, anterior cingulate cortex (aCC), hippocampus, rostral ventromedial medulla (RVM), thalamus, insula. 2.3 Multivariate pattern analysis (i.e., machine learning methods) will be used to investigate differences in BOLD responses during the acquisition of nocebo effects and thereby classify participants into pharmacological treatment groups (1) DCS, or 2) placebo) based on neural activity in the following ROIs: PAG, vlPFC, dlPFC, aCC, RVM, amygdala, thalamus, insula. 2.4. DCS and placebo groups will be characterized by divergent BOLD responses across a set of a priori ROIs during extinction. ROI analysis for differences in BOLD responses between DCS and placebo groups will be performed on the following ROIs: PAG, vlPFC, dlPFC, aCC, RVM, amygdala, thalamus, insula. 2.5 Multivariate pattern analysis will be used to investigate differences in BOLD responses during the extinction of nocebo effects and thereby classify participants into pharmacological treatment groups (1) DCS, or 2) placebo) based on neural activity in the following ROIs: PAG, vlPFC, dlPFC, aCC, RVM, amygdala, thalamus, insula. 2.6. Pain and baseline, and nocebo augmented pain of a similar intensity will be characterized by divergent neural activations. Within the placebo group, ROI analysis for differences in BOLD responses between nocebo experiences and sensory experiences of pain based on BOLD responses in the following ROIs: PAG, vlPFC, dlPFC, aCC, RVM, amygdala, thalamus, insula. 2.7. Pain and baseline, and nocebo augmented pain of a similar intensity will be characterized by divergent neural activations. Within the placebo group, multivariate pattern analysis will be used to investigate the differences in BOLD responses between nocebo experiences and sensory experiences of pain based on neural activity in the following ROIs: PAG, vlPFC, dlPFC, aCC, RVM, amygdala, thalamus, insula. . 2.8. Patterns of BOLD responses measured during the acquisition of nocebo effects in all pharmacological groups (1) DCS, or 2) placebo) at the previously listed ROIs, will predict the magnitude of nocebo effects on pain during extinction. 3. Questionnaires To assess the influence of psychological traits, questionnaires will also be included. These will include the Pain Catastrophizing Scale (PSC), Spielberger State Trait Anxiety Inventory (STAI), and the Body Vigilance Scale (BVS).

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