A neural mechanism of direct and observational conditioning for placebo and nocebo responses
Introduction
Placebo analgesia and its opposite nocebo hyperalgesia (negative placebo effect) are robust phenomena observed in clinical practice and, as such, have significant and widespread applications (Scott et al., 2008; Tracey, 2010). Although placebo and nocebo effects are not direct pharmacological or physical interventions, they modulate brain circuits that can confer therapeutic effects (Colloca et al., 2008; Lu et al., 2010; Wager et al., 2004; Watson et al., 2009), both consciously and unconsciously (Jensen et al., 2015, 2012).
Conditioning is known to play an important role in placebo and nocebo effects by establishing a link between a context (cues) and the following pain stimulus, thus creating expectancies for future pain responses to the same circumstance. In addition, the mechanisms leading to the release of endogenous opioids in placebo analgesia are believed to involve conditioning factors (Amanzio and Benedetti, 1999; Enck et al., 2008; Yu et al., 2014). Classic theories of placebo and nocebo had a strong focus on direct conditioning, suggesting that higher-order areas of the brain process the expectancies for forthcoming pain based on personal exposure (Freeman et al., 2015; Wager et al., 2004). Recent studies have extended the theories to indirect conditioning where subjects observe others and learn from their experiences, and demonstrated that social observation can elicit placebo analgesia (Colloca and Benedetti, 2009; Egorova et al., 2015; Hunter et al., 2014) or nocebo hyperalgesia (Egorova et al., 2015; Świder and Ba̧bel, 2013; Vögtle et al., 2013).
Although placebo and nocebo effects are robust in general, they are largely variable across individuals. A number of psychological factors influencing the magnitude of placebo and nocebo responses have been studied, including suggestibility (Morton et al., 2010), expectation (Atlas et al., 2010; Morton et al., 2010), desire for relief (Vase et al., 2003) and belief (Zubieta et al., 2006). Neuroimaging metrics of brain function and structure provide important insights into individual variability of placebo effects. For instance, studies found that brain activity in the opioid system (Petrovic et al., 2002; Scott et al., 2008; Zubieta et al., 2005) and the reward system (Enck et al., 2008; Scott et al., 2007; Yu et al., 2014), brain responses for anticipation to pain stimuli (Kong et al., 2013; Wager et al., 2011, 2004) and gray matter density (Schweinhardt et al., 2009) were associated with the magnitude of individuals' placebo responses.
Studies have suggested that learning may play an important role in placebo/nocebo effects, and a conditioning paradigm may provide a good model for us to explore how previous experience (learning) can shape our current experience (Bingel et al., 2006; Colloca et al., 2008; Kong and Benedetti, 2014; Price et al., 1999). However, the underlying neural mechanism of how conditioning mediates individual differences in placebo and nocebo response remains unknown.
In this study, we collected resting-state magnetoencephalography (MEG) data before and after direct and observational conditioning and investigated the relationship between brain connectivity changes and conscious and nonconscious conditioned placebo and nocebo effects (Egorova et al., 2015). We hypothesized that the connectivity of brain regions associated with opioid release and anticipatory anxiety, particularly the rostral anterior cingulate cortex (rACC) which is connected to the descending pain modulation system and widely reported as the key region for opioid placebo effect (Bingel et al., 2006; Enck et al., 2008; Kong and Benedetti, 2014; Wager et al., 2011, 2004; Zubieta et al., 2005), would be associated with the conditioning effect and may be used to predict the strength of conditioned placebo and nocebo responses. A unique characteristic of MEG is that it allows us to explore the functional connectivity at different frequency bands. Four frequency bands (delta, theta, alpha and beta) have been studied in this study. We are particularly interested in the alpha band (8–12 Hz) brain connectivity since the alpha band oscillations are capable of mediating the processing of both conscious and nonconscious stimuli, as well as modulating sensory perception (Forschack et al., 2017; Tu et al., 2016b).
Section snippets
Subjects
Thirty-seven healthy participants without any psychiatric or neurologic disorders were enrolled in the study. Participants were dropped from the study due to (1) excessive interference noise of the MEG scanner (n = 7); (2) inability to recognize facial cues (n = 2); (3) inconsistent rating during the experiment (n = 4); (4) reported back pain (n = 1); (5) inability to finish the experiment (n = 2). The final sample consisted of 21 participants (12 females; aged 25.0 ± 3.9). All protocols were
Pain ratings during conditioning phase
The pain ratings between the low and high pain stimuli were significantly different (p < 0.001); low pain elicited an average rating of 1.6 ± 0.5 (mean ± SE) and high pain elicited an average rating of 7.4 ± 0.7, indicating the different levels of pain perception associated with high and low cues during conditioning.
Pain ratings during test phase
We first compared the subjective pain ratings to identical moderate heat pain stimuli, which were preceded by conscious and nonconscious cues, using repeated measures ANOVA with
Discussion
In this study, we applied a visual cue conditioning paradigm to investigate the neural mechanism of conditioned placebo and nocebo effects using MEG. We found that after conditioning, resting-state brain connectivity showed significant changes in multiple brain regions. The changes in alpha band brain connectivity were capable of predicting the magnitude of directly and observationally conditioned placebo and nocebo responses. In particular, alpha band connectivity between the left rACC and
Conflicts of interest
Jian Kong has a disclosure to report (holding equity in a startup company (MNT) and pending patents to develop new neuromodulation devices), all other authors declare no conflict of interest.
Acknowledgement
Jian Kong is supported by R21AT008707, R61/R33AT009310, R01AT008563 from NIH/NCCIH. Natalia Egorova is supported by DE180100893 from the Australian Research Council.
References (61)
- et al.
Mechanisms of placebo analgesia : rACC recruitment of a subcortical antinociceptive network
Pain
(2006) - et al.
Placebo analgesia: a predictive coding perspective
Neuron
(2014) - et al.
Pain measurement: an overview
Pain
(1985) - et al.
How reliable are MEG resting-state connectivity metrics?
Neuroimage
(2016) - et al.
Placebo analgesia induced by social observational learning
Pain
(2009) - et al.
Learning potentiates neurophysiological and behavioral placebo analgesic responses
Pain
(2008) - et al.
Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns
Neuroimage
(2008) - et al.
New insights into the placebo and nocebo responses
Neuron
(2008) - et al.
Distinct neural representations of placebo and nocebo effect
Neuroimage
(2015) A mechanism for cognitive dynamics: neuronal communication through neuronal coherence
Trends Cognit. Sci.
(2005)
Ratio scales of sensory and affective verbal pain descriptors
Pain
Altered development and multifaceted band-specific abnormalities of resting state networks in autism
Biol. Psychiatry
Alpha-band oscillations, attention, and controlled access to stored information
Trends Cognit. Sci.
Functional connectivity of the frontoparietal network predicts cognitive modulation of pain
Pain
An fMRI study on the interaction and dissociation between expectation of pain relief and acupuncture treatment
Neuroimage
Neuronal correlates in the modulation of placebo analgesia in experimentally-induced esophageal pain: a 3T-fMRI study
Pain
Placebo analgesia as a case of a cognitive style driven by prior expectation
Brain Res.
An analysis of factors that contribute to the magnitude of placebo analgesia in an experimental paradigm
Pain
Individual differences in reward responding explain placebo-induced expectations and effects
Neuron
The effect of the sex of a model on nocebo hyperalgesia induced by social observational learning
Pain
A novel and effective fMRI decoding approach based on sliced inverse regression and its application to pain prediction
Neurocomputing
Assessing and tuning brain decoders: cross-validation, caveats, and guidelines
Neuroimage
The contributions of suggestion, desire, and expectation to placebo effects in irritable bowel syndrome patients. An empirical investigation
Pain
Nocebo hyperalgesia induced by social observational learning
Pain
Placebo conditioning and placebo analgesia modulate a common brain network during pain anticipation and perception
Pain
Belief or Need? Accounting for individual variations in the neurochemistry of the placebo effect
Brain Behav. Immun.
Neuropharmacological dissection of placebo analgesia: expectation-activated opioid systems versus conditioning-activated specific subsystems
J. Neurosci.
Brain mediators of predictive cue effects on perceived pain
J. Neurosci.
Somatotopic activation of opioid systems by target-directed expectations of analgesia
J. Neurosci.
Prediction of individual brain maturity using fMRI
Science
Cited by (26)
How expectations of pain elicited by consciously and unconsciously perceived cues unfold over time
2021, NeuroImageCitation Excerpt :Finally, the within-subject conditioned pain-related responses could be predicted by the anticipatory MEG after 170 ms of cue onset and maintained predictability prior to the pain stimulus for the consciously perceived trials only. Using a conditioning model and cue-based manipulations of stimulus expectancy, previous studies have revealed that short-term expectations that vary as a function of cue could be conditioned by both self-experience (Atlas et al., 2010; Shih et al., 2019; Tu et al., 2020) and social observation (Colloca and Benedetti, 2009; Hunter et al., 2014; Schenk and Colloca, 2020; Tu et al., 2018). These expectations have strong effects on pain perception and pain-evoked responses.
The Context of Values in Pain Control: Understanding the Price Effect in Placebo Analgesia
2020, Journal of PainCitation Excerpt :Literature suggested that the effects of cognitive processes during painful experience were mediated by the nucleus accumbens -vmPFC, which did not respond to changes in noxious stimulus intensity.43,45 The neural findings in our study include the precuneus, PCC, and middle temporal gyrus, the regions activated in response to price and reported to predict placebo effects.39 The precuneus and PCC combine bottom-up attention with the information from perception; moreover, precuneus, PCC, parahippocampus, and vmPFC are constituent regions of the default mode network, supporting affective appraisals including valuation of rewards.2
Identifying inter-individual differences in pain threshold using brain connectome: a test-retest reproducible study
2019, NeuroImageCitation Excerpt :We assessed the predictive power of each model by correlating predicted and real pain thresholds across all subjects. This index represents how much variation of pain thresholds was explained by the predictive model (Lindquist et al., 2017; Tu et al., 2019b; Wager et al., 2013). Given that individuals’ pain measurement scores were strongly correlated within pain modalities but not significantly related between modalities (See 3.1 for details), we summarized the patterns of functional connectivity that encoded individuals’ heat pain and pressure pain thresholds separately.
Different exercise modalities relieve pain syndrome in patients with knee osteoarthritis and modulate the dorsolateral prefrontal cortex: A multiple mode MRI study
2019, Brain, Behavior, and ImmunityCitation Excerpt :We used support vector regression implemented in LIBSVM (Cortes and Vapnik, 1995) and 5-fold cross validation to avoid overfitting. To quantify prediction performance, we used prediction-outcome correlation (Tu et al., 2019; Tu et al., 2018), which is defined as the correlation between actual and predicted KOOS pain subscore differences. The performance measure was assessed using permutation testing.
Nocebo hyperalgesia induced by implicit conditioning
2019, Journal of Behavior Therapy and Experimental Psychiatry