We compared brain activations in response to acute noxious thermal stimuli in controls and chronic back pain (CBP) patients. Pain perception and related cortical activation patterns were similar in the two groups. However, nucleus accumbens (NAc) activity differentiated the groups at a very high accuracy, exhibiting phasic and tonic responses with distinct properties. Positive phasic NAc activations at stimulus onset and offset tracked stimulus salience and, in normal subjects, predicted reward (pain relief) magnitude at stimulus offset. In CBP, NAc activity correlated with different cortical circuitry from that of normals and phasic activity at stimulus offset was negative in polarity, suggesting that the acute pain relieves the ongoing back pain. The relieving effect was confirmed in a separate psychophysical study in CBP. Therefore, in contrast to somatosensory pathways, which reflect sensory properties of acute noxious stimuli, NAc activity in humans encodes its predicted value and anticipates its analgesic potential on chronic pain.

Development and maintenance of chronic pain is associated with structural and functional brain reorganization. However, few studies have explored the impact of drug treatments on such changes. The extent to which long-term analgesia is related to brain adaptations and its effects on the reversibility of brain reorganization remain unclear. In a randomized placebo-controlled clinical trial, we contrasted pain relief (3-month treatment period), and anatomical (gray matter density [GMD], assessed by voxel-based morphometry) and functional connectivity (resting state fMRI nodal degree count [DC]) adaptations, in 39 knee osteoarthritis (OA) patients (22 females), randomized to duloxetine (DLX, 60 mg once daily) or placebo. Pain relief was equivalent between treatment types. However, distinct circuitry (GMD and DC) could explain pain relief in each group: up to 85% of variance for placebo analgesia and 49% of variance for DLX analgesia. No behavioral measures (collected at entry into the study) could independently explain observed analgesia. Identified circuitry were outside of nociceptive circuitry and minimally overlapped with OA-abnormal or placebo response predictive brain regions. Mediation analysis revealed that changes in GMD and DC can influence each other across remote brain regions to explain observed analgesia. Therefore, we can conclude that distinct brain mechanisms underlie DLX and placebo analgesia in OA. The results demonstrate that even in the absence of differences in subjective pain relief, pharmacological treatments can be differentiated from placebo based on objective brain biomarkers. This is a crucial step to untangling mechanisms and advancing personalized therapy approaches for chronic pain.

Placebo response in the clinical trial setting is poorly understood and alleged to be driven by statistical confounds, and its biological underpinnings are questioned. Here we identified and validated that clinical placebo response is predictable from resting-state functional magnetic-resonance-imaging (fMRI) brain connectivity. This also led to discovering a brain region predicting active drug response and demonstrating the adverse effect of active drug interfering with placebo analgesia. Chronic knee osteoarthritis (OA) pain patients (n = 56) underwent pretreatment brain scans in two clinical trials. Study 1 (n = 17) was a 2-wk single-blinded placebo pill trial. Study 2 (n = 39) was a 3-mo double-blinded randomized trial comparing placebo pill to duloxetine. Study 3, which was conducted in additional knee OA pain patients (n = 42), was observational. fMRI-derived brain connectivity maps in study 1 were contrasted between placebo responders and nonresponders and compared to healthy controls (n = 20). Study 2 validated the primary biomarker and identified a brain region predicting drug response. In both studies, approximately half of the participants exhibited analgesia with placebo treatment. In study 1, right midfrontal gyrus connectivity best identified placebo responders. In study 2, the same measure identified placebo responders (95% correct) and predicted the magnitude of placebo's effectiveness. By subtracting away linearly modeled placebo analgesia from duloxetine response, we uncovered in 6/19 participants a tendency of duloxetine enhancing predicted placebo response, while in another 6/19, we uncovered a tendency for duloxetine to diminish it. Moreover, the approach led to discovering that right parahippocampus gyrus connectivity predicts drug analgesia after correcting for modeled placebo-related analgesia. Our evidence is consistent with clinical placebo response having biological underpinnings and shows that the method can also reveal that active treatment in some patients diminishes modeled placebo-related analgesia. Trial Registration ClinicalTrials.gov NCT02903238 ClinicalTrials.gov NCT01558700.

Use of opioid analgesics for pain management has increased dramatically over the past decade, with corresponding increases in negative sequelae including overdose and death. There is currently no well-validated objective means of accurately identifying patients likely to experience good analgesia with low side effects and abuse risk prior to initiating opioid therapy. This paper discusses the concept of data-based personalized prescribing of opioid analgesics as a means to achieve this goal. Strengths, weaknesses, and potential synergism of traditional randomized placebo-controlled trial (RCT) and practice-based evidence (PBE) methodologies as means to acquire the clinical data necessary to develop validated personalized analgesic-prescribing algorithms are overviewed. Several predictive factors that might be incorporated into such algorithms are briefly discussed, including genetic factors, differences in brain structure and function, differences in neurotransmitter pathways, and patient phenotypic variables such as negative affect, sex, and pain sensitivity. Currently available research is insufficient to inform development of quantitative analgesic-prescribing algorithms. However, responder subtype analyses made practical by the large numbers of chronic pain patients in proposed collaborative PBE pain registries, in conjunction with follow-up validation RCTs, may eventually permit development of clinically useful analgesic-prescribing algorithms.