Brain Connectivity Predicts Placebo Response across Chronic Pain Clinical Trials

<div><p>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 (<i>n</i> = 56) underwent pretreatment brain scans in two clinical trials. Study 1 (<i>n</i> = 17) was a 2-wk single-blinded placebo pill trial. Study 2 (<i>n</i> = 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 (<i>n</i> = 42), was observational. fMRI-derived brain connectivity maps in study 1 were contrasted between placebo responders and nonresponders and compared to healthy controls (<i>n</i> = 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.</p><p><b>Trial Registration</b> ClinicalTrials.gov <a href="https://clinicaltrials.gov/show/NCT02903238" target="_blank">NCT02903238</a></p><p>ClinicalTrials.gov <a href="https://clinicaltrials.gov/show/NCT01558700" target="_blank">NCT01558700</a></p></div

Predicting placebo and duloxetine treatment outcomes from r-MFG degree counts in study 2.

<p>Prediction of future outcomes (A, B for placebo treatment; C, D for duloxetine treatment) was assessed for r-MFG degree counts (based on brain coordinates derived from study 1). (A) r-MFG degree counts were significantly higher in placebo responders (post hoc honestly significant difference [HSD] test, <i>p</i> = 0.001), and the receiver operating characteristic (ROC) curve identified grouping at 95% accuracy. (B) Empirical analgesia was correlated to analgesia predicted from the best-fit line calculated in study 1, using r-MFG degree counts from study 2, for VAS (<i>p</i> = 0.004) and more weakly for WOMAC (<i>p</i> = 0.12) outcomes. (C) In contrast, in patients randomized to duloxetine, the r-MFG degree count did not differentiate between responders and nonresponders (t-score₁₇ = 1.5, <i>p</i> = 0.17; ROC area under the curve [AUC] = 0.67) and (D) did not predict empirical analgesia for VAS and WOMAC outcomes. Error bars are 95% CIs. Symbol colors represent the same groups as in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002570#pbio.1002570.g003" target="_blank">Fig 3</a>.</p

Pain relief in the double-blind placebo-controlled 3-mo duloxetine treatment, study 2.

<p>(A) Of all the knee OA pain patients who participated in study 2, 20 were randomized to placebo (P, grey) and 19 to duloxetine (DLX, red) treatment. Both groups started at the same level of knee pain (VAS) and exhibited significant and similar magnitudes of pain relief with a 3-mo treatment (only time-effect repeated-measures ANOVA, F_1,37 = 14.8, <i>p</i> < 0.0001). (B) Participants in both arms were classified as responders (P +, white; DLX +, pink) or nonresponders (P −, black; DLX −, red) (≥20% analgesia over the 3-mo placebo treatment). Both treatments resulted in similar numbers of improvers and similar magnitudes of pain relief, observed, by design, only in the treatment responders (white and pink). (C) Twenty knee OA patients (study 3), matched for age, gender, and knee VAS pain at baseline, followed over 3 mo with no treatment (green). There was no within-group change in knee pain over 3 mo of no treatment. (D, E) We observe the same pattern of symptom relief, as observed for VAS, when the WOMAC scale is used as an outcome measure (only time-effect repeated-measures ANOVA, F_1,37 = 13.3, <i>p</i> = 0.001). Error bars are 95% CIs. The illustrated <i>p</i>-values are post hoc comparisons that were statistically significant.</p

Flow diagram summarizes overall experimental design, OA patients entering and completing each of the three studies, and participant subgroupings based on treatment and treatment effects.

<p>Study 1 was analyzed to discover brain connectivity predicting placebo response. All patients received only placebo pills. Study 2 was used to validate the results from Study 1 and also to examine how the active treatment was related to placebo response. Study 2 was a double-blind randomized clinical trial. Study 3 was an observation-only trial in which no treatment was provided. Groupings and dropout causes are indicated. fMRI, functional magnetic resonance imaging.</p

Right parahippocampal gyrus (r-PHG) degree counts predict future duloxetine response, based on modeling the placebo response in duloxetine-treated patients in study 2.

<p>(A) The empirical analgesia of individual duloxetine-treated patients (red) and the predicted placebo response (grey) are illustrated. The predicted placebo response was derived from the best-fit equation from study 1, which was applied to r-MFG degree count in duloxetine-treated patients. Patients with minimal predicted placebo and ≥20% empirical analgesia were considered mostly duloxetine responders (subjects 4 and 6; arrows). (B) Contrasting the whole-brain degree counts of these two subjects with the six other duloxetine responders (subjects 1, 2, 3, 5, 7, and 8) revealed a right parahippocampal gyrus region (r-PHG) in which degree counts were higher in subjects 4 and 6 (scatter of individual values and median and quartiles are shown; Mann-Whitney U-test, <i>p</i> = 0.071). (C) r-PHG degree count correlated with the difference between empirical analgesia and predicted placebo response for VAS (<i>p</i> = 0.048) and WOMAC (<i>p</i> = 0.033) outcomes, suggesting that the regional functional connections also reflect future placebo-corrected drug response for all 20 duloxetine-treated patients.</p

Personalizing rehabilitation for individuals with musculoskeletal impairments: Feasibility of implementation of the Measures Associated to Prognostic (MAPS) tool

The Measures Associated to PrognoStic (MAPS) tool is a standardized questionnaire that integrates validated prognostic tools to detect the presence of biopsychosocial prognostic factors in patients consulting for musculoskeletal disorders. The objectives were to assess the: 1) feasibility of implementation of the MAPS tool, 2) clinicians’ acceptability of the dashboard, and 3) patients’ acceptability of the MAPS tool. Twenty physiotherapists and two occupational therapists from seven outpatient musculoskeletal clinics were recruited to implement the MAPS tool during a 3-month timeframe, where new patients completed the questionnaire upon initial assessment. The results were presented to the clinicians via a dashboard. Surveys and semi-structured interviews were conducted to measure feasibility and acceptability. Six out of 11 feasibility criteria (55%) and 21 out of 24 acceptability criteria (88%) reached the a priori threshold for success. The interviews allowed us to identify three main themes to facilitate implementation: 1) limiting the burden, 2) ensuring patients’ understanding of the tool’s purpose, and 3) integrating the dashboard as a clinical information tool. Our quantitative and qualitative results support the feasibility of implementation and acceptability of the MAPS tool pending minor adjustments. Depicting the patients’ prognostic profile has the potential to help clinicians optimize their interventions for patients presenting with musculoskeletal disorders.</p