Brain networks predicting placebo analgesia in a clinical trial for chronic back pain

Abstract

A fundamental question for placebo research is whether such responses are a predisposition, quantifiable by brain characteristics. We examine this issue in chronic back pain (CBP) patients who participated in a double-blind brain imaging (functional magnetic resonance imaging) clinical trial. We recently reported that when the 30 CBP participants were treated, for 2 weeks, with topical analgesic or no drug patches, pain and brain activity decreased independently of treatment type and thus were attributed to placebo responses. Here we examine in the same group brain markers for predicting placebo responses--that is, for differentiating between posttreatment persistent CBP (CBPp) and decreasing CBP (CBPd) groups. At baseline, pain and brain activity for rating spontaneous fluctuations of back pain were not different between the 2 groups. However, on the basis of brain activity differences after treatment, we identified that at baseline the extent of information shared (functional connectivity) between left medial prefrontal cortex and bilateral insula accurately (0.8) predicted posttreatment groups. This was validated in an independent cohort. Additionally, by means of frequency domain contrasts, we observe that at baseline, left dorsolateral prefrontal cortex high-frequency oscillations also predicted treatment outcomes and identified an additional set of functional connections distinguishing treatment outcomes. Combining medial and lateral prefrontal functional connections, we observe a statistically higher accuracy (0.9) for predicting posttreatment groups. These findings indicate that placebo response can be identified a priori at least in CBP, and that neuronal population interactions between prefrontal cognitive and pain processing regions predetermine the probability of placebo response in the clinical setting.

Conflict of interest statement

The authors have no conflict of interest to declare.

Copyright © 2012 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

Figures

Fig.1. Brain activity for spontaneous fluctuations of back pain, in persisting and decreasing CBP groups

A, B. Group-averaged brain activity for rating back pain at baseline (prior to start of treatment), in CBPd (A) and CBPp (B) (n=15 subjects per group). In both groups brain activity was limited to the medial prefrontal cortex (BA 9) and the genual anterior cingulate cortex (BA 32), and the contrast between the groups was null. C. Two weeks after treatment brain activity contrast between the two groups (CBPp > CBPd) shows greater activation in CBPp in bilateral anterior insula (BA 13; horizontal slice at z=0), bilateral dorsal cingulate (BA 24 and 32; sagittal slice at x=2), right dorsal medial prefrontal cortex (RdmPFC, BA 8; coronal slice at y = 46), and lateral frontal pole (BA 10). Activity and contrast maps were generated using random-effects statistics with z score > 2.3 and cluster threshold p < 0.05, corrected for multiple comparisons.

Fig.2. Brain functional connectivity strengths at baseline predict patients who will report persisting or decreasing back pain after a 2-week placebo treatment

A. Adjacency matrices showing strengths of baseline functional connectivity between 6 regions of interest, in CBPd (left) and CBPp (middle). The contrast between the two groups (CBPp > CBPd) shows regions with significantly stronger connectivity in CBPp (right). B. Three dimensional schematic of the functional network examined at baseline in standard space. Connections in red are significantly stronger in CBPp. C. D. Functional connectivity strengths are distinct between CBPp and CBPd (individual values overlaid upon box plots) for between RdmPFC and LaINS (C), and RdmPFC and RaINS (D). E. Receiver operator curve (ROC) characteristics for discriminating between CBPp and CBPd at 2 weeks after treatment based on functional connectivities calculated at baseline between RdmPFC and LaINS (filled circles) and between RdmPFC and RaINS (open circles). Both functional connectivities significantly predict future outcomes at an accuracy of about 0.8. F. In a separate group of 12 chronic pain subjects ROC characteristics were measured to test validity of discriminating between persisting versus decreasing chronic pain, after 2 weeks of assumed inert treatment. Functional connectivities at baseline between RdmPFC and LaINS (filled circles) and between RdmPFC and RaINS (open circles, symbols are not seen as outcomes are identical) again strongly predict future outcomes (accuracy > 0.9).

Fig.3. Difference in power spectral density between CBPp and CBPd, during back pain rating task, measured at baseline predicts placebo reponse

A. Whole brain voxel-wise differences in power for the high frequency band of BOLD oscillations, contrasted between CBPp and CBPd groups at baseline. Regions shown in red-yellow depict significantly greater power in in CBPd (un-paired t-test, random-effects model, z-core >2.3, cluster p<0.01, corrected for multiple comparisons), localized mainly to the left dorsal lateral prefrontal cortex (LdlPFC). B. Bar graphs depicting the mean ± SEM spectral power for LdlPFC, for three frequency bands (low, mid, high), in CBPp and CBPd groups (* p< 0.05). C. High frequency power in LdlPFC BOLD is related to extent of pain relief with placebo (delta pain = pain between baseline and 2 weeks post treatment). D. Receiver operating curve (ROC, filled circles) characteristics for discriminating between CBPp and CBPd at 2 weeks after treatment based on LdlPFC high frequency power in BOLD response calculated at baseline. Prediction was at an accuracy of 0.78 (p = 0.01). ROC for a separate group of 12 chronic pain subjects (grey circles) where only high frequency power in BOLD at LdlPFC was calculated, to test differences between CBPp and CBPd, after 2 weeks of placebo treatment. Prediction accuracy was 0.8 (p=0.078).

Fig.4. Differences in LdlPFC whole brain connectivity between CBPp and CBPd groups at baseline

A–B. Show mean connectivity for CBPp (blue) and CBPd (red) groups with LdlPFC. Purple represents regions with connectivity overlap of CBPp and CBPd groups. C–G. Difference between CBPp and CBPd in whole brain LdlPFC connectivity (contrast for CBPp > CBPd is blue, and for CBPd> CBPp is red; un-paired t-test, random-effects model, z-core >2.3, cluster p<0.05, corrected for multiple comparisons).

Fig.5. Distinct BOLD frequencies are coupled with distinct LdlPFC brain connectivity patterns and behavioural measures

A. Loading plot for principal components analysis. Only two components were extracted. B. Factor loadings of each variable on principal component 1 and 2. HF= high frequency, MF=mid frequency, LdlPFC=left dorsal lateral prefrontal cortex; RdmPFC= right dorsal medial prefrontal cortex, LaPPC = left angular posterior partietal cortex, RlPFC= Right lateral prefrontal cortex, RM1=left primary motor area 1, RS1= right primary somatosensory area 1, LmCC = left mid cingulate cortex, LaINS= left anterior insula.