Effective connectivity predicts future placebo analgesic response: A dynamic causal modeling study of pain processing in healthy controls

Abstract

A better understanding of the neural mechanisms underlying pain processing and analgesia may aid in the development and personalization of effective treatments for chronic pain. Clarification of the neural predictors of individual variability in placebo analgesia (PA) could aid in this process. The present study examined whether the strength of effective connectivity (EC) among pain-related brain regions could predict future placebo analgesic response in healthy individuals. In Visit 1, fMRI data were collected from 24 healthy subjects (13 females, mean age=22.56, SD=2.94) while experiencing painful thermal stimuli. During Visit 2, subjects were conditioned to expect less pain via a surreptitiously lowered temperature applied at two of the four sites on their feet. They were subsequently scanned again using the Visit 1 (painful) temperature. Subjects used an electronic VAS to rate their pain following each stimulus. Differences in ratings at conditioned and unconditioned sites were used to measure placebo response (PA scores). Dynamic causal modeling was used to estimate the EC among a set of brain regions related to pain processing at Visit 1 (periaqueductal gray, thalamus, rostral anterior cingulate cortex, dorsolateral prefrontal cortex). Individual PA scores from Visit 2 were regressed on salient EC parameter estimates from Visit 1. Results indicate that both greater left hemisphere modulatory DLPFC➔PAG connectivity and right hemisphere, endogenous thalamus➔DLPFC connectivity were significantly predictive of future placebo response (R(2)=0.82). To our knowledge, this is the first study to identify the value of EC in understanding individual differences in PA, and may suggest the potential modifiability of endogenous pain modulation.

Keywords: Dynamic causal modeling; Effective connectivity; Pain; Placebo analgesia; fMRI.

Copyright © 2015 Elsevier Inc. All rights reserved.

Figures

Figure 1

Experimental procedures and corresponding analyses are shown in the schematic above. Abbreviations: DLPFC, dorsolateral prefrontal cortex; rACC, rostral anterior cingulate cortex; PAG, periaqueductal gray.

Figure 2

The paradigm used to induce experimental pain included 4-second blocks of thermal stimuli followed by 12 seconds of rest. Participants rated pain intensity after each stimulus was delivered.

Figure 3

Shown above are the models compared in BMS analyses. All models contained the same underlying structure of endogenous parameters and differ in estimated modulatory parameters (shown glowing). In Model A, only endogenous parameters were estimated; in Model B, modulatory parameters were estimated only on ascending connections which are primarily involved in pain processing; in Model C, modulatory parameters were estimated only on descending connections which are primarily involved in pain modulation; in Model D, modulatory parameters were estimated on both ascending and descending connections. Brain regions are shown as circles while experimental inputs (thermal stimuli) are shown as rectangles. Abbreviations: DLPFC, dorsolateral prefrontal cortex; PAG, periaqueductal gray; TS, thermal stimuli, rACC, rostral anterior cingulate cortex; THAL, thalamus.

Figure 4

Results of the random effects general linear model for the contrast “pain” > “rest.” Clusters are displayed at pFWE < 0.05. Abbreviations: ACC, anterior cingulate cortex; DLPFC, dorsolateral prefrontal cortex, LN, lentiform nucleus; SI, primary somatosensory cortex; SI, secondary somatosensory cortex; SMA, supplementary motor area; STG, superior temporal gyrus; THAL, thalamus.

Figure 5

Results of random effects Bayesian model averaging are displayed for each hemisphere. Line and glow widths are weighted to represent parameter strengths: green lines indicate positive endogenous couplings and experimental inputs, red lines indicate negative endogenous couplings, yellow glow indicates positive modulatory effects, and red glow indicates negative modulatory effects. Abbreviations: DLPFC, dorsolateral prefrontal cortex; PAG, periaqueductal gray; rACC, rostral anterior cingulate cortex; THAL, thalamus; TS, thermal stimuli.