Meta-analysis of neural systems underlying placebo analgesia from individual participant fMRI data

The brain systems underlying placebo analgesia are insufficiently understood. Here we performed a systematic, participant-level meta-analysis of experimental functional neuroimaging studies of evoked pain under stimulus-intensity-matched placebo and control conditions, encompassing 603 healthy participants from 20 (out of 28 eligible) studies. We find that placebo vs. control treatments induce small, widespread reductions in pain-related activity, particularly in regions belonging to ventral attention (including mid-insula) and somatomotor networks (including posterior insula). Behavioral placebo analgesia correlates with reduced pain-related activity in these networks and the thalamus, habenula, mid-cingulate, and supplementary motor area. Placebo-associated activity increases occur mainly in frontoparietal regions, with high between-study heterogeneity. We conclude that placebo treatments affect pain-related activity in multiple brain areas, which may reflect changes in nociception and/or other affective and decision-making processes surrounding pain. Between-study heterogeneity suggests that placebo analgesia is a multi-faceted phenomenon involving multiple cerebral mechanisms that differ across studies

Probabilistic TFCE: A generalized combination of cluster size and voxel intensity to increase statistical power

The threshold-free cluster enhancement (TFCE) approach integrates cluster information into voxel-wise statistical inference to enhance detectability of neuroimaging signal. Despite the significantly increased sensitivity, the application of TFCE is limited by several factors: (i) generalisation to data structures, like brain network connectivity data is not trivial, (ii) TFCE values are in an arbitrary unit, therefore, P-values can only be obtained by a computationally demanding permutation-test. Here, we introduce a probabilistic approach for TFCE (pTFCE), that gives a simple general framework for topology-based belief boosting. The core of pTFCE is a conditional probability, calculated based on Bayes' rule, from the probability of voxel intensity and the threshold-wise likelihood function of the measured cluster size. In this paper, we provide an estimation of these distributions based on Gaussian Random Field theory. The conditional probabilities are then aggregated across cluster-forming thresholds by a novel incremental aggregation method. pTFCE is validated on simulated and real fMRI data. The results suggest that pTFCE is more robust to various ground truth shapes and provides a stricter control over cluster "leaking" than TFCE and, in many realistic cases, further improves its sensitivity. Correction for multiple comparisons can be trivially performed on the enhanced P-values, without the need for permutation testing, thus pTFCE is well-suitable for the improvement of statistical inference in any neuroimaging workflow

Evaluating the Effects of Acupuncture Using a Dental Pain Model in Healthy Subjects – A Randomized, Cross-Over Trial

Acupuncture is a complementary and nonpharmacological intervention that can be effective for the management of chronic pain in addition to or instead of medication. Various animal models for neuropathic pain, inflammatory pain, cancer-related pain, and visceral pain already exist in acupuncture research. We used a newly validated human pain model and examined whether acupuncture can influence experimentally induced dental pain. For this study, we compared the impact of manual acupuncture (real acupuncture), manual stimulation of a needle inserted at nonacupuncture points (sham acupuncture) and no acupuncture on experimentally induced dental pain in 35 healthy men who were randomized to different sequences of all 3 interventions in a within-subject design. BORG CR10 pain ratings and autonomic responses (electrodermal activity and heart rate variability) were investigated. An initial mixed model with repeated measures included preintervention pain ratings and the trial sequence as covariates. The results showed that acupuncture was effective in reducing pain intensity when compared to no acupuncture (β = -.708, P = .002), corresponding to a medium Cohen's d effect size of .56. The comparison to the sham acupuncture revealed no statistically significant difference. No differences in autonomic responses between real and sham acupuncture were found during the intervention procedures. PERSPECTIVE: This study established a dental pain model for acupuncture research and provided evidence that experimentally induced dental pain can be influenced by either real acupuncture or manual stimulation of needles at nonacupuncture points. The data do not support that acupoint specificity is a significant factor in reducing experimental pain