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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. The neural mechanisms of placebo analgesia are not fully understood. Here the authors conducted a large scale meta-analysis of individual data from fMRI studies of pain and placebo conditions. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to reveal potential sources of systematic motion artifacts in stroke functional magnetic resonance imaging (fMRI) focusing on those causing stimulus-correlated motion on the individual-level and separate the motion effect on the fMRI signal changing from the activation-induced alteration at population level.Methods: Eleven ischemic stroke patients were examined by fMRI. The fMRI paradigm was based on passive ankle movement on both the healthy and the paretic leg's side. Three individual-level motion correction strategies were compared and we introduced five measures to characterize each subjects' in-scanner relative head movement. After analyzing the correlation of motion parameters and the subjects' physiological scale scores, we selected a parameter to model the motion-related artifacts in the second-level analysis.Results: At first (individual) level analysis, the noise-component correction-based CompCor method provided the highest -log10(p) value of cluster-level occurrence probability at 12.4/13.6 for healthy and paretic side stimulus, respectively, with a maximal z-value of 15/16.3. Including the motion parameter at second (group) level resulted in lower cluster occurrence values at 10.9/5.55 while retaining the maximal z-value.Conclusions: We proposed a postprocessing pipeline for ischemic stroke fMRI data that combine the CompCor correction at first level with the modeling of motion effect at second-level analysis by a parameter obtained from fMRI data. Our solution is applicable for any fMRI-based stroke rehabilitation study since it does not require any MRI-compatible motion capture system and is based on commonly used methods. [ABSTRACT FROM AUTHOR]