2011
DOI: 10.1523/jneurosci.3420-10.2011
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Predicting Individual Differences in Placebo Analgesia: Contributions of Brain Activity during Anticipation and Pain Experience

Abstract: Recent studies have identified brain correlates of placebo analgesia, but none have assessed how accurately patterns of brain activity can predict individual differences in placebo responses. We reanalyzed data from two fMRI studies of placebo analgesia (N = 47), using patterns of fMRI activity during the anticipation and experience of pain to predict new subjects’ scores on placebo analgesia and placebo-induced changes in pain processing. We used a cross-validated regression procedure, LASSO-PCR, which provid… Show more

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Cited by 276 publications
(272 citation statements)
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“…This multivariate pattern, henceforth referred to as a brain phenotype for ∆SBP, was derived from the largest neuroimaging study of individual differences in cardiovascular stress reactivity to date, which integrated the use of a standardized stressor battery, cross‐validation testing, and machine learning methods 35, 36, 43. The brain phenotype for ∆SBP accounted for around 9% of the variance in individual differences in stressor‐evoked SBP reactivity (our primary dependent variable), with overall accuracies in predicting normative SBP changes ranging from 0.6 to 0.8 (Figure 3C).…”
Section: Discussionmentioning
confidence: 99%
“…This multivariate pattern, henceforth referred to as a brain phenotype for ∆SBP, was derived from the largest neuroimaging study of individual differences in cardiovascular stress reactivity to date, which integrated the use of a standardized stressor battery, cross‐validation testing, and machine learning methods 35, 36, 43. The brain phenotype for ∆SBP accounted for around 9% of the variance in individual differences in stressor‐evoked SBP reactivity (our primary dependent variable), with overall accuracies in predicting normative SBP changes ranging from 0.6 to 0.8 (Figure 3C).…”
Section: Discussionmentioning
confidence: 99%
“…As contrasted with a more cognitivelymediated PA in adults, one might speculate that in children, reward-related motivational, emotional appraisal (e.g. safety) and associated changes in intra-subcortical limbic-related circuitries (61), as well as attachment are critical for the mediation of PA.…”
Section: Placebo Analgesia Responsesmentioning
confidence: 98%
“…For each participant, we conducted a general linear model (GLM) in SPM8 including regressors for conditions of interest (ie, the 'buy' decision epoch-the period in which decisions to purchase or decline cannabis were made-for purchased and declined offers convolved with the canonical hemodynamic response function) and conditions of no interest (motion regressions), with a high-pass filter of 128 s. This allowed us to summarize brain activation in each voxel for purchased vs declined offers. A machine learning regression approach, Least Absolute Shrinkage and Selection Operator-Regularized Principal Components Regression (LASSO-PCR; Wager et al, 2011), was applied in MATLAB (MathWorks, Natick, MA) to generate the whole-brain pattern of regression weights (the neural signature) that best differentiated between purchased and declined fMRI activation maps. We employed a leave-one-out cross validation approach, with data from 16 participants used as training data to generate the neural signature that was then applied prospectively to differentiate brain activity associated with purchased from declined cannabis offers in the left-out participant; data from each participant were sequentially excluded from the derivation of the signature to serve as the predicted data set.…”
Section: Experimental Protocolmentioning
confidence: 99%