PINES.

<p>Panel A depicts the PINES pattern thresholded using a 5,000 sample bootstrap procedure at <i>p</i> < 0.001 uncorrected. Blowout sections show the spatial topography of the pattern in the left amygdala, right insula, and posterior cingulate cortex. Panel B shows the predicted affective rating compared to the actual ratings for the cross validated participants (<i>n</i> = 121) and the separate holdout test data set (<i>n</i> = 61). Accuracies reflect forced-choice comparisons between high and low and high, medium, and low ratings. Panel C depicts an average peristimulus plot of the PINES response to the holdout test dataset (<i>n</i> = 61). This reflects the average PINES response at every repetition time (TR) in the timeseries separated by the rating. Panel D illustrates an item analysis which shows the average PINES response to each photo by the average ratings to the photos in the separate test dataset (<i>n</i> = 61). Error bars reflect ±1 standard error.</p

Factor Structure Underlying Components of Allostatic Load

<div><p>Allostatic load is a commonly used metric of health risk based on the hypothesis that recurrent exposure to environmental demands (e.g., stress) engenders a progressive dysregulation of multiple physiological systems. Prominent indicators of response to environmental challenges, such as stress-related hormones, sympatho-vagal balance, or inflammatory cytokines, comprise primary allostatic mediators. Secondary mediators reflect ensuing biological alterations that accumulate over time and confer risk for clinical disease but overlap substantially with a second metric of health risk, the metabolic syndrome. Whether allostatic load mediators covary and thus warrant treatment as a unitary construct remains to be established and, in particular, the relation of allostatic load parameters to the metabolic syndrome requires elucidation. Here, we employ confirmatory factor analysis to test: 1) whether a single common factor underlies variation in physiological systems associated with allostatic load; and 2) whether allostatic load parameters continue to load on a single common factor if a second factor representing the metabolic syndrome is also modeled. Participants were 645 adults from Allegheny County, PA (30–54 years old, 82% non-Hispanic white, 52% female) who were free of confounding medications. Model fitting supported a single, second-order factor underlying variance in the allostatic load components available in this study (metabolic, inflammatory and vagal measures). Further, this common factor reflecting covariation among allostatic load components persisted when a latent factor representing metabolic syndrome facets was conjointly modeled. Overall, this study provides novel evidence that the modeled allostatic load components do share common variance as hypothesized. Moreover, the common variance suggests the existence of statistical coherence above and beyond that attributable to the metabolic syndrome.</p> </div

Region of interest analysis.

<p>Panel A illustrates the spatial distribution of the three anatomical ROIs used in all analyses (amygdala = yellow, insula = red, ACC = cyan). Panel B depicts the average activation within each ROI across participants for each level of emotion and pain in the emotion hold out (<i>n</i> = 61) and pain test datasets (<i>n</i> = 28). Error bars reflect ±1 standard error. Panel C illustrates the spatial topography of the PINES and NPS patterns within each of these anatomical ROIs. While these plots show one region, correlations reported in the text reflect bilateral patterns.</p

Descriptive statistics of participants’ demographic and biomedical characteristics.

<p>Descriptive statistics of participants’ demographic and biomedical characteristics.</p

PINES clustering based on shared patterns of connectivity.

<p>This figure depicts the results of the hierarchical clustering analysis of the functional connectivity of the largest regions from the <i>p</i> < 0.001 thresholded PINES pattern. Clusters were defined by performing hierarchical agglomerative clustering with ward linkage on the trial-by-trial local pattern responses for each region using Euclidean distance. Data were ranked and normalized within each participant and then aggregated by concatenating all 61 subjects’ trial x region data matrices. Panel A depicts the dendrogram separated by each functional network. Panel B depicts the spatial distribution of the networks. Colors correspond to the dendrogram labels.</p

Model 3: Two second-order factor model: allostatic load and metabolic syndrome factors.

<p>Age, sex and race covaried; relevant medications excluded. MS resid.: Second-order metabolic syndrome factor with allostatic load parameters simulataneously modeled; AL resid.: Second-order allostatic load factor with metabolic syndrome pathways simultaneously modeled; IR – insulin resistance factor; boxes represent indicator variables and circles reflect latent factors. χ² = 125.00, df = 38, p<0.001, N = 645; CFI = .97, average absolute standardized residuals = .02, RMSEA = .06. Δχ² (4) = 20.05, p<0.01.</p

Model 1: Factor structure of the metabolic syndrome.

<p>Age, sex and race covaried; relevant medications excluded. MS – Second-order metabolic syndrome factor; IR – insulin resistance factor; boxes represent indicator variables and circles reflect latent factors. χ² = 57.51, df = 12, p<0.001, N = 645; CFI = .98, average absolute standardized residuals = .02, RMSEA = .08.</p

Single-cluster and “virtual lesion” analysis.

<p>All balanced accuracies reported in this table result from single interval classification on the test sample (<i>n</i> = 47; see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002180#pbio.1002180.s013" target="_blank">S2 Table</a> for forced-choice test). Analyses involving Level 5 and/or Level 1 comparisons exclude participants that did not rate any stimuli with that label. Accuracy values reflect the ability to discriminate the conditions compared, but are signed so that values >50% indicate the proportion of participants for which high intensity was classified as greater than low intensity for high vs. low analyses, or emotion was greater than pain for Emotion vs. Pain analyses. Values < 50% indicate the proportion of participants for which low intensity was classified as greater than high intensity or pain was classified as greater than emotion. For example, the 10.7% emotion classification of the NPS in the Emotion vs. Pain analysis should be interpreted as a 89.3% hit rate in discriminating pain from emotion. Correlations reflect Pearson correlations between participant’s pattern responses to levels of affective intensity and self-reported ratings averaged across participants.</p><p>⁺Indicates that accuracy is significantly different from chance (50%) using a two-tailed binomial test.</p><p>*Indicates accuracy is significantly different from PINES performance using a two-sample, two-tailed z-test for proportions (only tested on Emotion 5 versus 1 and Emotion versus Pain columns).</p><p>Single-cluster and “virtual lesion” analysis.</p

Affective and pain responses to PINES and NPS.

<p>This figure illustrates differences in the spatial topography in the thresholded PINES and NPS patterns and their predictions in independent emotion (<i>n</i> = 61) and pain (<i>n</i> = 28) test data. Panel A depicts the PINES thresholded at <i>p</i> < 0.001 uncorrected (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002180#pbio.1002180.g002" target="_blank">Fig 2</a>). Panel B depicts the average standardized PINES and NPS pattern responses at each level of emotion calculated using a spatial correlation. Error bars reflect ±1 standard error. Panel C depicts the NPS thresholded at false discovery rate (FDR) q < 0.05 whole-brain corrected. Panel D depicts the average standardized PINES and NPS pattern responses at each pain level calculated using a spatial correlation. Error bars reflect ±1 standard error.</p

Within participant emotion prediction.

<p>This figure depicts results from our within-participant analysis, in which the PINES was retrained separately for each participant to predict ratings to individual photos. Panel A shows the voxels in the weight map that are consistently different from zero across participants using a one sample <i>t</i> test thresholded at <i>p</i> < 0.001 uncorrected. Panel B shows a histogram of standardized emotion predictions (correlation) for each participant. The dotted red line reflects the average cross validated PINES correlation for predicting each photo’s rating. Panel C depicts how well each participant’s ratings were predicted by the PINES (y-axis) versus an idiographically trained, cross-validated map using their individual brain data (x-axis). Each point on the graph reflects one participant. The dotted red line reflects the identity line. Any data point above the identity line indicates that the participant was better fit by the PINES than their own weight map.</p