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A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect.

Chang LJ, Gianaros PJ, Manuck SB, Krishnan A, Wager TD - PLoS Biol. (2015)

Bottom Line: The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience.Furthermore, it was not reducible to activity in traditional "emotion-related" regions (e.g., amygdala, insula) or resting-state networks (e.g., "salience," "default mode").Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology & Neuroscience, University of Colorado, Boulder, Colorado, United States of America.

ABSTRACT
Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants in cross validation (n =121) and test (n = 61) samples (high-low emotion = 93.5% accuracy). It was unresponsive to physical pain (emotion-pain = 92% discriminative accuracy), demonstrating that it is not a representation of generalized arousal or salience. The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience. Furthermore, it was not reducible to activity in traditional "emotion-related" regions (e.g., amygdala, insula) or resting-state networks (e.g., "salience," "default mode"). Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes.

No MeSH data available.


PINES.Panel A depicts the PINES pattern thresholded using a 5,000 sample bootstrap procedure at p < 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 (n = 121) and the separate holdout test data set (n = 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 (n = 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 (n = 61). Error bars reflect ±1 standard error.
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pbio.1002180.g002: PINES.Panel A depicts the PINES pattern thresholded using a 5,000 sample bootstrap procedure at p < 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 (n = 121) and the separate holdout test data set (n = 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 (n = 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 (n = 61). Error bars reflect ±1 standard error.

Mentions: The PINES accurately predicted ratings of negative emotional experience in both cross validation and hold-out test datasets (Fig 2). For individual participants in the cross validation sample, the average root mean squared error (RMSE) was 1.23 ± 0.06 (standard error; SE) rating units, and the average within-subject correlation between predicted and actual ratings was r = 0.85 ± 0.02). Accuracy was comparable in the test sample (RMSE = 0.99 ± 0.07, r = 0.92 ± 0.01). The PINES accurately classified highly aversive (rating 5) versus nonaversive (rating 1) pictures with 100% forced-choice accuracy in both cross validation and test samples (Fig 2B). Classification accuracy was also high in both the highly aversive range (rating of 5 versus 3: forced-choice = 91%; test sample) and the moderately aversive range (rating of 3 versus 1: 100%; test sample) (See S1 Table). We also assessed single-interval classification based on a single image rather than a relative comparison (Table 1), which were only slightly less accurate (Table 1). Comparisons with Support Vector Regression (SVR), another popular algorithm, indicate that these results appear to be robust to the choice of algorithm and, to a large extent, the amount of data used in the training procedure (see S1 Methods).


A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect.

Chang LJ, Gianaros PJ, Manuck SB, Krishnan A, Wager TD - PLoS Biol. (2015)

PINES.Panel A depicts the PINES pattern thresholded using a 5,000 sample bootstrap procedure at p < 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 (n = 121) and the separate holdout test data set (n = 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 (n = 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 (n = 61). Error bars reflect ±1 standard error.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4476709&req=5

pbio.1002180.g002: PINES.Panel A depicts the PINES pattern thresholded using a 5,000 sample bootstrap procedure at p < 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 (n = 121) and the separate holdout test data set (n = 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 (n = 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 (n = 61). Error bars reflect ±1 standard error.
Mentions: The PINES accurately predicted ratings of negative emotional experience in both cross validation and hold-out test datasets (Fig 2). For individual participants in the cross validation sample, the average root mean squared error (RMSE) was 1.23 ± 0.06 (standard error; SE) rating units, and the average within-subject correlation between predicted and actual ratings was r = 0.85 ± 0.02). Accuracy was comparable in the test sample (RMSE = 0.99 ± 0.07, r = 0.92 ± 0.01). The PINES accurately classified highly aversive (rating 5) versus nonaversive (rating 1) pictures with 100% forced-choice accuracy in both cross validation and test samples (Fig 2B). Classification accuracy was also high in both the highly aversive range (rating of 5 versus 3: forced-choice = 91%; test sample) and the moderately aversive range (rating of 3 versus 1: 100%; test sample) (See S1 Table). We also assessed single-interval classification based on a single image rather than a relative comparison (Table 1), which were only slightly less accurate (Table 1). Comparisons with Support Vector Regression (SVR), another popular algorithm, indicate that these results appear to be robust to the choice of algorithm and, to a large extent, the amount of data used in the training procedure (see S1 Methods).

Bottom Line: The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience.Furthermore, it was not reducible to activity in traditional "emotion-related" regions (e.g., amygdala, insula) or resting-state networks (e.g., "salience," "default mode").Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology & Neuroscience, University of Colorado, Boulder, Colorado, United States of America.

ABSTRACT
Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants in cross validation (n =121) and test (n = 61) samples (high-low emotion = 93.5% accuracy). It was unresponsive to physical pain (emotion-pain = 92% discriminative accuracy), demonstrating that it is not a representation of generalized arousal or salience. The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience. Furthermore, it was not reducible to activity in traditional "emotion-related" regions (e.g., amygdala, insula) or resting-state networks (e.g., "salience," "default mode"). Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes.

No MeSH data available.