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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.


Region of interest analysis.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 (n = 61) and pain test datasets (n = 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.
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pbio.1002180.g005: Region of interest analysis.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 (n = 61) and pain test datasets (n = 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.

Mentions: We employed two types of analyses to examine the PINES specificity. First, we compared the spatial topography of the PINES to another pattern map, the Neurologic Pain Signature (NPS), which shows high sensitivity and specificity to somatic pain across multiple studies [35]. The PINES and NPS maps were almost completely uncorrelated (robust ranked spatial correlation, = −0.01; Fig 4). Several regions showed positive weights in both maps, including the anterior cingulate (ACC), insula, and amygdala. As shown in Fig 5C, however, the weight patterns within these regions were also uncorrelated (bilateral ACC, = 0.04, insula, = −0.05), though weights in the amygdala were modestly correlated ( = 0.21).


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

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

Region of interest analysis.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 (n = 61) and pain test datasets (n = 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.
© Copyright Policy
Related In: Results  -  Collection

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

pbio.1002180.g005: Region of interest analysis.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 (n = 61) and pain test datasets (n = 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.
Mentions: We employed two types of analyses to examine the PINES specificity. First, we compared the spatial topography of the PINES to another pattern map, the Neurologic Pain Signature (NPS), which shows high sensitivity and specificity to somatic pain across multiple studies [35]. The PINES and NPS maps were almost completely uncorrelated (robust ranked spatial correlation, = −0.01; Fig 4). Several regions showed positive weights in both maps, including the anterior cingulate (ACC), insula, and amygdala. As shown in Fig 5C, however, the weight patterns within these regions were also uncorrelated (bilateral ACC, = 0.04, insula, = −0.05), though weights in the amygdala were modestly correlated ( = 0.21).

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.